Shalini Sehgal

Barinderjit Singh
Vasudha Sharma Editors

Smart and
Sustainable Food
Technologies
Smart and Sustainable Food Technologies
Shalini Sehgal • Barinderjit Singh •
Vasudha Sharma
Editors

Smart and Sustainable Food

Technologies
Editors
Shalini Sehgal Barinderjit Singh
Department of Food Technology Department of Food Science and Technology
Bhaskaracharya College of Applied I. K. Gujral Punjab Technical University
Sciences, University of Delhi Kapurthala, Punjab, India
New Delhi, India

Vasudha Sharma
Department of Food Technology
Jamia Hamdard
New Delhi, India

ISBN 978-981-19-1745-5 ISBN 978-981-19-1746-2 (eBook)

https://doi.org/10.1007/978-981-19-1746-2

© The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Singapore
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Foreword

Approaches in food processing are evolving with time to complement sustainable

food security and much required food safety. In this changing scenario, the role of
digitization is rapidly expanding and provoking essential changes to advance food
processing practices in a smart and sustainable way. Various digital technologies are
revolutionizing and breaking barriers in the food chain, from production to con-
sumption, especially in minimizing food loss and wastage. In future scenarios, novel
technologies like 3D printing of food, image processing, integration of artificial
intelligence, machine learning, blockchain and smart packaging are going to be the
key elements in ensuring a safe and sustainable food system. Apart from this, climate
change is another grave matter of concern in the global scenario. Therefore, robust
and resilient adaptation and employing smart agricultural interventions are the need
of the hour. This not only needs the right technology, rather due capacity building,
regulations and policies are to be brought in place to promote efficient transforma-
tion in agricultural practices.
This comprehensive book contains four sections: the first section covers the
recent smart food production innovations such as precision agriculture, indoor
vertical farming, automation, robotics, livestock technology, modern greenhouse
practices, artificial intelligence and blockchain. The second section provides the
current knowledge and developments related to the recent smart technologies in
manufacturing pertaining to various food sectors, non-thermal food preservation
technologies, food packaging and 3D printing. The third section covers smart
technologies such as biosensors to ensure food safety in the supply chain. Finally,
the fourth section covers topics relevant to the minimization of waste and maximi-
zation of co-product recovery in food processing; upcycling technologies in food
and sustainable value stream mapping in the food industry.
I am extremely happy and congratulate the editors Dr. Shalini Sehgal,
Dr. Barinderjit Singh and Dr. Vasudha Sharma for seeing the need for such a book
and producing this book “Smart and Sustainable Food Technologies”. This is a well-
edited book, and its material will be a great resource for graduate students, scientists
and technocrats. I would say, the book is a genuine preview of the next 15 years
timeline as far as advancements in food processing technologies are concerned.
v
vi Foreword

Moreover, this book is an ideal resource for policymakers to draft policies antici-
pating the future advancement in food processing.
Overall, the content provided in this book is highly scientific with most updated
information and advancements made in the field of food technology and food supply
chain. Without any reservation, I strongly recommend this book to food technolo-
gists, food engineers, students and faculty in food processing technologies and
policymakers. I wish all the success to the book and all editors and authors.

Department of Agricultural Kasiviswanathan Muthukumarappan

and Biosystems Engineering,
South Dakota State University,
Brookings, SD, USA
Preface

This book covers the smart technologies applicable in food production, food
manufacturing, food packaging, food monitoring and surveillance, food supply
chain and food waste management along with coproduct recovery. This book consists
of four sections: the first section consists of chapters primarily focusing on the recent
smart food production innovations such as precision agriculture, vertical farming,
automation, robotics, smart livestock technology, modern greenhouse practices,
artificial intelligence and blockchain technology that dramatically increase the quality
of raw materials for the food industry and also the use of tools to forecast issues that
affect food and agriculture on the planet. The second section provides the current
knowledge and developments related to the recent smart technologies in manufactur-
ing pertaining to various food sectors, non-thermal food preservation technologies,
3D printing and food packaging, developed for the food manufacturing industries that
improve the organoleptic and nutritional quality, enhance chemical and microbial
safety, as well as cost-effectiveness and convenience of processed foods. The third
section covers smart technologies to ensure food safety in the supply chain, moni-
toring and surveillance of food contamination and neural network approach for risk
assessment. The fourth section covers topics relevant to the minimization of waste
and maximization of co-product recovery in food processing, upcycling technologies
in food and sustainable value stream mapping in the food industry. This section
covers both general and practical knowledge and information about the current and
potential waste treatment methods that help food technologists, environmental and
agricultural engineers/scientists in industries and governmental entities in their quest
to improve food and agricultural waste management and generate value from waste.
In conclusion, the book provides the most updated information and advancements
made in the field of food technology and food supply chain using IoT.

New Delhi, India Shalini Sehgal

Punjab, India Barinderjit Singh
New Delhi, India Vasudha Sharma

vii
Contents

Part I Smart Farming for Food Production

1 Smart and Sustainable Food Production Technologies . . . . . . . . . . 3
Anuj Kumar, Shantanu Kumar Dubey, R. Sendhil, A. K. Mishra,
Uma Sah, Truptimayee Suna, and Ramesh Chand
2 Smart Technologies in Livestock Farming . . . . . . . . . . . . . . . . . . . . 25
Amandeep Singh, Y. S. Jadoun, Parkash Singh Brar,
and Gurpreet Kour
3 Prospects of Smart Aquaculture in Indian Scenario: A New
Horizon in the Management of Aquaculture Production
Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
B. K. Das, D. K. Meena, Akankshya Das, and A. K. Sahoo
4 Smart and Automatic Milking Systems: Benefits and Prospects . . . 87
Suvarna Bhoj, Ayon Tarafdar, Mukesh Singh, and G. K. Gaur

Part II Smart Food Manufacturing

5 Smart Technologies in Food Manufacturing . . . . . . . . . . . . . . . . . . 125
Rahul Vashishth, Arun Kumar Pandey, Parinder Kaur,
and Anil Dutt Semwal
6 Non-thermal Food Preservation Technologies . . . . . . . . . . . . . . . . . 157
Ravneet Kaur, Shubhra Shekhar, Sahil Chaudhary, Barinderjit Singh,
and Kamlesh Prasad
7 3D Printing: Technologies, Fundamentals, and Applications
in Food Industries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197
Mohammed A. Bareen, Jatindra K. Sahu, Sangeeta Prakash,
and Bhesh Bhandari

ix
x Contents

8 Smart Food Packaging Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235

Aastha Bhardwaj, Nitya Sharma, Vasudha Sharma, Tanweer Alam,
and Syed Shafia

Part III Smart Food Safety in Food Supply Chain

9 Smart Monitoring and Surveillance of Food Contamination . . . . . . 263
Shalini Sehgal, Sunita Aggarwal, Ashok Saini, Manisha Thakur,
and Kartik Soni
10 Neural Network Approach for Risk Assessment Along the Food
Supply Chain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287
Uma Tiwari

Part IV Sustainable Food Waste Management and Coproduct

Recovery
11 Waste Minimization and Management in Food Industry . . . . . . . . . 309
Rahul Kumar, Vasudha Sharma, and Maria Jose Oruna-Concha
12 Co-Product Recovery in Food Processing . . . . . . . . . . . . . . . . . . . . 341
Abhay Tiwari, Garima Singh, Kanika Chowdhary, Gaurav Choudhir,
Vasudha Sharma, Satyawati Sharma, and Rupesh K. Srivastava
13 Upcycling Technologies in the Food Industry . . . . . . . . . . . . . . . . . 367
Rubeka Idrishi, Divya Aggarwal, and Vasudha Sharma
14 Sustainable Value Stream Mapping in the Food Industry . . . . . . . . 393
Himanshi Garg and Soumya Ranjan Purohit
About the Editors

Shalini Sehgal is presently working as an Associate

Professor in the Department of Food Technology at
the Bhaskaracharya College of Applied Sciences (Uni-
versity of Delhi), India. Dr. Sehgal holds a Doctorate in
Dairy Microbiology from National Dairy Research
Institute (N.D.R.I), Karnal, India. She has 23 years of
experience in the field of academics and research along
with administration. She has been awarded the Award of
Excellence in Food Technology by AATSEA, Thailand,
and Society of Applied Biotechnology, India, and Best
Teacher Award by the State Government of Delhi,
India. She is the former Director, Quality Assurance
and Food Fortification at Food Safety and Standards
Authority of India (FSSAI), GOI. Her area of interest
is Food Safety, and she is trained in Laboratory Quality
Management Systems (as per ISO 17025), HACCP
Implementation, IS 22000: Food Safety Management
System and Food Safety and Food Hygiene. Also she
has expertise in Container Integrity and undergone train-
ing by USFDA at their Alameda Lab, California, USA.
She has worked as National Food Safety Consultant
with WHO and also undertaken projects on safety
aspects of street foods, fresh produce and probiotics.
Dr. Sehgal has authored two books in the area of Chem-
ical and Microbiological testing of Food and chapters on
different areas of Food Microbiology and Food Safety.
She has published her research work in journals of
repute. She has introduced the concept of Better Process
Control School in India for the food industry profes-
sionals in collaboration with USFDA, India office.
Dr. Sehgal has been instrumental in introducing Food
xi
xii About the Editors

Safety as a core subject across India. Currently, She is

President, AFSTI, Delhi Chapter, and member of Tech-
nical and Regulatory board of AIFPA along with board
of studies of different universities.

Barinderjit Singh earned a B.Tech. (Food Technol-

ogy), M.Tech. (Food Engineering and Technology),
Ph.D. (Food Technology) and MBA (Operation Man-
agement). Apart from this, he also holds to his credit PG
Diploma in Dairy Technology, Diploma in Export Man-
agement and Certificate, Diploma, Advance Diploma in
the French language. He has more than 14 years of work
experience in both academics and the food industries.
Currently, he is working as an Assistant Professor (Food
Technology) in the Department of Food Science and
Technology at I. K. Gujral Punjab Technical University,
Punjab, India. He has contributed over 54 scientific
papers in different national/international journals and
conferences in the area of food science/technology/engi-
neering. Currently, he also served as Vice President of
the ICASR: International Council of Applied Science
Research, International Centre for Research and Inno-
vation, Eudoxia Research Centre, India.

Vasudha Sharma is working as Assistant Professor in

the Department of Food Technology, Jamia Hamdard,
New Delhi, India. Dr. Sharma obtained her PhD in Food
Process Engineering from IIT Kharagpur and has more
than 10 years of teaching and applied research experi-
ence in food processing technologies. She has published
widely in the areas of functional foods, non-dairy
probiotics, process optimization and nanobiosensors
for food safety. Dr. Sharma has guided more than
50 M.Tech. students for their dissertation and has
seven Ph.D. students’ in progress. She has over
20 research publications, 12 book chapters, one book,
five popular articles and three patents (filed) to her
credit. Currently, Dr. Sharma has Indian Council of
Medical Research (ICMR) and National Project Imple-
mentation Unit (NPIU)-TEQIP-funded research projects
in the area of non-dairy functional probiotic product
development.
About the Editors xiii

Dr. Sharma has received the Centre for Quality and

Food Safety (CQFS) Award 2021 on World Food Safety
Day 2021 and has served as Member of Expert Com-
mittee for drafting academic curricula for Food Safety
education in India. Dr. Sharma is also involved in sev-
eral extension activities in the area of food safety. She is
also currently, serving as the Vice President of AFSTI
Delhi Chapter and Executive Member for WICCI Delhi
Food Processing Council.
 Part I
Smart Farming for Food Production
Chapter 1
Smart and Sustainable Food Production
Technologies

Anuj Kumar, Shantanu Kumar Dubey, R. Sendhil, A. K. Mishra, Uma Sah,

Truptimayee Suna, and Ramesh Chand

Abstract The oldest vocation of human civilization has been farming. Over centu-
ries, mankind has been dependent on earth for food but there have been several
damaging effects of our ways of growing crops. The dysfunctional consequences
have resulted in damage to soil, water, micro-flora, and fauna to a considerable
extent. This led to the search for the alternative approaches which may transform the
landscape of Indian agriculture so that sustainability may become its innate attribute.
The approach of smart and sustainable food production has been conceptualized into
the re-orientated use of land, water, carbon, nitrogen, and energy all of which has
essentially the link with nature. Experiences have confirmed that the judicious and
smart uses of these resources have resulted into not only the enhanced or even
comparable level of resources’ productivity, the profitability has also moved upward
and the sustainability of the production systems has been protected. This chapter
highlights many of the smart technological options for production factor optimiza-
tion and several end-to-end disruptive technologies and practices have been
discussed. Moreover, the up-scaling and out-scaling mechanisms of those innova-
tions are also deliberated.

Keywords Smart agriculture · Sensors · Smart irrigation systems · Drones ·

Precision farming

A. Kumar · R. Sendhil · R. Chand

ICAR-Indian Institute of Wheat and Barley, Karnal, Haryana, India
S. K. Dubey (*)
ICAR-Agricultural Technology Application Research Institute, Kanpur, UP, India
A. K. Mishra · T. Suna
ICAR-Indian Agricultural Research Institute, New Delhi, India
U. Sah
ICAR-Indian Institute of Pulses Research, Kanpur, UP, India

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 3
S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies,
https://doi.org/10.1007/978-981-19-1746-2_1
4 A. Kumar et al.

1.1 Introduction

Agriculture is the pivot for ensuring global food security. Value added from the
sector has reached 68% between 2000 and 2018, estimated at US$ 3.4 trillion and its
share to the global gross domestic product is around 4% since 2000. It has generated
employment to around 884 million, globally in 2019. In terms of global food
production, the primary crops output has been estimated at 9.2 billion tonnes during
2018, which is about 50% more in comparison to 2020. Of the primary crops,
sugarcane, maize, wheat, and rice account for around 50% of the world output
(FAO 2020). Among the countries, notably India’s surge in food production, post-
Independence, is largely attributed to the semi-dwarf rice and wheat varieties and has
been the harbinger of the Green Revolution. Food production has increased by multi-
folds since 1950. In the past seven decades, food grains production has jumped from
about 51 million tonnes (1950–1951) to 309 million tonnes (2020–2021) witnessing
a quantum growth of 507.34%, largely attributed by productivity growth (357%),
followed by acreage (33%) as evident from Fig. 1.1.
Decade-wise analysis of food grains production in India (Table 1.1) indicates the
magnitude of contributing factors (area and productivity). Except the area under food
grains during the 1980s and 1990s, the rest all exhibited a positive change (com-
pound annual growth rate) in the area as well as food grains productivity. The
production growth was highest during the 1950s, largely attributed to productivity
growth (2.26% per annum), and followed by area (1.94% per annum). In the
subsequent decades, the growth rate witnessed a decline till 2009–2010 and then
experienced an increasing trend. Barring the 1950s, the rest of the period showed
less than 1% growth per annum in the area under food grains. Within the food grains,
wheat is the only commodity that recorded a positive change in the area, production
as well as productivity in the past seven decades, and also registered the highest

Fig. 1.1 Trends in food grains production for India. (Source: FAO 2020)
1 Smart and Sustainable Food Production Technologies 5

Table 1.1 Growth in food grains production in India

Compound annual growth rate (%)
Period Area Production Productivity
1950–1951 to 1959–1960 1.94 4.25 2.26
1960–1961 to 1969–1970 0.52 1.85 1.32
1970–1971 to 1979–1980 0.46 2.07 1.60
1980–1981 to 1989–1990 0.23 2.78 2.97
1990–1991 to 1999–2000 0.08 2.09 2.17
2000–2001 to 2009–2010 0.29 1.90 1.60
2010–2011 to 2020–2021* 0.27 2.07 1.80
Overall (1950–1951 to 2020–2021) 0.18 2.38 2.19

Fig. 1.2 Smart agriculture and its integrals (Bach and Mauser 2018)

output growth (Sharma et al. 2014). The gargantuan production in the food grains led
to a status of surplus, by catapulting the country from “food grain importer” to a
“net-exporter” one (Chand 2001). Yet, with the increasing production challenges in
the recent past and aggravated by climate change adverse effects, there is a dire need
for the adoption of smart technologies as the country needs to feed millions despite
marching progressively by crossing the magical figure of 300 million tonnes.
Smart agriculture is a concept focused on managing agricultural resources using
advanced technology inclusive of data analytics, cloud computing, and the Internet
of Things (IoT) for various activities such as tracking, monitoring, automating, and
analyzing data (Fig. 1.2).
It is a big deviation from traditional farming as it offers certainty and predictabil-
ity. Tools such as robotics, automation, and cloud software systems are used to make
the farming systems efficient and sustainable. Robots, drones, and sensors placed
6 A. Kumar et al.

Fig. 1.3 Information-based farming (Saiz-Rubio and Rovira-Más 2020)

throughout the farms collect data which is processed to create valuable insights. It
also helps to optimize the human resources and ease the farmers.
Smart farming also decreases the ecological footprint of farming. The minimized
or limited use of fertilizers and pesticides such as in precision farming reduces
leaching as well as the greenhouse gasses.
Various technologies have been integrated in agriculture so that the farmer and
environment both are benefited. Some of them are used (Fig. 1.3) as follows and
discussed in detail in this chapter.
1. Field mapping
2. Predictive analytics
3. Data generation for analytics
4. Tracking and monitoring
5. Automation using drones and robotics
6. Warehousing
7. Saving energy, for example, using smart irrigation.

1.2 Smart Water Management

Smart water management primarily intends scientific, efficient, reasonable, and

sustainable application of irrigation water at appropriate times of need. It also
ascertains the better utilization of recycled and treated wastewater. The rapidly
increasing demands of food grains, pulses, oil seeds, and other agro based commod-
ities demand a rational increase in the production and productivity along with higher
water productivity as the same has become a limiting resource. The burgeoning
population increases environmental issues and pressure on the food and agriculture
sector makes the water even a more valued asset. Furthermore, the booming popu-
lace is posing tremendous environmental threat and pressure on resources sustain-
ability; therefore, the agriculture sector needs to make concerted efforts for most
1 Smart and Sustainable Food Production Technologies 7

Fig. 1.4 Components of smart green farming

efficient use of the world’s limiting and precious commodity, i.e. good quality water
for which most modern technologies are a must. Therefore, appropriate technologies
and related activities for water management are a must.
As the world is struggling with challenges of looming climate change such as the
growing intensity and frequency of floods, drought, cloud bursts, landslides, dust
storms, cyclones/hurricanes, and erratic weather phenomenon which has brought the
agricultural sector on the brink of serious production risks. Therefore, the smart and
wise use of limited water resources in agricultural production for producing more
crop per drop to enhance water productivity has become the need of the hour not
only in water scarce regions but also in the regions having fairly good water
availability as of today. Hence, the development of new water smart strategies and
technologies with the more prudent and scientific water use techniques are a must to
boost and improve crop yields while ensuring the resources sustainability. Nowa-
days, several water smart agricultural production technologies belonging to all areas
of farming encompass many original, novel, and high-quality contributions, scalable
from molecular to whole plant studies, and from farm to global levels are being
developed to explore sustainable water use in different climate change scenarios
(Fig. 1.4).
In this context, the whole water supply chain starting from freshwater reservoirs
to wastewater collecting and recycling demand the attention and application of smart
water technology which in turn ensures clarity and improved control over such
resources. The related hardwares, instruments, software tools, and techniques may
8 A. Kumar et al.

optimize production, distribution, and consumption of water not only locally but also
globally. There is a plethora of hardware and software instruments, including
sensors, meters, data processing, and visualization tools, actuators, web applications,
and mobile apps/controls are available in the market and the list is increasing.
However, the high implementation cost of smart water technologies is the great
barrier to the implementation of it especially in developing countries like India.
Similarly, data security and its reliability along with system ability to give accurate
results are some of the key challenges in its field of implementation of smart water
management technologies in agriculture and allied sectors (Gupta and Kulat 2018).
Leakage detection and assessment is usually the difference between measured and
predicted hydraulic parameters (Tucciarelli et al. 1999). It is worth mentioning the
Kalman filter-based algorithm for the prediction of hydraulic parameters of water
distribution systems developed by Ye and Fenner (2011).
With the advancement of technology in the twenty-first century, we have tech-
nologies, instruments, and equipment that are vital to address the challenges of water
scarcity and make smart use of water to ensure socio-economic well-being without
challenging the long-term resources sustainability. The high use of fertilizers and
other chemicals on the farms and rapid growth of mining and construction sectors
have contributed adversely to the overall water quality globally (Prasad et al. 2015).
It is now a well-known reality to abridge the barriers between the physical and digital
world to support the management of complex water cycle in irrigated agriculture by
integrated applications of all the available techniques. Some of the modern tools and
techniques used for prudent management of water are being described as follows.

1.2.1 Smart Water Meters and Monitoring Systems

Real-time water consumption measuring helps in identifying excessive usage and

waste points and also ensures the correct usage patterns and making predictions for
future consumption. Analog meters are still used in most of the cities of our country
and across the world for measuring domestic and corporate water usage. These early
age meters are being used for long periods but they are unable to report any anomaly
in water use to the water authorities as they lack connectivity and reproducibility.
Smart water meters support the correction of water consumption for budgeting and
sustainability goal. Therefore, connected smart meters leveraging IoT technology
enable owners to view their water usage and also send immediate alerts to users in
case of excessive use of water. Smart meters notify the water authorities regarding
the wasting of water by the owner and helping the water authorities in intervening a
violator. Nowadays Internet of Things (IoT) and Remote Sensing (RS) techniques
are being used in tandem in research and actual field use for monitoring the water use
by collecting and analyzing data from remote locations without the manager actually
being present on site.
1 Smart and Sustainable Food Production Technologies 9

1.2.2 Smart Water Sensors

Managing the water smartly involves a broad application of sensors because of their
diversity and purposes. In every basic water supply chain, sensors measure the
quality of raw catchment water and its chemical composition after treatment and
wastewater recycling. It also measures the changing quantity in the storage reservoir
as well as the pressure in the distribution pipeline and wear and tear of the machinery
that process and distribute water to end-users.
The immediate detection of the leak which is unseen by the human eye in almost
every pipeline can be ensured by using a smart sensor integrated with a maze of
pipes. Actually, the maze of pipes enables the smooth flow of freshwater into the
farms/building and takes out the waste materials. The sensor sends an alert to the
farm manager in case of unwanted water flow which in turn guides the farm manager
in detecting the exact location of the leak and carrying out the necessary repairs.
Such sensors will go a long way in detecting the changes and the same could be
plugged in. Managers may receive the appropriate insights into the changing condi-
tions of water at different points by using the data collected by IoT water sensors.
Simitha and Subodh (2019) also used IoT/WSN-based water quality monitoring
systems for smart cities which needs to be extended to the farm sector as well.

1.2.3 Smart Irrigation Systems

A major share of over 80% of total water usage in India goes to irrigation of crops
and raising ornamental plants. Smart irrigation systems using IoT modules may
facilitate water conservation to a new level by reduction in the excessive water use in
farming and allied activities. Smart sensors in combination with smart irrigation
application systems like sprinklers and drips shall enable water supply by
ascertaining soil moisture as per the need of plants. Hence, instead of the traditional
method, using smart devices is an appropriate intervention for water conservation. In
the scientific literature this is referred to as smart irrigation scheduling and efficient
irrigation water management.

1.2.3.1 Use of Information and Communication Technologies (ICT)

A large number of ICT have found place in the irrigation sector of late. The Wireless
Sensor Network (WSN) innovation is promising ICT intervention for checking the
data concerning an outside region of the farming environment which is inevitable too
(Pandey et al. 2019). This kind of sensor framework gathers ecological and edaphic
data. Actually, it uses a moisture and temperature sensor which helps the field to
control the water level as well as soil temperature. The use of WSN as GSM (Global
System for Mobile communication) has been advocated for use (Suresh et al. 2014).
10 A. Kumar et al.

The shadow of climate change looming over the world is a reality now where safe
and good quality of water is the epicenter of sustainability and is becoming ever
scarcer. Albeit, water shall remain a vital resource for techno-economic progress,
healthy ecosystems, and human survival. Modern Information and Communication
Technologies (ICT), today had more pronounced applications in smart water man-
agement such as water resources mapping and establishing the models for precise
availability and meteorological predictions.

1.2.3.2 The Automated Hydrological Information Systems (AHIS)

For the assessment of the total available water resources for planning for future
distribution to various sectors, the hydrologic measurement is a must. Indeed, it is
the need of the hour and ultimate requirement for water resources assessment for the
water resource managers to have the correct information about the hydrology of the
watershed or the river basin. Modern tools and advanced technologies have been
developed globally for collection, conveyance, processing, and presenting the data
capturing the hydrological status of a river basin in real-time. In India, however,
there is a need for technology adaptation in almost all river basins in real time.
According to the World Bank estimate, there is one-fourth filtration and leakage loss
globally, impacting water availability and the farm economy.
Looking into the future of ever expanding global urban cities and their food
requirements, water management is the key word. Water requirement is, however,
grossly challenged by the demographic pressures, augmented water demands, and
the yawning water deficit. In this context, optimizing and the integrated water
management processes has become unavoidable. It is in this reference that IoT
enables careful monitoring of water resources and helps in optimizing and efficient
use of water resources and their management. Gubbi et al. (2013) evolved an IoT
framework along with a decision support interface supporting the cloud-centric
storage, processing, and analysis of data received from ubiquitous sensors. Cruz
et al. (2018) further consolidated the case by advocating the reference model for IoT
middleware platform capable to supplement intelligent IoT applications. The agri-
culture landscape is also harnessing the dividends of IoT-based solutions (Sharma
et al. 2016). It is believed that these intelligent solutions shall be instrumental in
smart irrigation for optimum water utilization. The major elements for designing the
smart irrigation system include soil moisture, precipitation, and evaporation. The
major dimensions of IoT-based water management systems include: smart water
metering and management (SWM), use of modern technologies for smart water
deliveries to the intended place/crop, economic objectives, etc. These objectives of
smart water management are used to reap the rich benefits of using IoT for scientific
and efficient water management, and developing IoT enabled on-farm water man-
agement technologies.
The water sector is under tremendous pressure as the natural resources are very
limited and being used close to their maximum potential. It is essential to identify not
only current needs but also future needs for ensuring the sustainability of water
1 Smart and Sustainable Food Production Technologies 11

resources. Artificial intelligence helps to automate risk management and simulate the
behavior of water networks and detect anomalies and improves efficiency.
Meeting the satisfaction of the end user is the main challenge for water service
providers. It is the fast and simple communication which may help to mitigate this
challenge. Mobile based applications facilitate companies in offering the new
services to customers, disseminating real-time information and scope for contacting
staff and solving the potential queries on a 24  7 basis. For example, Smart-Aqua,
by Aqualia, is an Android and IOS based mobile app that allows customers to
manage the services provided by the company at any time and conveniently.

1.2.3.3 Application of Blockchain for the Strong Link Between Supply

and Demand Centers

Water sector, by virtue of its larger magnitude demands transparent, fast, flexible,
and secure information, and knowledge exchange, among all the actors associated in
the integrated water management process. The digital purchase-sale transaction
between the actors for water services is enabled by blockchain intervention which
at the same time strengthens the transparency and secure non-personal data. The
Catalonian Technological Centre (Eurecat) offering the blockchain platform and
enabling the water services resulting into strengthened relationships between con-
sumers and water management entities is a good example of this system. The water
management sector has witnessed the unprecedented technological advances
impacting the functions of the related entities, transforming the business climate,
and resulting in changed opinions of the end-users. Blockchain is an open and
distributed ledger that records the transactions between two parties in an efficient,
verifiable, and permanent way (Iansiti and Lakhani 2017). Blockchain is the disrup-
tive ICT intervention that may revolutionize how data could be used for agriculture.
This technology also offers a dependable platform for tracing the anonymous trans-
actions which may detect the fraud or malfunction, if any in this process and
reporting the problems on a real-time basis (Haveson et al. 2017; Sylvester 2019;
Mohapatra et al. 2021).

1.2.3.4 Automated Distribution Systems and Precision Application

Algorithms

Environmental sensors and predefined algorithms have come forward in a big way
for dynamically regulating and managing the water supply. As a result, a large
number of related companies are shifting to an automatic water management regime.
The smart irrigation, for example, sprinkler system may capture the soil moisture, air
humidity, and crop condition and using these reads it may provide enough water to
the crop.
12 A. Kumar et al.

1.3 Nutrient Smart Technologies

The use of nutrients for crop production is very much essential for sustainable
production as well as for maintaining soil health. There are 16 nutrients essential
for plants during their life cycle. Nitrogen, phosphorus, and potassium are the
primary (macro) nutrients for crops and plants require these in larger quantities
mostly in the form of chemical fertilizers such as urea, DAP, MOP, NPK mixture,
etc. On the other hand, the secondary nutrients such as calcium, magnesium, and
sulfur are required in lesser quantities by the plants. The micronutrients such as
boron, chlorine, copper, iron, manganese, molybdenum, and zinc are required in
very small amounts by the plants, but they are crucial to plant development and
profitable crop production alike major nutrients. These micronutrients work “behind
the scene” as the activators of many plant functions.
Application of nitrogen, phosphorus, and potash is widely adopted by farmers
across the country but the application of secondary and micronutrients is rarely
adopted by the farmers. Even many farmers are not applying potassic fertilizers in
their fields. It has been also observed that farmers in Punjab, Haryana, and western
Uttar Pradesh are using an overdose of nitrogen in wheat, paddy, sugarcane, and
other crops. The technology of Integrated Nutrient Management (INM) has not been
adopted by most farmers. Time has come to upscale and outscale INM to each and
every farm for balanced fertilization.

1.3.1 Application of Fertilizers Based on Soil Health

Application of fertilizers on a soil health card basis is becoming a reality in India

with the launch of the Soil Health Card (SHC) scheme in 2015. This mega scheme
was launched to provide SHC to all farm holdings in the country at an interval of
2 years so that farmers can apply recommended dosages of nutrients for crop
production and improving soil health and fertility. Till today 140 million SHCs
have been prepared and distributed among the farmers. It contains the status of soil
with respect to 12 parameters, namely NPK (Macronutrients); S (Secondary nutri-
ent); Zn, Fe, Cu, Mn, Bo (Micronutrients); and pH, EC, OC (Physical parameters).
Under this scheme, the Government has made a provision for the assistance of
Rs. 2500/ha for the distribution of micronutrients and soil ameliorates. For the
supply of gypsum/pyrite/lime/dolomite, 50% cost of the material + transportation
limited to Rs. 750/ha. For the adoption of Integrated Nutrient Management, a
provision of Rs. 1200/ha (restricted to 4 ha area) has been made (MoA&FW 2021).
1 Smart and Sustainable Food Production Technologies 13

1.3.2 Smart Fertilizer Management

The new era crop nutrition technology-smart fertilizers enables not only the dosage
reduction, but it also improves the crop yield in an environment-friendly manner. So
far, smart fertilizers are available for the phosphates in the form of smart phosphates
and for micronutrients - smart micronutrients, i.e. the fertilizers for zinc, boron,
manganese, etc. Smart phosphate is basically the replacement for water-soluble
phosphates available in popular chemical fertilizers such as DAP, SSP, etc.; and
similarly, smart micronutrients can replace water-soluble micronutrients (zinc sul-
fate, borax, etc.). The use of smart fertilizers facilitates the nutrient release as per the
requirements of the plant itself. This becomes beneficial for yields to increase as
plant require different nutrients at different crop stages. Moreover, minimizing the
nutrient losses reduces the phosphate dosage by 10%, i.e. from ¼th to ½ of current
dosages and contributes for yield enhancement by 10%. In case of smart
micronutrients application, such reduction may be ensured to the extent of 90%
with corresponding yields increase by 15–20 percent. In this way, the investment
made by farmers per acre is reduced but the yield is more than the current fertilizers
application level. AAPFCO (1995) and Trenkel (1997) has long back academically
defined that the slow- or controlled-release fertilizers are those which either:
1. delays its availability for plant uptake and use after application or
2. is available to the plant for a significantly longer period than reference fertilizers.
Practically, there is no operational differentiation between slow-release and
controlled-release fertilizers. However, the microbial decomposed N products,
such as urea-formaldehyde, are termed as slow-release fertilizers and coated or
encapsulated products as controlled-release fertilizers (Trenkel 1997). Delayed
availability of nutrients or consistent nutrient supply for extended periods can be
achieved through various modifications like use of semi-permeable coatings for
controlled solubility of the fertilizer in water, protein materials, occlusion,
chemicals, slow hydrolysis of water-soluble compounds of lower molecular weights,
and some other unknown means (Naz and Sulaiman 2016).

1.4 Smart Monitoring System for Soil and Crop Health

The soil type and nutrition status of soil are the important factors for deciding the
crop to be grown and their quality, increased deforestation has degraded the soil
quality enormously. Also, it is often difficult to determine the soil quality objec-
tively. In this direction, a German-based tech start-up PEAT has developed an
AI-based application called Plantix. This system identifies the soil nutrient deficien-
cies and also diagnoses the plant pests and diseases which help farmers to get an idea
for using fertilizer leading to improved harvest quality. This app uses image
recognition-based technology in which farmers capture the images of plants using
14 A. Kumar et al.

smart phones and this image is used by the app for further analysis. Trace Genomics
is another machine learning-based company supporting farmers to do soil analysis
themselves. Such app based interventions help farmers for soil and crop health
monitoring and thus, producing healthy crops yielding higher levels of productivity.
IoT-based crop health monitoring includes monitoring of different parameters like
temperature, humidity, precipitation, pest intrusion, seed and soil quality, thus
enabling better decision-making in terms of crop quality and health
(info@biz4intellia.com). GSM technology gives a smarter and an efficient way for
a better yield of crops (Bogena et al. 2010; Ramson and Moni 2017). The IoT-based
monitoring systems ensure low cost, high fidelity, flexibility, and rapid deployment.
There are many other wireless networking protocols such as LoRaWAN, SIGFOX,
and NB-IoT which achieve a long communication range (in the order of kilometers)
while consuming low power and without the need for intermediate nodes and they
are attractive and user-friendly options for agricultural operations (Davcev et al.
2018; Gia et al. 2019).

1.4.1 Analyzing Crop Health by Drones

Drones are the emerging technological options which facilitate aerial photography at
considerably lower cost than using a helicopter or small plane (West and Bowman
2016). Drones have confirmed their worth for recording the canopy reflectance
(Primicerio 2012). For quantifying the crop growth over a season, aerial images
can be taken periodically i.e. at the season’s start, at predetermined intervals during
the season and just prior to harvesting to illustrate the growth across the field, thus
highlighting any rows that show signs of stunted growth may be because of poor
irrigation or lower initial nutrient uptake (Honkavaara 2013). Sky Squirrel is one
such technology that has smoothen the drone-based Aerial imaging solutions for
crop health monitoring. In this technique, the data captured from fields are trans-
ferred to a computer and analyzed by experts. A detailed and systematic algorithm is
used to analyze the captured images and a comprehensive report containing the
current health of the farm is generated. Moreover, it also helps farmers to identify
pests and diseases thus supplementing the timely use of pest control and other
methods to take the required action.

1.5 Energy Smart Technologies

Energy is one of the components which add to the cost of production of any crop.
The use of energy-efficient technologies should be adopted in farming to reduce the
cost of cultivation. Energy is required to perform different farm operations such as
tillage operation, leveling of fields, seeding, irrigation, intercultural operations,
harvesting, threshing, and transportation. There is a need to upscale and outscale
1 Smart and Sustainable Food Production Technologies 15

energy-efficient technologies at farmers’ fields. Under the rice-wheat cropping

system, farmers are adopting conventional methods, i.e. harrowing, planking for
field preparation which requires lots of energy. Conventional tillage practices for
wheat are very intensive in India’s rice-wheat systems. For instance, tillage alone
encompasses around 25% of the total cost of conventional wheat production (Karnal,
Haryana). Due to the adoption of zero tillage technology, the number of field
operations for the establishment of the wheat crop (including tillage) decreased
from an average of seven to only one (Sharma et al. 2002). In Haryana, zero tillage
saved 59 L/ha of fuel, 8 h/ha of tractor time, and approximately 3000 MJ/ha of
energy in tractor operations as compared to conventional tillage (Sharma et al. 2002).
Such potential savings are not limited to the IGP but have also been reported in
Central India (Madhya Pradesh), where zero tillage had saved 75 L/ha of fuel by
reducing tillage operations from seven to one (Yaduraju and Mishra 2002). The
awareness for recent energy smart technologies such as Super Seeder, Turbo Happy
Seeder, and Zero till seed cum ferti drill for seeding of wheat without tilling the soil
are helping in saving huge quantities of energy. Even seeding of wheat crop with
rotavator is also an energy-smart technology wherein one go seedbed preparation, as
well as drilling of seed, is done. Most of the small farmers are adopting rotavators in
their fields to save energy as well as cost. Harvesting of wheat crop with SMS
mounted combine harvesters is also an energy-smart technology adopted by the
farmers of northern states. Use of reaper binder for quick harvesting of crops
followed by threshing with power threshers will be another option to save energy.
For in-situ and ex-situ management of paddy and wheat straw up-scaling and
out-scaling of technologies such as straw reaper, straw chopper, hay rack, mulcher,
and a straw bailer is of utmost importance. Replacement of diesel-operated pump
sets with electric operated tube wells and solar pumps need to be promoted on a
larger scale to save energy and time. All energy-efficient technologies need to be
promoted through custom hiring centers in rural areas under different Government
schemes.

1.6 Carbon Smart Technologies

The emission of carbon in agriculture varies from crop to crop, technology to

technology, and field to field. But in the wheat crop, the emission of carbon can be
reduced with the adoption of resource conservation technologies. In an on-farm
study, the zero tillage-based wheat production helped to reduce CO2 emissions by
1.5 Mg/ha (Aryal et al. 2014). Up-scaling and out-scaling of carbon smart technol-
ogies such as zero tillage for sowing wheat can be more eco-friendly technology.
Use of all such technologies to stop stubble burning such as straw reaper, straw
chopper, straw bailer, reversible MB plough, etc. in northern states should be
adopted on a larger scale. There is a need to devise extension strategies to promote
carbon smart technologies in the farmer’s field. The govt. of India has made pro-
visions through budgetary allocation for the popularization of all these machines. A
16 A. Kumar et al.

subsidy of 80% for the farmer’s groups and custom hiring centers and 50% to
individual farmers is given in a majority of wheat-growing states (MoA&FW
2021). The government is promoting custom hiring hubs in Punjab and Haryana to
popularize these machines on a larger scale. Regular awareness campaigns are being
organized by different extension agencies for the farmers. Use of mass media such as
television, radio, newspaper, social media, farmers’ fairs, and exhibitions for creat-
ing awareness among the farmers on a regular basis.
In recent years a lot has been talked about the impact of climate change on wheat
in particular and agriculture in general. The rise in mean temperature owing to
climate change and water logging due to heavy rainfall are major factors affecting
the overall production. The use of weather data for forecasting has become a regular
feature of today’s agriculture. Day-to-day variations in weather parameters are
recorded by different agencies to make predictions through prediction models.
There is a need to be a member of such portals for the weather updates in order to
avoid any negligence in the application of irrigation, herbicide, and pesticide
application.
Many farmers have become members of WhatsApp groups of Agricultural
institutes, state agricultural universities, state department of agriculture,
KrishiVigyan Kendra, m-Kisanportal, etc. for getting regular weather updates. All
the farmers/farmers groups must be linked with IMD and other departments respon-
sible for day-to-day weather forecasting. Farmers need to be educated on how to
safeguard themselves against natural risks like natural disasters/calamities, insect,
pest and diseases, and adverse weather conditions (MoA&FW 2021).

1.7 Knowledge Smart Technologies

Modern agriculture is a synonym of knowledge and skill. In recent years there has
been a tremendous change in agriculture and it is shifting from traditional to modern
and from modern to high-tech. In hi-tech agriculture, proper knowledge and skill are
the prerequisite to make it a profitable venture as a heavy investment is made on
infrastructure. Now protected cultivation is done in a poly house or greenhouse or
low tunnels require a lot of skills such as selection of crops and their varieties,
cultivation skills, intercultural skills, harvesting skills, grading and packaging, and
marketing skills. Knowledge of the e-NAM portal for selling farm produce is the
need of the hour and by registration farmers are getting a better price. To promote
online marketing of agricultural commodities across the country and to provide
maximum benefit to the farmer, on July 1, 2015, the government launched the
e-National Agriculture Market (e-NAM) through which a web-based platform has
been deployed across 250 regulated markets to promote online trading. There are
other online platforms too for marketing of produces and price negotiation. Farmers
can get the price information of their produce which is available on the
AGMARKNET website (www.agmarknet.nic.in) or through Kisan Call Centers or
SMS. The buyer-seller portal is available at www.farmer.gov.in/buysell.htm.
1 Smart and Sustainable Food Production Technologies 17

Farmers in a group may form marketing cooperatives and FPOs for better marketing
reach and these marketing cooperatives can open retail and wholesale outlets.
Farmers may also operate cold storage and warehouse to store the produce in
order to avoid distress sales. It has become very important to remain updated in
agribusiness for a better price realization and for that, the following steps must be
taken. There are other online platforms too for marketing produce and price nego-
tiation. AGMARKNET website (www.agmarknet.nic.in) or Kisan call centers have
been brought into action through which Farmers can fetch information about their
produce. The buyer-seller portal can be accessible at www.farmer.gov.in/buysell.
html. Farmers in a group may form marketing cooperatives and FPOs for the end-to-
end support and cover services like better marketing reach and opening of retail and
wholesale outlets. Cold storage and warehouse can be a better option to store farm
produce and operated by farmers to avoid distress sale. It has become very important
to remain updated in agribusiness for better price realization.

1.8 Agricultural Robotics

AI companies are manufacturing multitasking robots for farming purposes. This type
of robot is trained to control weeds and harvest crops at a faster pace with higher
volumes compared to humans. Checking the quality of crops, detection of weeds,
and picking of crops such activities can be accomplished by these robots (Fig. 1.5).
These robots are also capable of fighting challenges faced by agricultural force labor.
Some of the well-known names that are actively involved in the research and
development for various types of weed control robots are the Wageningen Univer-
sity and Research Center (The Netherlands), Queensland University of Technology,

Fig. 1.5 Robots used for planting

18 A. Kumar et al.

the University of Sydney, Blue River Technologies (Sunnyvale, CA, USA),

Switzerland’s ecoRobotix (Yverdon-les-Bains, Switzerland), and France’s Naio
Technologies (Escalquens, France). For example, a flexible multipurpose farming
and weeding robot platform named BoniRob. (a) BoniRob (Ruckelshausen et al.
2009; Sander 2015) an integrated multipurpose farming robotic platform for row
crops weed control developed by interdisciplinary teams which is also capable of
creating detailed map of the field, (b) AgBot II (Bawden et al. 2014) an innovate field
robot prototype developed by the Queensland University of Technology for auton-
omous fertilizer application, weed detection, and classification, and mechanical or
chemical weed control, (c) Autonome Roboter (Ruckelshausen et al. 2006) a
research effort robot developed by Osnabrück University of Applied Sciences for
weed control, (d) Tertill (MacKean et al. 2017) a fully autonomous solar-powered
compact robot developed by Franklin Robotics for weed cutting, (e) Hortibot
(Jørgensen et al. 2007) a robot developed by the Faculty of Agricultural Sciences
at the University of Aarhus for transporting and attaching a variety of weed detection
and control tools such as cameras, herbicide, and spraying booms.

1.9 Precision Farming and Predictive Analytics

AI applications in agriculture have developed applications and tools which help

farmers in inaccurate and controlled farming by providing proper guidance to
farmers about water management, crop rotation, timely harvesting, type of crop to
be grown, optimum planting, pest attacks, and nutrition management. In particular, it
is worth pointing out that predictive analytics datasets always have data, which is
linked to crop rotations, crop patterns, weather patterns, the conditions of the
environment, the types of soil, soil nutrients, Geographic Information System
(GIS) data, farmer details, Global Positioning System (GPS) data, agriculture
machinery data, like yield monitoring as well as Variable Rate Fertilizers (VRF)
(Grisso et al. 2009). While using the machine learning algorithms in connection with
images captured by satellites and drones, AI-enabled technologies predict weather
conditions, analyze crop sustainability, and evaluate farms for the presence of
diseases or pests and poor plant nutrition on farms with data like temperature,
precipitation, wind speed, and solar radiation.

1.10 Smart Green Housing

In the conventional strategy for cultivating, human work was necessary to see the
greenhouse at a particular point and to observe all the required levels physically. The
regular technique is observed to be slow and requires a large amount of effort and
energy. Along these lines, this analysis is around building up a framework that can
consequently screen and anticipates various changes in light, temperature soil
1 Smart and Sustainable Food Production Technologies 19

moisture, and humidity levels of the greenhouse. The goal of the survey is to build up
a programmed monitoring device observing framework utilizing sensors and send
email warnings and messages to the mobiles (Sultan et al. 2021). The recommended
framework has an estimation which is equipped for identifying the levels of light,
temperature, soil moisture, and humidity. The framework additionally had an instru-
ment to caution agriculturists with respect to the limitation change in the conserva-
tory then safeguard measures can be taken in advance. In this examination, a few
experiments were directed to a particular final aim to demonstrate the suitability of
the framework. Test outcomes showed that the dependability of the framework in
spreading data straightforwardly to the agriculturists could be picked up astound-
ingly in different conditions.

1.11 Vertical Farming

In the physical layout, the plants are vertically stacked in a tower-like structure. This
way, the area required to grow plants is minimized. Next, a combination of natural
lights and artificial lights is used to maintain a perfect environment for the efficient
growth of the plants. The third parameter is the growing medium for the plants.
Instead of soil, aeroponic, hydroponic, or aquaponic growing mediums are used as
the growing medium. Using advanced greenhouse technology such as hydroponics
and aeroponics, the vertical farm could theoretically produce fish, poultry, fruit, and
vegetables (Despommier 2010). This way, more than 3 dozen types of vegetables
can be chosen to grow inside the building hydroponically (Ankri 2010). The most
common products now produced in vertical farms are lettuce, tomato, Chinese
cabbage, eggplant, green onion/chives (Fig. 1.6). Vertical farming has several
advantages, which makes it promising for the future of agriculture. The land
requirement is quite low, water consumption is 80% less, the water is recycled and
saved, it is pesticide-free, and in cases of high-tech farms, there is no real depen-
dency on the weather. A vertical farm makes farming within the confines of a city, a
reality. In case the farms are nearby, the produce is quickly delivered and always
fresh; when compared to the refrigerated produce usually available at supermarkets.
Reduction in transportation reduces the fossil fuel cost and resulting emissions and
thus also reduces the spoilage in transportation. However, like everything else
vertical farming has its drawbacks. Initial capital costs for establishing the vertical
farming system are the major problem. In addition, there are costs of erecting the
structures along with its automation like computerized and monitoring systems,
remote control systems and software’s, automated racking and stacking systems,
programmable LED lighting systems, climate control system, etc.
20 A. Kumar et al.

Fig. 1.6 Vertical farming

1.12 Cloud Software Systems in Agriculture

Cloud computing is an information technology paradigm through which users can

access shared pools of configurable system resources over the internet. Such a
sharing of resources enables coherence and economies of scale, which functions
like a public utility, which can be quickly allotted by service providers to users with
very little managerial effort. In sum, cloud computing can help with real-time
computation, data access, and storage to users without having to know or worry
about the physical location and configuration of the system that delivers the services.

1.12.1 Practical Information Sharing

Web-based agriculture management information systems can be useful in the agri-

culture sector, as it brings the latest bulletins on weather, prices, fertilizer, sowing of
crops, etc., to farmers in rural areas. AgJunction (Precision Ag 2012) has developed
an open and cloud-based system that captures and shares data from many types of
precision agriculture controllers on a farm to lower costs and reduce environmental
impact. Additionally, Fujitsu has launched the “Akisai” (Fujitsu Limited 2012)
cloud for food and agricultural industries and is utilizing information communica-
tions technology to ensure plentiful food supplies in the future (Sourcetrace 2022).
1 Smart and Sustainable Food Production Technologies 21

1.13 Plant Factory

Plant factory refers to a plant production facility consisting of six principal compo-
nents: a thermally insulated and nearly airtight warehouse-like opaque structure,
4–20 tiers equipped with hydroponic culture beds, and lighting devices such as
fluorescent and LED (light-emitting diodes) lamps, air conditioners with air fans, a
CO2 supply unit, a nutrient solution supply unit with water pumps, and an environ-
mental control unit (Kozai 2007). Workers generally enter the cultivation room of
the plant factory only after taking hot water or air showers and wearing clean clothes.
Using plant factories, high-quality pesticide-free plants are produced all year round
owing to the optimal control of the aerial and root-zone environment. Leaf-grown
vegetables after harvest are doubled compared to those produced in a greenhouse
because the bacterial load is generally lower than 300 CFU/g, which is 1/100th to
1/1000th that of field-grown vegetables after washing with tap water. Plant factories
with artificial light are becoming increasingly important nowadays for commercial
production of leaf vegetables and other short-height leaf plants to enhance local
production for local consumption in urban areas (Kozai 2013). Residents/users
living in urban areas and having little chance to grow plants in the open field may
enjoy using a household plant factory. It is suggested that such a plant factory and its
network have the potential to contribute to a better life for people in urban areas, and
to provide an educational perspective to them about science, technology, virtual
community, plant growing, the origin of food, global ecosystems, and global
productivity.

1.14 Artificial Intelligence Based Smart Water

Management Solutions

The AI can help the farmers to increase the capacity of production and reduce the
cost of production and drudgery. No need to say that the diffusion of AI in all
application domains will also bring an ideal shift in the way we do research and
development in agriculture now. AI moves towards more automation with more
accuracy to perform on real-time management, which is helping in standard shifting
of traditional agriculture to precision agriculture with low cost. The AI solution must
be viable and accessible to the farming community. With the advent of technology
and interference of AI, there has been a dramatic transformation in the capacity of
production and reduction in the cost of production and drudgery. The diffusion of AI
in all application domains facilitated the, paradigm shift in the way we do research
and development in agriculture now (Saxena et al. 2020). IT boosts automation with
more accuracy to perform on real-time management, which is helping in standard
shifting of traditional agriculture to precision agriculture with low cost. The solution
of AI must be economically viable and easily accessible to the farming community.
AI solutions should offer an open-source platform for faster adaptation and greater
22 A. Kumar et al.

insight among the farmers by making its solutions more affordable. It will be a
powerful tool that can help organizations cope with the increasing amount of
complexity in modern agriculture. It is high time that big companies invest in this
space (Wipro 2019). AI cannot replace the knowledge of farmers but in near future
definitely, it will edify their knowledge. AI will complement and also challenge the
way decisions are made and facilitate to improve farming practices. Such techno-
logical interventions are likely to lead to better agricultural practices, yields, and
qualitatively improve the lives of farmers.

1.15 Conclusion

After reviewing the available tools and techniques it can be safely said that despite
many odds the irrigation sector has evolved several good technologies which can
revolutionize water management on future farms. It is needless to say that not only
India but the whole world is experiencing a rapid population growth and constantly
enhancing requirements for water and all such commodities which use huge amounts
of water for their production. The need for becoming water smart, energy smart, and
climate smart is the order of the day. Without such interventions there is absolutely
no future for mankind, especially due to uneven distribution of the water globally,
regionally as well as locally. Technological solutions are being evolved ever since
man learnt to use wheels or advancement of science and technology set in. Many
modern and futuristic solutions have been suggested in the chapter which are reality,
viable, and feasible. At the same time the innovators and developers are required to
develop and propagate such technologies which are affordable, effective, and path
breaking. Overall objective of all efforts combined should be sustainable resource
management for averting any future catastrophists and well-being of the human race.

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Chapter 2
Smart Technologies in Livestock Farming

Amandeep Singh, Y. S. Jadoun, Parkash Singh Brar, and Gurpreet Kour

Abstract Smart technologies and its application have shown great promise for the
modernization of extension services in both developed and developing countries.
Improving rural livelihoods through smart technologies is one of the key areas,
which has potential to change the livestock economy. Enormous increase in the
mobile and internet users has ushered in a revolution in ICT research and develop-
ment. We wouldn’t be wrong if we dubbed this period the “ICT Era”. The govern-
ment of India’s Digital India Mission, as well as telecom providers’ provision of
affordable pricing to subscribers, have cleared the road for internet technology to
reach everyone’s doorstep. Many public and private organizations involved in
research related to the livestock sector have developed many such ICTs for the use
of livestock farmers. Improved package of practices are being provided to the
farmers by the use of mobile apps, expert systems, and web portals whereas the
regular advisories are provided to them through tele-services, SMS and Remote
Sensing based tools. The animals are being identified by the use of RFID tags which
are helping livestock farmers as well as the resource-based companies for resource
disposal. Furthermore, the farmers are connected to peers through social media and
mobile telephony like Kisan Call Centre. The new buzzword, i.e. artificial intelli-
gence (AI) through its diverse applications has the potential to revolutionize the
livestock industry, like; artificial neural networks, deep learning, machine learning,
natural language processing, cloud computing, block chain technology, internet of
things, precision farming, sensor based systems, robotics, and so forth. It is also
predicted that AI will lead in the world’s “fourth industrial revolution”. which will be
a digital revolution. All of these technologies work together to create an “Informa-
tion Web” for farmers, which is in charge of disseminating timely livestock devel-
opment information. This chapter details the ICTs which are in use by the livestock
farmers and the ones which are yet to come.

Keywords Artificial intelligence · Farmers · ICTs · Information · Internet ·

Livestock

A. Singh (*) · Y. S. Jadoun · P. S. Brar · G. Kour

Guru Angad Dev Veterinary & Animal Sciences University, Ludhiana, Punjab, India

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 25
S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies,
https://doi.org/10.1007/978-981-19-1746-2_2
26 A. Singh et al.

2.1 Introduction

Livestock resources are very important for a developing country like India. In order
to make India a worldwide leader in animal husbandry, it is necessary to amalgamate
it with developments in other fields. Information Technology (IT) based smart
technologies have been used for the study and development of livestock production
systems, teaching, research and field extension activities. In developed countries ICT
is being effectively used for the sustainable livestock management, livestock disease
control, precision livestock farming, and diagnosis and treatment through IT appli-
cations and technologies. IT has a great role in dissemination of livestock informa-
tion, technologies, and indigenous technical knowledge to the end users. In the
present scenario the mobile phones looks like tomorrow’s most liable right to use
device for information dissemination to the end users. Information related to live-
stock, e.g. alerts related to vaccination can be disseminated before monsoon through
mobile phones. Despite all constraints under Indian conditions, the smart technolo-
gies are spreading at its own pace and in future the process will speed up. It seems
that in the near future there will be a sound platform for livestock-based technology
dissemination in rural areas in particular and in urban areas in general through the
smart technologies-based applications and devices and value-added services. The
utilization of ICT application has the potential to develop livestock and agricultural
farmers in India (Sasidhar and Sharma 2006). The need-based, location-specific, and
local language content presented in the form of computer software is the need of the
hour for the livestock sector along with other e-material with regard to disease
control, herd management, production and marketing of livestock, and livestock
produce (Tiwari et al. 2010). The development of crop/livestock production systems,
as well as increased market demand for animal-based products, are driving up the
need for ICTs in emerging countries (Morton and Matthewman 1996). With the
ability to smoothen the information communication process among farming com-
munities, the mobile phone in comparison to other ICT tools has turned out to be one
of the widely accepted instruments covering almost the entire world (Hayrol et al.
2009). In India, next to the radio and television, mobile phone users are fast
expanding, particularly in villages, creating a platform for information transmission
through services such as short message service (SMS). The mobile phone shows a
very promising role for information dissemination in coming years.
Artificial intelligence (AI) has increased the usability of electronic media and
sparked a technological revolution in practically every field where it is being used.
Since 2017, massive growth has been witnessed in the application of AI. The
application of artificial intelligence-mediated ICTs in the livestock industry is no
exception. Massive increases in mobile and internet users have sparked a revolution
in artificial intelligence-based ICT research and development. The government of
India’s Digital India Mission, as well as telecom providers’ offering of affordable
pricing to users, has prepared the path for internet technology to reach everyone’s
doorstep, boosting the internet of things (IoTs). The world population is growing and
similarly the farmers are using smarter tools to optimize utilization of land, water,
2 Smart Technologies in Livestock Farming 27

and other resources in agriculture to meet the global food demand. The adoption of
AI technology has witnessed an upward trend among various industries and agri-
culture is no exception to it with drones, robots, and intelligent monitoring systems.
In India’s Agriculture and Allied Sectors, AI currently accounts for only 5% of
the total, but it is expected to double by 2030. AI can be used in a variety of ways for
farmers, including the development of learning simulators for farmers who wish to
pursue livestock farming, the development of algorithms to determine livestock
production, the development of algorithms to understand mortality and disease
losses, development of intelligent expert systems, and so on. Although AI has
both advantages and disadvantages, it is also true that robots cannot replace people.
Humans are endowed with the ability to be creative, which robots will never possess
(Singh 2019).
Greenhouse gas emissions primarily lead to global climate change that leads to
global warming (Pearce et al. 2014). The livestock sector contributes 14.5% of GHG
emissions worldwide, and thus may elevate degradation of land, air, and water along
with reduction in biodiversity (Gerber et al. 2015). Consequently, the climate change
will affect livestock production through affecting the quantity and quality of feeds,
disease outbreaks, heat stress, and loss of biodiversity while the expected demand for
livestock products will increase by 100% by mid of the twenty-first century (Garnett
2009). Therefore, to prevent environmental degradation and to optimize livestock
production, there are certain smart techniques and technologies which shall be
followed by the farmers. These technologies are also detailed in this chapter. On
the whole, this chapter exhaustively details all the smart technologies in livestock
production.

2.2 Novel Terms

There are some novel terms related to information and communication technologies
which the readers must understand. Few of them are explained by Singh et al.
(2021b) as follows:
• Agvocacy: The word itself is an amalgamation of advocacy and agriculture. It is
the positive encouragement of the agriculture industry. When taking part in
agvocacy, you are telling your agriculture story. This provides genuine answers
to those outside our industry about the product safety, humane treatment of the
animals, the reasonable sustainability of the farm practices, and many more
queries. In the absence of agvocacy, the extremist groups can manipulate the
truth of the agriculture industry and the people practicing it.
• Felfies: or Farm Selfies, is the newest edition to self-portraits where people post
pictures showing smiling faces with cattle, fields, and farm equipment in the
background.
• Millennials: Also known as Generation Y or Gen Y, the millennials are the
demographic cohort being the successors of Generation X and predecessors of
28 A. Singh et al.

Generation Z. These digital natives are known for their extensive use and
acquaintance with the elaborate digital services like the internet, social media,
and the mobile devices. People in the developing world grew more educated
between the 1990s and the 2010s, a factor that aided economic growth in many
countries.
• Social media readiness: The degree to which an organization is prepared for
elements relating not only to technology but also to organizational and business
imperatives, as well as the ability to successfully implement social media
activities.
• Artificial Intelligence (AI) is typically defined as the science of programming
computers to perform tasks that would normally be done requiring human
intelligence.
• Machine learning (ML) is the procedure by which an AI performs artificial
intelligence activities using algorithms.
• Deep learning (DL) is a sort of machine learning that uses numerous layers of
processing to retrieve progressively increasing level features from data. It is based
on artificial neural networks.
• Internet of Things (IoT) can be defined as a network of physical devices and
other systems that are embedded with sensors and electronics, allowing them to
communicate and connect.
• Cloud computing is a system that allows for worldwide access to shared
reservoirs of configurable system resources that can be deployed quickly and
with minimal administrative effort, often using the internet.

2.3 Thematic Areas of Information Technology (IT) Use

in Livestock Farm Management

• Breeding: This contains information on the genetic stock of the animal and all
facets related to animal rearing related to pregnancies, vaccinations, and diseases
(Hayes et al. 1998).
• Individual records: For keeping track of pictures and body measurements over
time, with assistance of electronic identification devices (Stubbs and Ross 1985).
• Herd: It shows information about various species/categories of animals on the
farm like male/female, milking/dry, adult/heifer/calf, etc.
• Selection/culling: When an animal is bought, sold, or transported to another
farm, an entry is created automatically.
• Feeding: Feeding details of individual animals, concentrate feeding,
compounding of feed, balancing of feed, rationing using various feed ingredients,
etc. can be done (Nath et al. 2002).
• Milking: Individual production records, milk disposal. ICTs have become impor-
tant tools in Africa and other developing countries for monitoring livestock
production.
2 Smart Technologies in Livestock Farming 29

• Health: Complete individual/group health records, including details on symp-

toms, drugs used, dose, route of administration, and so forth (Stubbs and Ross
1985).
• Task Reminder: Any planned activities, such as the projected delivery date,
veterinarian services, stall cleaning, and so on, should be communicated to you
ahead of time (Giovannini et al. 2003).
• Finance: Income and costs are broken down into several categories, and balance
sheets can be generated (Paterson et al. 2000; Murray and Sischo 2007).
• Integration with newer technologies: In order to boost farm productivity, herd
management software is a must if the farm manager desires to incorporate
contemporary technology such as Radio Frequency Identification Devices
(RFID), revolutionary machine milking, and so on.

2.4 ICT Applications

Information and communications technology (ICT) is used in most of the fields such
as e-commerce, e-governance, banking, agriculture, education, medicine, defence,
transport, etc. The use of ICTs in the livestock sector dates back to the relay of
livestock related programmes on radio and television (Singh et al. 2021a).

2.4.1 Radio Frequency Identification Device (RFID)

Technology

RFID is the abbreviation for “radio frequency identification”. As the name indicates,
digital data is used by this technology which is read through radio waves after
encoding. RFID systems consist of three parts: an RFID tag, an RFID reader, and an
antenna. An RFID tag in turn consists of two parts: an integrated circuit and an
antenna, which transmits the data to the RFID reader. The RFID reader then works
for the conversion of the radio waves into a practical form of data which is
transferred to a main computer system through a communications interface, for
storage and analysis of the data (Singh et al. 2021b). The RFID tags on the packages
of agricultural products allows the farmers to ascertain the quality of the product,
which further makes it convenient for processing companies to simultaneously add
valuable information on the tag, like processing date, batch processing, enterprise
codes, and package weight. RFID has also been used in the livestock sector and the
examples are detailed in Table 2.1.
30 A. Singh et al.

Table 2.1 RFID based technologies adopted by farms in India

Organization Start date Operational area Benefits
IIT Delhi and NDRI, Karnal July 2008 NDRI, Karnal • The temperature and
humidity sensor based mist
controller and water trough to
control water flow based ani-
mal proximity sensor have
been put in a cattle yard for
testing by IIT Delhi. Models
are being developed to ana-
lyse the animal behaviour.
Institute of Financial Manage- April 2009 Thanjavur, • The online policy disbursal
ment and Research (IFMR), Tamil Nadu on the same day for covering
Dairy Network Enterprise the farmer without delay once
(DNE) and Ergo-HDFC GIC the insurance policy has been
Ltd. purchased.
• Low premium due to reduc-
tion in mortality rate by inte-
grating the insurance schemes
vaccination and deworming of
animals.
• Better animal health man-
agement by timely execution
of protocol based veterinary
and animal husbandry
services.
• Initial economic study
revealed that the investment
cost would be recouped even
if only 0.5% of insured ani-
mals’ false claims could be
avoided. Cheating by farmers
is difficult due to inbuilt
mechanism in the system.
ITGI (IFFCO-Tokio General August Gujarat, Maha- • Using RFID technology
Insurance Co. Ltd.) Pasudhan 2009 rashtra, Punjab, enabled IFFCO-Tokio to
Bima Rajasthan, and change the current tagging
Odisha process and brought a check
on insuring non-existent or
sick cattle.
• RFID has helped in correct
identification of the animal.
• Current claim settlement
time is around 10 days which
is much lesser than earlier
time of 30 days to issue
insurance policy.
• Reduction in claims.
(continued)
2 Smart Technologies in Livestock Farming 31

Table 2.1 (continued)

Organization Start date Operational area Benefits
Lakshya Dairy, Jind, Haryana November Jind, Haryana • Record keeping on pedigree,
2010 production, reproduction,
feed, health, and costs.
• To produce a stronger future
herd, sound decision-making
in the selection of animals
with increased genetic pro-
ducing ability is required.
Sangamner Milk Union, 2011 Maharashtra • Performance, in terms of
Maharashtra enhancement of productivity,
can be calculated
• It’s easier to keep track of
information about the services
they provide and their insights
about an animal’s health, than
it was before, even with man-
ually handwritten reports.
Gauseva and Gauchar Vikas August Gujarat • For improved cattle breed-
Board (GGVB), Gujarat 2017 ing, disease management,
trade, and food safety, every
detail of a cow’s history may
be recorded and retrieved, and
the animal and its products
can be traced back to their
exact origin.
• An RFID-enabled mobile
computer may write informa-
tion such as the animal’s date
of birth, breed, milk yield, and
owner’s name on it.

2.4.2 Mobile Phone Technology

Cellular communication is carried out using mobile technologies. Over the last few
years, mobile technology has advanced at a breakneck pace. A conventional mobile
device has evolved from a simple two-way pager to a mobile phone, GPS navigation
device, embedded web browser and instant messaging application, and a portable
gaming console since the turn of the millennium. There were 4.66 billion active
internet users globally in January 2021 (59.5% of the global population). 92.6%
(4.32 billion) of this total, used mobile devices to access the internet. Mobile phones
are used for sending SMS, for calls, for using different applications, and for using
social media for the input and output of information. The few applications of mobile
phone technology are detailed below (Singh 2019).
32 A. Singh et al.

2.4.3 Mobile Apps

Mobile apps are proving to be very handy tools for need-based information dissem-
ination among livestock farmers. Various national and regional institutions are
developing mobile apps on various aspects of livestock farming in regional lan-
guages. The package of practices can be easily disseminated using mobile apps with
high interactivity. Mobile apps provide a provision for content developers to dis-
seminate information in the form of images, text, graphics, videos, etc. (Singh et al.
2021a). The commonly used mobile apps have been listed in Table 2.2.

2.4.4 Extension Advisory and Social Media

The effectiveness of Extension and Advisory Service (EAS) agencies can be ascer-
tain by the evidence of its contribution towards strengthening and adapting the
innovation networks to the extreme events that impact agricultural production and
productivity. EAS agencies which provide farmers with information, knowledge,
training, and other resources which are necessary for sustaining rural livelihoods.
These agencies belong to multiple sectors and facilitate farmers with latest produc-
tion and marketing skills. Extension advisory and social media are used for knowl-
edge dissemination (creation of different groups and pages on social media for
dissemination of information to farmers), direct marketing, for peer-to-peer network-
ing as well as using farmer friendly platforms for online demonstrations (Singh et al.
2021a).

2.4.5 Pashu Palak Tele-Advisory Kendra (PP-TAK)

PPTAK is NABARD sponsored project earned by GADVASU, Ludhiana. It is the

first of its kind initiative for livestock farmers. Advisory and information through
tele-communications will establish a strong linkage between the farmers and the
University all around Punjab. The call centre facility of GADVASU will reach the
information deficit areas, thus providing an impulse to livestock farming. The centre
will be linked to a Mobile App which will further increase its overall utility. Farmers
can call on phone numbers 62832-58834 and 62832-97919 for query redressal and
advisories on livestock farming.
2 Smart Technologies in Livestock Farming 33

Table 2.2 List of mobile apps developed by various institutions for farmers
Launched Developed
Name of the App on by Use
m-Kisan July 2013 NIC, GoI m-Kisan is a mobile-based extension
service that aims to give resource-poor
farmers with information on crops,
livestock, market pricing, and weather-
based alerts.
IVRI-Pashu Prajanan App 2017 ICAR- The major reproductive diseases/disor-
IVRI ders covered in the App are Anoestrus,
Repeat Breeding, Silent Estrus, Uterine
Torsion, Dystocia, Abortion, Uterine
Prolapse, Retention of Foetal Mem-
branes, Metritis, Brucellosis,
Campylobacteriosis and IBR - IPV.
The App additionally provides basic
information on Artificial Insemination
in cattle and buffaloes. The App is
presently available in Hindi, English,
Punjabi, Assamese, Bengali, Gujarati,
Tamil, and Malayalam languages.
IVRI-Shookar Palan App 2018 ICAR- This app provides information about
IVRI commercial pig farming. The app con-
sists of model bankable projects for
ease of the farmers to start the enter-
prise. The app is currently launched in
Hindi. The Punjabi and English version
of the app is being developed.
IVRI-Artificial Insemina- 2018 ICAR- This app provides information on arti-
tion App IVRI ficial insemination in case of cattle and
buffaloes. The app is developed in
English. The app contains a linked
software for record keeping and links to
watch instructional videos.
IVRI-Waste Management 2019 ICAR- The app provides comprehensive
Guide App IVRI information about management of
waste originating from agricultural,
livestock and household activities.
Information Network for – NDDB NDDB has created the Information
Animal Productivity & Network for Animal Productivity and
Health (INAPH) Health (INAPH), a Desktop/Netbook/
Android Tablet based field IT
programme that allows for the collec-
tion of real-time, accurate data on
breeding, nutrition, and health services
delivered to farmers’ doorsteps. This
system allows the implementing and
monitoring bodies to monitor the pro-
ject on a real-time basis.
Pashu Poshan App 2015 NDDB This app can be used on phones and
tablets and is based on the Android
(continued)
34 A. Singh et al.

Table 2.2 (continued)

Launched Developed
Name of the App on by Use
operating system. With the help of this
software, a balanced ration is formu-
lated while optimizing the cost, taking
into account the animal’s profile, such
as cattle or buffalo, age, milk produc-
tion, milk fat, and feeding regime,
among other factors. The livestock
farmers are also provided with advi-
sories to use locally available resources
for feeding their animals.
NDDB AGR 2016 NDDB An easy and interactive way to under-
stand Good Animal Management Prac-
tices and Clean Milk Production. Learn
how to handle milk hygienically. The
app showcases Good Animal Hus-
bandry practices that are easy to imbibe
and follow. It showcases practices on
clean milk production, Ration
Balancing Programme, Green Fodder
and also captures health-related aspects.

2.4.6 Other Initiatives for Connecting with Farmers

Other than the above-mentioned initiatives, various platforms like teleconferencing,

videoconferencing, satellite phones, and emails are used for connecting to the
farmers (Singh 2019).

2.4.7 Information Systems

Information systems are the integrated software and hardware systems connected so
as to provide information regarding a particular activity or set of activities. Infor-
mation systems provide need-based information to the farmers and other
stakeholders.

2.4.7.1 E-Choupal

Started in the year 2000, e-Choupal covered a target of 38,000 villages, 6500 kiosks
in nine states. Indian Tobacco Company (ITC) Limited started e-Choupal to link the
farmers directly through the internet for purchasing agricultural and aquaculture
products. The computers were installed in rural areas of the country to provide
farmers with up-to-date marketing and agricultural information under this project.
2 Smart Technologies in Livestock Farming 35

2.4.7.2 WMSDP (Web Module for Scientific Dairy Practices)

Sher-E-Kashmir University of Agricultural Sciences and Technology in Jammu

established a Web Module for Scientific Dairy Practices to disseminate need-based
information about scientific dairy practices to dairy producers. This information
system was developed in the English language and contains information on scientific
housing, feeding, breeding, healthcare management, etc. Microsoft dot (.) net tech-
nology was used to develop this information system. WMSDP and other ICT
technologies can be a great medium for disseminating needed information to farmers
(Singh et al. 2020a, b).

2.4.7.3 BroiLearn

Web module on Broiler Farming (BroiLearn) was developed by Sher-E-Kashmir

University of Agricultural Sciences and Technology of Jammu, and consists of
comprehensive information on scientific broiler farming covering important aspects
like breeds of chicken, poultry housing, nutrition, brooding, diseases, farm equip-
ment, and recent trends in broiler farming like organic broiler farming, contract
farming, institutional finances for poultry entrepreneurship development, poultry
waste management, etc.

2.4.8 Expert Systems

The expert systems are almost similar to information systems but are developed for a
specified purpose like milk fat analysis, disease prediction, etc. The programming in
expert systems is more rigorous as compared to information systems as these are
more result oriented. Few examples of expert systems are shown in Table 2.3.

2.4.9 Web Portals and Websites

Web portals provide two-way communication whereas one-way communication is

the feature of a website. Few examples of portals are Agritech Portal by TNAU,
Coimbatore, Vet Extension, Information Network for Animal Productivity and
Health (an application that allows for the collection of real time, accurate data on
breeding, feeding, and health services at the farmer’s doorstep), etc. Table 2.4 lists
the web portals developed for the farmers along with their benefits and Table 2.5
highlights the Educational software developed by ICAR-IVRI.
36 A. Singh et al.

Table 2.3 List of expert systems developed for the benefit of livestock farmers
Start Operational
Name of the system date Developed by area Benefits
Automatic Milk 1996 National Gujarat • Facilitate easy and fast pay-
Collection Unit Sys- Dairy Devel- ment for the milk delivered.
tems (AMCUS) opment Board • Information on fat content,
quality of milk and payment to
farmers is also provided.
• The testing of milk takes place
within 2–3 h after milk
collection.
• The card reader unit of this
system enables the users fast
speed of operation and error-free
entry of the data.
National Animal 2011 NIVEDI For the • About 13 priority diseases has
Disease Referral whole been identified by ICAR—
Expert System country NIVEDI based on the past inci-
(NADRES) dence patterns and built a strong
database of these diseases. This
database forms the basis of
NADRES which further pro-
vides monthly livestock disease
forewarning. Based on this fore-
warning, alerts are sent to animal
husbandry departments, both at
the national/state level, to take
appropriate control measures for
livestock diseases.

2.4.10 Educational CDs

There are many educational CDs developed by ICAR-Indian Veterinary Research

Institute (IVRI). “Health Information System” is a CD in Marathi language which
includes detailed information related to important diseases of the dairy animals.
“Digital Pashuswasthya-avum-Pashupalan-Prashnottri” is a well-understood answer
to the farmer dilemma, since it contains commonly asked questions (500) on animal
husbandry and veterinary science. Other CDs developed by ICAR-IVRI include
video CD on Scientific swine management (SSMV) in Hindi and English, video CD
on Integrated farming system (IFS) in English and Tamil, audio CDs on Livestock
diseases Part-I, Livestock Diseases Part-II and Neonatal Calf Management. Educa-
tional CDs on bovine reproduction are developed by GADVASU, Ludhiana.
2 Smart Technologies in Livestock Farming 37

Table 2.4 Web portals developed for farming community along with their benefits
Name of Start Operational
the portal date Developed by area Benefits
Agropedia January Government of All the Agropedia is an online knowledge
2009 India with assis- states of resource for agricultural informa-
tance from World country tion in India. It offers universal
Bank meta models and localized con-
tent for a wide range of users, as
well as collaboratively produced
interfaces in different languages.
It provides information on
improved animal husbandry and
fish farming practices.
mKisan 2013 C-DAC, NIC For the This portal was started along with
whole mKisan SMS service in order to
country register farmers for the same. For
subscribing to the SMS service
provided by mKisan farmers have
to register them in this portal.
Furthermore, it also registers
farmers for USSD, IVRS, KSewa,
and KCC. This portal also con-
tains the link to download apps
related to agriculture and allied
sectors in India.
Epashuhaat 2016 Department of For the The portal is simple to use
Agriculture, GoI whole because it does not require a login
country to view information. However,
prior registration is required for
every transaction. Sellers can cre-
ate an account, post animal
details, including photographs,
change those details, and offer
other important information, such
as their complete address, so that
buyers can contact them easily.

2.4.11 ICT-Based Models

2.4.11.1 NDDB: Next Generation AI Service Delivery Model

The National Project on Cattle and Buffalo Breeding (NPCBB) of the Government
of India has mandated the support to all initiatives connected to the extension of
Artificial Insemination (AI) delivery services. However, NDP I approved a pilot
concept for sustainable doorstep AI delivery services that followed Standard Oper-
ating Procedures and were provided by a professional service provider. It is antic-
ipated that the Pilot Project will become self-sustaining in 5 years and will continue
to provide AI services without the need for outside funding. By the end of NDP I, the
Pilot Project expects to have inducted around 3000 trained mobile AI Technicians
38 A. Singh et al.

Table 2.5 Educational software developed by ICAR-IVRI

Name Use
“Pashudhan-avum-Kukkut Rog Suchna This software is basically an information
Pranali” (PAKRSP)—An information system package and provides information regarding
for farmers in Hindi language. 78 most important diseases of livestock and
poultry in India. This system is made interac-
tive for farmers by incorporating animations,
photographs, and voice back up. The package
is provided to the farmers in the form of a CD
and provides information on animal diseases,
primary aid for most of the ailments, helps in
disease identification, its prevention, control,
etc.
“Livestock and Poultry Disease Information The information system is in English and is
System” (LPDIS)—An information system for housed on a CD that includes voice over, ani-
students and other stakeholders in the livestock mations, and photo and line illustrations. This
industry in English language. approach is useful for students and veterinary
professionals dealing with illness treatment in
cattle and poultry, including disease identifi-
cation, prevention, control, and timely
treatment.
Goat Health Management Information System This software can be purchased from
(GHMIS) ICAR-IVRI. Goat owners can learn about
numerous diseases that affect goats, as well as
how to differentiate between healthy and sick
goats along with goat vaccination and
deworming schedules. The technology is
supported with language-specific voice, text,
and high-quality images that help to convey
goat health information in an engaging
manner.

and completed approximately 4 million AIs in a financially self-sustaining manner.

All the activities were destined to operate through this model digitally (Singh 2019).

2.4.11.2 E-velanmai Model by Tamil Nadu Agricultural University

According to studies published in May, less than 20% of the innovations developed
by State Agricultural Universities and ICAR labs in India were transmitted to
farmers’ fields. “e-Velanmai” is an ICT-based model developed by Tamil Nadu
Agricultural University (TNAU) for timely dissemination of agricultural technolo-
gies. This experiment began in July 2007 and was carried out with the help of the
Tamil Nadu government (Singh 2019).
2 Smart Technologies in Livestock Farming 39

Table 2.6 Various projects undertaken by ISRO for farming community in India
Name of the project Start date Utility
National Agricultural Land Use 2004–2005 The information on net sown area is
Mapping critical for national planning and identi-
fying possible food security zones. Since
2004–2005, multi-temporal AWiFS
datasets have been used to give near
real-time net sown area of the country on
an annual basis at 1:250,000 scale.
Forecasting Agricultural output using 2007 Predicts crop acreage using Space,
Space, Agro-meteorology and Land Agro-meteorology and Land based
based observations (FASAL) observations.
Automatic Weather Stations (AWS) 2010 Weather forecasting.
and Doppler Weather Radars (DWR) Advisories to fishermen in coastal areas
Cyclones are forecasted
Internet based Dairy Geographical 2014 According to the Census of India, this
Information System (i-DGIS) by system provides locational and attribute
NDDB related information for around 5 lakh
villages out of approximately 6 lakh
inhabited villages in the country (which
includes all villages in the country’s key
milk producing States), as well as all
towns and cities. As people census,
livestock census, and land use/land
cover of the village are all integrated and
displayed in one place on the digital
map, i-DGIS can be utilized as a pow-
erful visualization tool for planning
operations in the operational region.

2.4.12 Satellite Broadcasting by Indian Space Research

Organization (ISRO)

ISRO is the premier organization which works for satellite communication in India.
ISRO has implemented various projects as shown in Table 2.6 for the farming
community of the country.

2.4.13 Remote Sensing and GIS Based Mapping

GIS stands for geographic information system, and it is a computer-based tool for
mapping and analysing objects and occurrences on the planet. GIS technology
combines typical database functions with maps, such as querying and statistical
analysis. Remote sensing, on the other hand, is the science of gathering data about an
object or an event without making direct touch with it. Both remote sensing and GIS
40 A. Singh et al.

based mapping can be used for livestock production and management in the country.
Fodder production, movement of vector, wildlife inhabitation, livestock waste
management, area under fodder crops, etc. can be analysed using features of remote
sensing and GIS, which will help in livestock related policy and planning (Singh and
Brar 2021).

2.5 Artificial Intelligence and Its Application in Livestock

Sector

In India’s agriculture and allied sectors, Artificial Intelligence (AI) now accounts for
only 5% of the total, but it is expected to quadruple by 2030. AI can be used in a
variety of ways for livestock farmers, including the development of handy tools for
disease identification, estimation of milk production, development of AI-based
information and expert systems, estimation of disease losses, development of learn-
ing simulators, etc. Although AI offers both advantages and disadvantages, it is also
true that robots cannot replace people. Humans are endowed with the ability to be
creative, which machines will never possess. This chapter examines the artificial
intelligence-based ICTs now in use in the livestock industry, with a focus on
technologies for animal products (Singh et al. 2021b).

2.5.1 Applications for Livestock Health

2.5.1.1 Livestock Disease Control

The “National Disease Control Information System” (NDCIS) of New Zealand,

according to Ryan and Wilson (1991), provides a database on important animal
diseases like tuberculosis and brucellosis. Contagious animal disease outbreaks,
according to Jalvingh et al. (1995) and Sanson et al. (1999), necessitate prompt
identification and elimination of all virus sources due to their monetary importance.
The use of computerized decision support systems (DSS) appears to have potential
for managing large amounts of data and assisting in the proper prioritization of tasks.

2.5.1.2 Programme for Monitoring Emerging Diseases (ProMED)

This is an International Society for Infectious Diseases (ISID) programme. Open to

all sources, the global electronic reporting system for outbreaks of promising
infectious illnesses and poisons.
2 Smart Technologies in Livestock Farming 41

2.5.1.3 Disease Monitoring and Surveillance

Animal disease monitoring refers to continuing efforts aimed at determining a

population’s health and illness status. The disease might be a specific infectious
disease or general health, and the monitoring activities include the routine recording,
analysis, and dissemination of disease or health-related information. Sickness sur-
veillance is a more active approach that suggests that if data indicates a disease level
above a given threshold, some type of guided action will be performed. International
Agencies involved in Disease Monitoring and Surveillance are WHO Statistical
Information System (WHOSIS), World Organization for Animal Health (OIE)
(Kivaria and Kapaga 2006).

2.5.1.4 Robotic Imaging

Penn State University’s veterinary college is the world’s first veterinary teaching
hospital to use the EQUIMAGINE robotics-controlled imaging system. This system
is eyed as better clinical and research advancement in animal as well as human health
(https://www.vet.upenn.edu/veterinary-hospitals/NBC-hospital/services/imaging/
robotic-imaging). Furthermore, robotics-controlled computed tomography
(CT) scans of various body parts are offered by New Bolton Centre. There are
several advantages of obtaining CT scan with New Bolton Center’s EQUIMAGINE
system viz.
• The patient is standing and awake without much sedation.
• Scans are obtained using sedation which decreases risk to the patient.
• Less time to obtain a scan, i.e. 30 s.
• High-quality images are obtained.
• Diseases can be diagnosed easily.
• Board-certified radiologists at the New Bolton Center interpret the scans and can
help with image acquisition.

2.5.1.5 Canine Patient Simulator

In the year 2010, the world’s first robotic dog simulator for training purposes was
developed which led to the establishment of a new simulation centre at Cornell’s
College of Veterinary Medicine. These pet simulators are cutting edge learning tools
which are used to teach students. These are in line with animal ethics and welfare.
Students can learn using these tools effectively without causing harm to the real
animal (Fletcher et al. 2012).
42 A. Singh et al.

2.5.1.6 Thermal Imaging Cameras

A thermal imaging camera is a handy tool for examining an animal body. These
cameras can be used quickly and are reliable non-contact methods. Animals need not
be sedated, need not to be touched and also there is almost zero exposure of the
animals to harmful radiations. A potential benefit of these cameras over the conven-
tionally used diagnostic aids is that these cameras provide real-time results right
away to the owners (Singh et al. 2021b).

2.5.1.7 Anti-Stress Ear Tag for Cattle

With robust, real-time animal state monitoring, the anti-stress ear tag enhances herd-
wide production providing an analysis of about 200 physiological parameters. It
helps in heat sensing and advice for the time of insemination. It helps in easy and
early detection of diseases. It provides insights on the body condition of the animals
and thereby advises the farmers for balancing the right nutrition for optimum body
condition of the animals. It also provides integrated herd management solutions
along with timely reporting.

2.5.1.8 Pig Respiratory Disease Package

This package consists of a microphone and a sound analyser connected with a

computer to analyse pig sounds. The microphone picks up any changes in the
pigs’ voices, coughing, or respiratory distress and sends it to the analyser. Any
sound that deviates from the norm is detected. It is useful for diagnosing diseases
7–10 days before they appear since it is quite effective at detecting even tiny changes
in pigs’ respiratory sounds.

2.5.2 Applications for Livestock Production

2.5.2.1 3D Cameras to Assess Beef Cattle

3D cameras have the potential to enhance livestock productivity. Generally, these

cameras are used to assess beef cattle. These cameras take multiple pictures which
are tested to form a convolutional neural network based algorithm. Based on the
algorithm results, the cameras assess the cattle based on body condition score (BCS).
Whenever the cattle has a high or low body score, the alert is made to the farmer
(Singh et al. 2021b).
2 Smart Technologies in Livestock Farming 43

2.5.2.2 Automatic Feed Manager

Automatic Feed Manager is a complex system based on sensors and predictive data
analytics. Sensors identify any changes in the batch of the feed manufactured and
alerts the manufacturer (Karn et al. 2019).

2.5.2.3 Robo-Cams for Poultry

Ground robots were first utilized in the University of Georgia’s (UGA) experiment
farmers to determine the practicality of using robots in poultry houses. The results of
this experiment revealed that robotic systems in flocks have no harmful impact on
the birds. The utilization of robot cams is feasible in poultry houses, however,
studies are underway for their automation in poultry houses (Poultry Tech 2016).

2.5.2.4 Virtual Fences for Controlling Cattle

Experiments have shown that cattle may be kept away from a place by using audio
and electrical stimulation applied remotely. Cattle learn this stimulation and move to
the other part of the pasture. Marsh (1999) proposed that the GPS can be used along
with electrical stimulation. The use of GPS technology to track the whereabouts of
wildlife is common. Marsh’s work to include bilateral stimulation, using separate
sound stimuli for each ear, has led to the better control of animal. The actual stimulus
used is a combination of audio tones and electric shocks.

2.5.2.5 The Dutch Cattle Expert System (veePRO)

The Dutch Cattle Expert System was developed by a Dutch organization named
Veepro and this expert system may prescribe feed diets, treatments, and livestock
health and welfare conditions. It also aids in animal reproduction by suggesting the
mating partners whose progeny can lead to better production results. This system
also keeps a proper record of individual and group of animals regarding their
production, health status, etc. and provides advisories for optimal production. The
expert system provides detailed recommendations on health measures to be adopted
in the farm to prevent diseases and maintain herd health. The system is efficient in
the development of tailor-made breeding programmes particular to the herd.
44 A. Singh et al.

2.5.3 Applications for Animal Reproduction

2.5.3.1 Smart Neck Collar

Smart collars have shown to be beneficial not only to health management but also to
fertility. The smart neck collars are sensor based equipment for recording various
physiological parameters. Based on the collected data, the analysis is done by a
computer to yield results. These neck collars are increasingly being used to detect the
animals in heat so that the timely insemination can be done.

2.5.3.2 Face Recognition Systems

Face recognition works on the principle of image analysis. The images pertaining to
patterns of spots on the animal body along with their actual face are analysed for
generating results. It takes a few seconds for the system to distinguish a certain
animal. These systems are helpful in keeping record of animal’s nutrition, health,
and breeding status. The information obtained using this system can be used by the
dairy farmers to increase or decrease the plane of nutrition, inseminate the animal,
and keep record of it. The software stores information related to animals and
provides farmers with necessary alerts.

2.5.3.3 Cow Gait Analyser or Pedometry

The present status of female fertility is an aspect based on multiple variables. These
variables may be based on herd health, nutrition, management, effect of climates,
reproduction status, etc. and aid in the cyclicity of the animal. The cyclic animals
show prompt estrus signs, whereas some show weak signs. However, pedometry is
based on the number of steps an animal walks a day. During estrus, the animals show
restlessness and walk more footsteps. Analysis of the footsteps to ascertain the estrus
behaviour of animals is pedometry. The cow gait analyser detects the animals in
estrus as they walk more footsteps during heat compared to other days.

2.5.3.4 Intelligent Dairy Assistant

Intelligent Dairy Assistant serves as an aide to the livestock farmers for management
of their dairy animals. It was developed by a Dutch company to track the movements
of the dairy animals. The system is AI-based and consists of motion sensors which
are wrapped around the neck of the animal to check its activity. It was launched in
the USA in 2017 after many trials in Europe. The data received from the sensors is
processed by a computer using AI to understand the behaviour of the animals in real
2 Smart Technologies in Livestock Farming 45

time. The processed data provides information regarding the productivity of the
animal and also the predictions can be made for the same.

2.5.3.5 MSUES Cattle Calculator

The MSUES Cattle Calculator app was developed by Mississippi State University’s
Extension and is beneficial for the users rearing beef cattle. A reproductive calculator
is included in the software for the calculation of breeding and calving periods.
Another calculator for evaluating animal performance is available, with modified
weight amounts for birth, weaning, and yearling primarily highlighted. The final
calculator helps managers make informed decisions on medicine dosages and other
health-related issues. The app is available for IOS operating systems and is free to
download.

2.5.4 Applications for Livestock Products

2.5.4.1 Robotic Milking Systems or Automatic Milking Systems (AMS)

The AMS have been developed for reducing the time management constraint in
dairy operations. This is based on the voluntary milking principle whereby a dairy
animal decides the time of milking and interval between milking on its own. An
automated milking unit comprises a milking machine, a sensor for teat position, a
robo-arm for placing and removing teat cups and a gate for controlling dairy animal
traffic. These systems are generally used in an open or extensive system of farm
management whereby the animals spend most of their time in grazing and resting.
When the cow feels that it should be milked, a cow tag sensor on the cow reads
the code and sends the same to the control system. If there is less interval between the
cow milking, then automatically the cow is sent out of the milking unit. The cow
entering the milking unit gets the teat cleaned by a robotic arm. Robotic arm fixes the
cups of milking machine on teats, milking takes place, post-milking spraying is also
done and the cow is let out of the milking unit by automated operations. Cows are
provided with concentrate feed after milking as a perk to get milked in the milking
unit. Robotic manipulation in the milking unit is core innovation of this system. The
activities of teat cleaning and milking attachment are automated by a robotic arm,
which eliminates the final aspects of physical labour from the milking process. This
system reduces human intervention and human touch during the complete milking
process.
46 A. Singh et al.

2.5.4.2 Robotic Hide Puller

Robotic hide puller signifies an era of automation in the meat industry. It is based on
increasing automation and reducing human touch. This is basically an instrument
used to remove animal hides after slaughter. The automatic hide puller has a stainless
steel stand with built-in apron washes, knives/whizzers, sterilizers, drip trays, and
drainage. The principle of reducing human contact enhances the quality due to
hygienic production which leads to clean meat production. This machine is made
of rust resistant GI steel. Furthermore, the motion and intelligence cameras analyse
the quality of the meat and assure that it is safe to consume.

2.5.4.3 Smart Packaging

AI-based packaging is replacing the conventional laser-based packaging. Cortex

system is an AI-based system for livestock products packaging which consists of a
camera with computer vision. The camera scans the products passing through the
conveyor belt and removes the faulty ones from the production line. Cortex can also
distinguish between different types of carton packing, such as gable-top and aseptic
cartons, distinguishing between almond milk and broth cartons. Cortex has learned
to recognize over 150 different carton types and is constantly learning new ones
(Ahmed et al. 2018).

2.5.4.4 E-Nose or E-Tongue

Electronic nose or electronic tongue comes under the ambit of electronic sensing or
e-sensing. It is a set of gas or chemical sensors that are embedded in an instrument
and work together to form a complete sense of taste, smell, and flavour. “Electronic
nose (e-nose)” consists of gas sensor arrays, whereas “electronic tongue (e-tongue)”
consists of chemical sensor arrays. Sensor arrays are typically used for quick
sensing, and their cost is less than that of traditional analytical equipment such as
a laser scattering analyser, gas chromatography–mass spectrometry (GC–MS), and
high-performance liquid chromatography (HPLC). Sensor arrays can be used to
determine a variety of food qualities with respect to microbial, sensory, and
processing (Matindoust et al. 2016). Sensor arrays are used in conjunction with
classification algorithms and data pattern recognition technologies to achieve these
applications. Artificial neural networks can be used to analyse the data collected by
these sensors in order to determine meat quality.
2 Smart Technologies in Livestock Farming 47

2.5.4.5 Meat Quality Evaluation using Computer Vision

Computer vision (CV) or imaging technology has gotten a lot of attention as a

non-destructive and quick way to measure the quality aspects of agricultural prod-
ucts, including meat and meat products, all over the world. The idea behind artificial
intelligence systems that retrieve information from images is known as computer
vision. The image data may be retrieved from images, videos, and any other source
of pictures or cameras. The pictures are thus analysed to come up with defects of
manufacturing, processing, or packaging. Computer vision, like ultrasonography,
provides details about the meat structure by detecting the reflected signature of the
medium’s interior structure. Traditional meat quality assessment methods, on the
other hand, have several drawbacks, such as being costly and time-consuming,
whereas computer vision is non-destructive and rapid which makes it a better
alternative for assessment of meat quality.

2.5.4.6 Bio-Sensing Technology

There are many health hazards which are related to the meat industry, viz. pathogens,
chemical residues, toxins, drugs, heavy metals, etc. To identify these hazards, there
is a requirement of precise and handy tools which can ensure food safety. Biosensors
are novel aids to ensure food safety which works on the principle of conversion of
chemical signals into electronic signals and thereby detecting a hazard in food
products. A bioreceptor recognizes the target hazard and emits an electronic signal
confirming its presence. These outcomes are presented in such a manner that they
can be easily understood by a user. Bio-sensing technology is easy, quick, and user-
friendly technology for meat quality assessment (Velusamy et al. 2010).

2.5.4.7 AI Based Meat Sorter

Meat was sorted by human touch until the end of the twentieth century in wealthy
countries, but this has recently changed to an AI-based approach. AI-based meat
sorter works on the principle of near-infrared spectroscopy, X-rays, LASER, and a
specific algorithm to analyse meat samples in contrast to the traditional meat sorting
machines. It has a special place in quality control of the product. The product which
does not meet the quality requirements are sorted in the initial stages which imparts
consumer preference to the product.

2.5.4.8 CNN Based Meat Identification

The convolutional neural network (CNN) is a deep learning technique which is used
frequently in the classification type of inputs. CNN learns from the input images and
48 A. Singh et al.

trains itself from a large dataset (Krizhevsky et al. 2017). In the meat industry
adulteration of superior meats with inferior quality meats and mixing of different
meats is an issue which can be resolved using CNN. It can be used for identification
of meat belonging to various animal species, fresh or spoiled meat, fat content of the
meat, and many more. The GoogleTM Brain team launched TensorFlow, an open-
source deep learning neural network software programme, in 2016 (Abadi et al.
2016) which can be used to create an effective CNN based application for assess-
ment of meat quality.

2.5.4.9 Ascertaining Carcass Quality or Classification

The meat industry relies on the production of lean meat, the carcass having more lean
meat is graded highly and fetches good returns. The quality of meat is ascertained at
the end of the slaughter process and largely relies on human touch and veterinary
inspection. This human involvement to ensure carcass quality can be reduced by
using convolutional neural networks which will ensure quality along with hygiene in
the slaughterhouses. Furthermore, computer vision can also be used for this purpose.

2.5.4.10 AI Based Cameras for Food Safety Compliance

In the meat industry, safe meat production is a major concern. A smallest of

contamination can lead to far reaching consequences. Although traditional
HACCP procedures have reduced meat contamination on a broader scale, account-
ability is called into question once a product leaves the factory and enters the retail or
food chain. AI-based cameras can be used at eateries, processing plants, restaurants
to ensure meat safety and hygiene. These cameras can detect whether the employees
are wearing proper safety suits along with tracking their movement and physiolog-
ical status. While detecting any indiscipline or anomaly, an alert can be issued to the
owner of the food joint or processing plant.

2.5.4.11 Intelligent Cleaning Systems

Keeping meat handling and manufacturing plants, slaughter houses, abattoirs, and
butcheries clean is a major concern. Most of the companies use automated cleaning
systems which are untouched by human hands. But the question arises, what if the
pieces of equipment or the machines as a whole are contaminated? Customers these
days have been enlightened, and they know that every automated process may not
guarantee a product to be safe for consumption. If we compare traditional cleaning
methods with the intelligent cleaning systems, it can be found that the former cannot
remove minute food particles which leads to pathogen build-up thus reducing the
quality of the product.
2 Smart Technologies in Livestock Farming 49

2.5.4.12 Development of Meat Products

Many meat products are available now, each with its own set of components,
production processes, and possibility of being purchased by people. Several hundred
meat products are produced at a time by one firm. When humans are involved, it is
always a gamble to keep the original flavour. Machine learning algorithms used by
AI play a crucial part in precisely adding ingredients to a meat product, as well as
managing temperature and processing conditions.

2.5.4.13 Meat Supply Chain Optimization

Neural network based algorithms can calculate the present supply and future demand
of the meat products. The marketing of products can be optimized by monitoring the
demand and supply of products. The perfect balance can be made between the
demand and supply which will lead to better customer experience and stable market
prices of the livestock products.

2.5.4.14 Marketing of Livestock Products

Information Technology (IT) is used by the National Dairy Development Board to

deliver profits to a large number of farmers which are involved in dairy sector. This
method has reduced the alteration of milk and prompted payment to the farmers. It is
because of transparency in milk marketing, the dairy sector in India has seen an
unparalleled growth (Sharma 2000). In India nearly 2500 computerized milk centres
are functional today (Kenneth 2001). “Warana Wired Village” project of Maharash-
tra is an existing computer network used for milk marketing of dairy cooperatives.

2.5.5 Applications for Animal Welfare

2.5.5.1 Robot Fish

A robot fish is an endeavour to replicate the original fish using modern day robotics.
It is a bionic robot which mimics the working of a living fish. The first research on
robot fish was published by Massachusetts Institute of Technology in 1989. Most of
the robot fish are designed so as to emulate living fish which use Body-caudal fin
(BCF) propulsion. The BCF robot fish is classified into three categories: Single Joint
(SJ), Multi-Joint (MJ), and smart material-based design. The improvement of robot
fish control and navigation is the most significant area of their research and devel-
opment, as it allows them to “communicate” with their environment, allowing them
to go along a certain course and respond to commands to make their “fins” flap. The
robot fish also serves as a companion robot for the fish lovers. Moreover, more
50 A. Singh et al.

precision models are being developed to orient the fisheries research on robot fishes
(Yu and Tan 2015).

2.5.5.2 Protection Assistant for Wildlife Security (PAWS)

PAWS (Protection Assistant for Wildlife Security) is an integrated module to

prevent poaching which fetches data from the previously poached areas and routes
and predict regions and routes where future poaching can take place. This module is
based on machine learning (Lemieux 2014).

2.5.5.3 Man’s Best Friend 2.0

A Beijing based start-up named Roobo has developed an artificially intelligent dog
named “Domgy”. The pet dog can walk around the house, remember the names and
faces of the family member, and greet them by their names using facial recognition
systems. It can sense and alert its own battery and maintenance systems. It can
integrate IoTs in the household and turn them on and off if asked so. This pet dog
provides companionship along with utility for the family members.

2.5.5.4 Minimizing Drug Testing on Animals

All the drugs rolled on for human use that need to be tested on animals has proved to
be a harsh reality. One big data analytics firm is working on a technique to use
artificially intelligent substitutes in place of real animal subjects. In silico Medicine
creates novel medications and investigates strategies to prevent ageing and disease.
Located in Baltimore, they employ computers to test clinical studies instead of real
animals or humans, using analytical and deep learning approaches. The conditions
are predicted using deep learning models and computer-based programmes provide
data as recorded after a human trial. The systems can produce good predictions
without the use of animals if given enough data, although traditional testing is still
required in some circumstances.

2.5.6 Applications for Livestock Statistics

There are many AI based software which are used for analyses of data and interpre-
tation of results. From disease diagnosis to computational genomics, the AI based
software have entered animal sciences and proved their utility. Few of the software
are enlisted below for reference.
2 Smart Technologies in Livestock Farming 51

• Vettel’s Diagnostic Software

• IBM’s Vet Computing Tool
• Sofie Cognitive Computing Tool
• Deep Mind for Record Keeping
• Deep Genomics

2.6 Smart Technologies for Climate Smart Livestock

Farming

Climate Smart Farming (CSF) emphasizes on sustaining the farming by creating

resilience in the practices through reorientation or transformation in the ambit of
climate change scenario. Climate change is a recent phenomenon which is affecting
all farming types globally. The ill-effects of climate change are evident through
surging temperatures, forest fires, flash floods, droughts, etc. Livestock being an
interwoven part of the ecological balance is also witnessing measurable effects of
climate change. Reduction in feed resource efficiency, outbreak of diseases, heat
stress, breeding problems, reduction in production attributes, etc. are the direct
measurable effects of climate change on livestock. Unavailability of feed and fodder
resources, shrinking grazing lands, production of greenhouse gases (GHGs), com-
petition for space with agriculture, etc. can be considered as indirect effects. There-
fore, for mitigating the effects of climate change on livestock production and
building resilience among the livestock, there is an imminent need to draft strategies
and develop technologies for Climate Smart Livestock Farming (CSLF). Although
all the technologies which are discussed in this chapter can be used for optimizing
livestock production and reducing GHG emissions, few being particular to the
concept are detailed below.

2.6.1 Nutritional Interventions

The production of the animal depends on the type of feed it consumes. Over and
under feeding should be avoided. Over feeding leads to heat loss and under feeding
results in decreased production. Economic feed processing techniques like wetting
of grasses, cropping and chopping of greens, grinding, and pelleting, use of urea-
molasses will reduce the energy loss in the digestion and decrease the heat loss for
maintenance of body temperature. Use of available green fodder during summer or
efficient use of non-conventional feed resources or newer feed resources will help to
negotiate the fodder scarcity produced due to adverse climatic conditions (Behera
et al. 2019). Diet modification, direct inhibitors, feed additives, propionate
enhancers, methane oxidizers, probiotics, defaunation, and hormones are some of
the nutritional approaches that can assist reduce methane production (Moss 1994).
52 A. Singh et al.

Dietary manipulation through increased green fodder decreases methane production

by 5.7%. Increasing concentration in the diet of animals helps in reducing methane
by 15–32% depending on the ratio of concentration in diet (Singhal and Mohini
2002). The CH4 produced from molasses-urea supplementation was found to be
8.7% (Srivastava and Garg 2002) and 21% from use of feed additive monensin
(De and Singh 2001). Improvements in feed efficiency and milk output can help the
dairy herd emit fewer greenhouse gases and require less land (Bell et al. 2011).
Highly digestible high-energy diets have been discovered to be an excellent form of
summer diet for helping animals’ maintain body temperature by reducing excess
heat. The animals are more comfortable when they are fed a low-fibre diet and have
access to cool drinking water. In a study, it was reported that heat stress was reduced
by a level of 18–20% when by-pass fat was fed to dairy animals. Feeding of
increased quantities of minerals and vitamins in diet have also been useful for the
livestock (Bell et al. 2011). Supplementing cows with 1.5–1.6% DM potassium and
0.5–0.6% DM sodium may help heat-stressed cows produce more milk. Antioxi-
dants including vitamin E, vitamin A, and selenium aid to reduce the effects of heat
stress by restoring oxidant equilibrium, resulting in better reproductive efficiency
and animal health (Behera et al. 2019).

2.6.2 Reproductive Interventions

Progesterone supplementation during early pregnancy has shown better results.

Exogenous progesterone administration during the summer season has resulted in
low heat stress which boosted fertility. The use of GnRH and PGF2 to synchronize
heat in dairy animals promotes fertility. Embryo transfer technology (ETT) is being
investigated as a possible technique for reducing the deleterious effects of heat stress
on cow reproduction.

2.6.3 Manure Management

In India, most of the animal manure is extensively used as fuel in the form of dry
dung cakes or spread in the field (Singh et al. 2020a, b). Animal waste including
manure accounts for more than 25 million tonnes of methane emission globally per
year. Better management of animal excreta through various interventions can reduce
the methane emission. The CH4 emission from subsurface applied manure can be
mitigated by using manure solids separation and anaerobic degradation
pre-treatment, which otherwise may be greater than that from surface applied
manure. Temperature, time of application, and storage duration all influence GHG
emissions from manure. Furthermore, manure contains remnants of some com-
pounds that are harmful to both humans and the environment. Furthermore, any ill
animal excretions may carry zoonotic diseases that are extremely hazardous to
2 Smart Technologies in Livestock Farming 53

humans and can survive in the soil for several days to weeks. Animal excretions and
effluents emitted by the livestock products and processing sectors contain active
substances that offer a larger threat to all environmental components. Manure
management can be improved by combining traditional management practices
with climate wise manure management (Singh and Rashid 2017). Recycling manure
is a key step in ensuring long-term animal waste management and reducing the
negative environmental impact of improper management. Biogas production from
animal dung has been an age-old tradition followed throughout India which is used
for cooking and lighting purposes (Henuk 2001). The decomposed slurry that is left
over is a wonderful supply of manure for agricultural fields since it includes 80%
carbon, 1.8% nitrogen, 1% phosphorus, and 0.9% potash, making it a great source of
humus and micronutrients for crops. Livestock dung has been utilized as an excellent
organic fertilizer for centuries. Animal droppings are a great fertilizer since they
contain all of the needed plant nutrients (Bell 2002). Because of its high nitrogen
concentration, poultry dung has been identified as the most desired of these natural
fertilizers (Sloan et al. 2008). Furthermore, a low-cost vermiculture system can
theoretically turn animal waste into vermin-cast and vermin-meal (protein meal)
(Singh and Rashid 2017).

2.6.4 Housing and Management Interventions

Good house ensures proper design, height, and orientation with good open and
covered space. Adequate ventilation and comfortable floor space per animal will
provide a cooler microenvironment inside the house. Proper housing ensures a
stress-free environment which leads to better productivity among livestock, thus
building resilience among them. While constructing animal houses, heat ameliora-
tive measures such as foggers, sprayers, drinkers, and shady areas should be properly
built. There are few recommendations which shall be taken into consideration before
constructing an animal house, viz. the long axis of the house shall have north-south
orientation with a height of 10–12 ft for dairy animals. The top of the house shall be
painted white to reflect as much radiation as it can. The trees shall be planted
surrounding the house. There shall be proper feeding and watering space for the
animals to avoid competition. The comfortable bedding and optimal floor space also
reduces heat stress among animals.

2.6.5 Precision Livestock Farming

The optimum utilization of resources in a farm can be obtained through precision

livestock farming (Tripathi and Bisen 2019) along with cutting direct and indirect
greenhouse gas emissions. Smart tags, drones, cameras, sensors, and computers are
54 A. Singh et al.

available for specific interventions in livestock management. The software based

digital tools cut the supply of inputs after sensing the threshold.

2.6.6 Using Digital Technologies

Digital technologies have multifarious role in building resilience and mitigation of

climate change in livestock production. The network of farmers can be created using
social media whereby timely information can be passed onto them which will also
strengthen extension and advisory services. Early weather forecasting systems can
help farmers to be ready with preventive measures after the passage of calamity.
Disease surveillance and monitoring systems can alert the farmers well before
disease outbreak so that the preventive and control measures can be strengthened.
Furthermore, livestock monitoring can be done using digital technologies like tags
and collars which helps in better management (Singh 2019).

2.6.7 Better Extension Advisory Services

Extension advisory services are the backbone of any livestock enterprise. Farmers
requiring any sort of information turn to an extension agent. Therefore, it becomes
mandatory for extension services throughout the world to focus on climate smart
aspects of farming. The new strategies should be discussed with the farmers and the
literature regarding the same shall be distributed. Focus group discussions, mass
media talks, popular articles in mass media, blogs on social media can be effective in
spreading knowledge about climate smart activities.

2.7 The Way Forward

Until now, extension workers have been manually conveying technology messages
to farmers. Due to a paucity of competent human resources, particularly in terms of
labour, the modern techniques have been limited to the academic and research
institutions. Even today, this gap in information dissemination is a roadblock for
extension workers. According to the findings, academics and extension profes-
sionals need to be educated on how to use better technology to communicate
knowledge and increase productivity in the livestock industry. Furthermore, scien-
tists must develop and disseminate field-relevant, profitable, and long-term tools and
approaches with the participation of farmers as research and extension partners in
order to effectively produce and transfer technical advances. The country’s rapidly
growing number of internet users demonstrates that ICTs can be a new paradigm for
dissemination of livestock related information. Digital illiteracy remains a barrier,
2 Smart Technologies in Livestock Farming 55

but this can be solved by providing livestock owners with need-based digital literacy
programmes. Improper telecom coverage is also a challenge which can either be
overcome by installation of cellular towers in the rural areas or by using satellite
phone technology. Furthermore, the technology developers require to assess the
information needs of the rural community and develop the technologies accordingly.
Capacity building of stakeholders and livestock producers is required for the devel-
opment and usage of ICTs, respectively. Modern protocols, such as AI-based tools,
remote sensing, and Geographical Information System (GIS)-based ICT tools, have
better prospects, but they must be evaluated for their cost. Utilization of ICTs for the
better good of the farming community can also lead to sustainable production and
environmental management. Further research should be focused on making the ICT
tools more interactive, user-friendly, and cost effective.

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Chapter 3
Prospects of Smart Aquaculture in Indian
Scenario: A New Horizon
in the Management of Aquaculture
Production Potential

B. K. Das, D. K. Meena, Akankshya Das, and A. K. Sahoo

Abstract As the scale and density of aquaculture operations have expanded,

overproduction in contemporary aquaculture has resulted in an unbalanced water
environment, increased fish disease outbreaks, and decreased aquatic product qual-
ity. The intelligent fish farm attempts to deal with the precise work of increasing
oxygen, optimising feeding, reducing disease incidences, and accurately harvesting
through the concept of “replacing man with machine” in order to completely free
human labour and complacency, as a result of a labour shortage and an urgent need
for innovation in aquaculture technologies. Thus, IoT adoption is increasing at an
alarming rate. IoT is currently widely employed in a wide range of industries and
applications. Take aquaculture, for instance, as an example of one of a number of
options. Traditional farmers struggle to keep up with changes in their cultural system
and the quality of their water. Cloud-based aquaculture monitoring and control
systems are built on model integration. Client data visualisations were part of a
system that included an open-ended smart sensor module for the management of the
system’s aeration as well as components for a local network and the cloud comput-
ing infrastructure. The smart sensor module gives us information about the water that
we use to monitor it. This high-tech sensor module has sensors for hydrogen
potential, dissolved oxygen, temperature, and level. For every type of aquaculture,
web and Android apps can assist you determine the ideal water temperature for your
pond. Additionally, in India, feed dispensing and sensors have been adopted
recently. Artificial neural networks and machine learning must be integrated in the
logarithm in order for the system to run smoothly AI and machine learning are
summarised below, along with their present state and challenges and prospects in the
field of smart aquaculture.

Keywords Artificial intelligence · Smart aquaculture · Machine learning ·

Robotics · Precision farming · Sustainable aquaculture production

B. K. Das (*) · D. K. Meena · A. Das · A. K. Sahoo

ICAR-Central Inland Fisheries Research Institute, Kolkata, India

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 59
S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies,
https://doi.org/10.1007/978-981-19-1746-2_3
60 B. K. Das et al.

3.1 Introduction

Aquaculture production in Asia accounts for 88.5% of global output. A record

82 million tonnes of fish were harvested from aquaculture throughout the world in
2018. With the increasing expansion of aquatic product output, traditional
manufacturing models have played a significant role (Li and Li 2020). As a result,
the quality and quantity of the world’s food supply are now regarded to be at risk due
to climate change (Hamdan et al. 2015; Myers et al. 2017). Climate change is posing
an increasing danger to food security, notably in terms of dietary protein (Kandu
2017). Aquaculture has evolved from a labour-intensive agricultural method to one
that is fully automated (Fore et al. 2018). Intelligent aquaculture is now conceivable
thanks to the rise of IoT, big data, AI, 5G networks, cloud computing, and robot
technologies. Modern aquaculture development has a new commercial model for
this. There are several elements to consider while assessing the water’s overall
quality, including its physical, chemical, and biological characteristics. As more
and more devices connect to the internet, IoT is becoming increasingly common.
Aquaculture is one of several businesses that have embraced the Internet of Things
(IoT). When it comes to keeping track of their cultural system and water quality,
traditional farmers have a difficult time keeping up. Based on model and cloud
integration, a real-time monitoring and control system for aquaculture has been
developed. A modular smart sensor module, a smart aeration system for system
control, a local network system, a cloud computing system, and client data visual-
isation were all part of this system’s functionality. Data from the smart sensor
module is used to keep tabs on the water’s health. Sensors for dissolved oxygen,
hydrogen potential, water temperature, and water level make up the smart sensor
module. Any sort of aquaculture pond may be set to the ideal water conditions using
a web and Android application. Feed dispensing and sensors, on the other hand, are
relatively new additions to the Indian market. Logarithms, machine learning, and
artificial neural networks must all be integrated if the system is to run smoothly and
accurately. Using robots and high-tech equipment, intelligent aquaculture is able to
complete the breeding and growing stages of farmed species, as well as the treatment
of circulating water and the accurate feeding of animals. Intelligent aerator systems
can operate the aerator, circulatory water treatment, and cleaning equipment based
on water quality, fish behaviours, and weather data. For healthy and speedy growth,
the intelligent feeder and deep learning consider biomass, water quality, the sur-
rounding environment, and the fish’s behaviour. The automated fish divider may be
used to gather and pool fish fry of various sizes and ages. This system’s purpose is to
ensure that the circulating water system is always operating at its peak efficiency.
With today’s advancements in aquaculture, a central command station, communi-
cation with each other as well as offering a comprehensive picture of an entire
facility are now very essential. An IoT platform device is used to create a smart pond
management system. Sensors for pond water temperature, pH, dissolved oxygen,
and water level are used in an IoT intelligent pond water system. Sensors might be
solar or non-solar. Fish and shrimp aquaculture are made simple with this system’s
3 Prospects of Smart Aquaculture in Indian Scenario: A New Horizon in. . . 61

24-h smart monitoring. As a result, farmers can rest and save money by keeping tabs
on the state of their pond culture from home using their smartphones or laptops. A
human–machine interface (HMI) display, remote control, cloud storage, and a big
data repository are all included in the system. Farmers may better grasp daily
changes by turning daily recording and management into a graphical presentation.
It is possible to employ an intelligent control system for a variety of different
types of equipment such as an aerator or feed pump, as well as water temperature and
salinity metres as well as pH and dissolved oxygen metres as well as water level
metres. The aquaculture pond’s sophisticated control system sends out alerts when it
identifies abnormalities. A microprocessor in the aerator regulates the flow of
dissolved oxygen, allowing it to switch on or off the power as needed.

3.2 Significant Components Smart Aquaculture

3.2.1 Collection of the Data

Collection of data utilising a variety of sensors, including temperature and humidity

gauges, CO2 monitors, light sensors, dissolved oxygen monitors, and other water
quality sensors, as well as cameras and other digital picture data gathering devices.

3.2.2 Data Communication

Using communication nodes to send the collected data to the command and control
centre. This data may include information about fish growth, environmental param-
eters, operation, and resource allocation.

3.2.3 Processing of Data

Data is processed and decisions are made using the cloud platform.

3.2.4 Execution

To accomplish long-term “high efficiency, high quality, ecological, health, and

intelligence” aquaculture, intelligent and automated operation execution, as well as
decision feedback to each execution device, is required.
62 B. K. Das et al.

3.3 Development of the Sensors

It is common practice for aquaculture farmers to use conventional methods and

practices. The aquaculture farm’s water quality, water level, oxygen level, and stress
level are measured and monitored manually by the farmer. An Internet of Things
(IoT) based smart aquaculture model was presented in this work that would monitor
water quality (pH, water level and temperature; turbidity; and fish motion detection)
for aquaculture. Wireless sensor network modules are used to monitor and manage
aquaculture in real time in this study. A water recycling system is also being
considered as a way to limit the amount of aquatic trash. This system continually
monitors water parameters through a serial port, reducing internet use, transmitting
data on a regular basis with minimal latency and error-free, and ensuring aquatic
life’s survival in the process. Increased aquaculture profitability is also a result of this
practice. An intelligent aquaculture system cannot function without sensors (Su et al.
2020). In recent years, the sensor sector has grown significantly. Sensors will be used
more frequently in breeding, adult fish growth, aquatic product storage and trans-
portation, aquatic product processing, operation, and maintenance as a result of
advancements in core sensor technology, modern information technology develop-
ment, rapid cloud technology development, big data platform construction, and
application and promotion enhancements. New sensors are being developed that
are more accurate, more versatile, more cost-effective, and more network-capable at
the same time that they are being developed. As new technologies in contemporary
physics such as nanotechnology, laser infrared ultrasound microwave optical fibre
strong magnets, radioactive isotopes, and integration technology continue to
develop, this has opened up new options for sensor integration (Sharma et al.
2019). On the other hand, micrometre-level sensing components, signal detection
circuits, as well as the CPU on a single silicon chip are merging to build
multifunctional compact portable sensors with a wide variety of applications as
well as high reliability and extended service life. Biosensing is another potential
future option for sensing technologies. Unmanned intelligent aquaculture production
is achievable with the development and implementation of novel sensors in all
aspects of aquaculture, including field monitoring, remote diagnostics, remote data
collecting and real-time operation (Jennifer 2017).

3.3.1 Sensor for Automatic Feed Dispensing

3.3.1.1 Automatic Feeding

It is particularly prevalent in big and intensive farms that require an almost constant
supply of feed to use automatic feeding systems. Because this is so time-consuming,
it is usually done by an automated feeder. According to Fig. 3.1, there are four key
parts of an automated feeder, namely the feeder hopper, feed distribution device,
3 Prospects of Smart Aquaculture in Indian Scenario: A New Horizon in. . . 63

Fig. 3.1 Automatic fish feeders on pond. (By USFWS Mountain Prairie is marked with CC PDM
1.0)

feed spreader, and power supply. Feeders can be placed on a rack or in a cage that is
buoyantly attached. The control unit may be programmed to distribute feed at
predetermined intervals based on the frequency and duration of feeding instructions
placed into it. A feeding system can range from a basic dispenser that does not
require power to a sophisticated computerised feeding system that regulates the food
based on the appetite of the fish computerised feeding system.

3.3.1.2 Central Feeding System

In cage farming in India, a central feeding system is a viable option. Feed is stored in
a feeder at the centre of the system. The feeder is responsible for delivering feed to
fish farms by pumping it via a series of pipelines (tanks, ponds, or cages). Silos,
sluice valve, water or air transport pipelines, selection valve, and distribution unit are
all part of this system.

3.3.2 Fish Catch Estimation

For the purpose of digitising India’s peninsular reservoir fish catch statistics on a
daily basis, the ICAR-Central Inland Fisheries Research Institute has developed an
64 B. K. Das et al.

android app-based Electronic Data Acquisition System (eDAS) that uses real-time
data from mobile phones to collect fish catch data in real time. India has 3.5 mil-
lion hectares of reservoirs, which are a major source of fish production and employ-
ment in the country. Data on the state of fish production, species composition, fish
variety, fishing effort, etc. are necessary for the development of management plans
for fisheries enhancement and the sustainable utilisation of reservoir resources.
There are a lot of water bodies with inaccessible fish landing centres that make it
difficult to collect data on fish harvest from reservoirs, making this type of research
expensive in terms of both time and money. Electronic Data Acquisition System
(eDAS) has been developed by the Institute to overcome data acquisition issues in
reservoirs and has been successfully trial-implemented in selected reservoirs in
India’s three states of India, i.e. Karnataka, Tamil Nadu, and Jharkhand.

3.4 Process Control and Machine Learning in Aquaculture

AI in the strictest sense is the future created from past pieces. We learn through trial
and error. Beginning with agriculture, AI has been implemented in a variety of
industries. The fishing sector can rapidly advance because the aquaculture sector is
less labour-intensive. For example, feeders, water quality, harvesting, processing,
etc. the implementation of artificial intelligence in conserving aquatic species global
fish tracking is aided by AI. AI helps considerably in IUD fishing. In aquaculture,
30% of inputs can be conserved via AI. Thus, AI can control fish production with
less upkeep and decreased input costs.
In a scenario where robots can think and act on their own, AI and the Internet of
Things have made this possible (IOT). It is a human simulator that is
pre-programmed with your cognitive ability. Nearly 50 billion electronic devices
are connected to the Internet today’s IoT. Artificial intelligence is now being applied
to agriculture and fishing. No part of the animal enclosure needs to be overlooked in
managing the facility. Experience enables it to learn more quickly and easily. As
environmental conditions change, this will aid in industry growth of the fisheries.
GIS aid not only to commercial fisheries, but also non-fishing open sea management.
Fish consumption has risen fourfold in the past decade. Aquaculture has increased in
demand while output has fallen. Using AI increases productivity and reduces labour
costs (Chrispin et al. 2020).

3.4.1 Artificial Intelligence and Management of Feeding

Feed accounts for nearly 60% of the total cost of an aquaculture system. In the
containment, too little or too much feeding can cause a variety of problems. On the
one hand, feeding less can lower muscle conversion and, in extreme cases (such as in
shrimps), can lead to cannibalism and mutual attack. Excessive feeding, on the other
3 Prospects of Smart Aquaculture in Indian Scenario: A New Horizon in. . . 65

hand, leads to waste and degrades water quality. Appetite measurement can assist in
feeding the correct amount of feed at the correct time. Through vibration-based
sensors and acoustic signals, AI plays a significant role in reading the fish. This will
help you distinguish between a hungry and a full fish. An AI feed dispenser
developed by eFishery, an Indonesian aquaculture intelligence company, releases
the right amount of feed at the right time. It detects the animal’s appetite using a
variety of sensors. The device can save you about 21% on feed costs. Observe
Technologies is a company that develops artificial intelligence (AI) and data
processing systems for measuring and tracking stock feeding patterns. It provides
objective and empirical guidance on the amount of feed that farmers should feed. A
smart fish feeder controlled by a remote is produced by an aquaculture technology
company known as “umitron cell” in Singapore and Japan. It is a data-driven
decision-making tool for farmers who want to optimise their feeding schedules.
This artificial intelligence (AI) feeding devices help to reduce feeding costs while
also maintaining water quality.

3.4.2 Artificial Intelligence and Drones Applications

in Aquaculture

Drones allow us to collect and analyse data like the cloudiness and temperature as
well as things like blood oxygen and heart rates and blood oxygen levels of fish and
vertebrate life cycles, and beat-depth information. Simply connecting a smartphone
to this drone makes it simple to retrieve this information and the development of the
technique of using robots that are stationed in the shallow water adjacent to a farm to
determine pollution levels prior to development as the robot shoaling approach was
initially conceived. These autonomous water vehicles are able to navigate across the
water to collect water quality information, Such long-wavelength microwaves, even
those with extremely low frequencies, can be employed for communication
purposes.

3.4.3 Artificial Intelligence and Disease Prevention

The biggest danger facing the fishing industry is the introduction of diseases. The
most successful systems are those that compare historical data with pre-programmed
information at the site to newly collected data to detect disease outbreaks. Also, they
are able to administer measures to avoid incidents before they occur. A cloud-based
programme called Aquacold launched in April of 2017, helping to protect both cage
and wild salmon farmers from developing sea lice, as well as open-ocean farmed
salmon from ocean lice, was in use. We were able to stop or even minimise the fish
mortality, without having to resort to the costlier treatments.
66 B. K. Das et al.

3.4.4 Artificial Intelligence and Fish Seed Screening Form

Culture Sites

Identifying and selecting a suitable variety of fish as good or bad food is critical in
fish farming. Because of the manual labour involved, it is difficult to employ a large
number of workers for seed screenings. The Underwater Agriculture Research,
Fisheries, and Forestry Research Institute (JAFRI) at Kindai University employs
Microsoft Azure ML Studio to sort out and destroy “non-shaped” seeds from the
rearing tank. In the Indian scenario it is imperative to develop and adopt such types
of devices for betterment of the aquaculture sector.

3.4.5 Smart Phone-Based Application in Aquaculture

Researchers are working on smartphone apps that use artificial intelligence to help
farmers in monitoring and track water quality and predict disease outbreaks. Farm
MOJO was a completely new mobile app created by “Aquaconnect” an Indian start-
up focused on aquaculture, to help shrimp farmers keep an eye on water quality and
predict diseases. Farmers may be able to stave off disease outbreaks long before the
start of the use of these applications. In order to remain as relevant as possible,
farmers and developers continuously upload photos of parasites and other maladies
of shrimp to the app on a regular basis. These images make it possible for the
programme to record information on the diseases to be retrieved and stored in future.

3.4.6 Artificial Intelligence and Real-Time Monitoring

of Stocks

The swimming pattern, size, injuries, and other characteristics of the cultured animal
can be analysed using vision-based sensors on AI devices. These records will be kept
in order to compare them in the future. “Xpertsea” is an aquaculture innovation
company that offers the “Xpercount” AI device, which uses machine learning and a
camera to weigh, count, image, and size shrimp in seconds. These data are analysed
in order to determine the stock’s periodic health.

3.4.7 Artificial Intelligence and Shrimp Culture

In the areas of real-time water quality monitoring and voice call alerts, appetite-
based intelligent feeders, and automatic aerator control, Eruvaka, an Indian com-
pany, provides AI-based solutions to shrimp farmers. Farmers in Surat, Goa, Andhra
3 Prospects of Smart Aquaculture in Indian Scenario: A New Horizon in. . . 67

Pradesh, and Pondicherry are benefiting from Eruvaka’s AI-based shrimp culture
solutions, which are now installed on 1000 hectares of shrimp farms.

3.4.8 Artificial Intelligence and Software Development

The general assumption about aquaculture is that it is trial and error base system. The
formulation of feed for fish species is initially a trial and error base method using
Pearson and least square methods then updated excel version came into existence,
however, some of the information such as cost, essential elements profiling, com-
plete biochemical composition of the formulated feed was not included. Similarly,
the estimation of carcass composition of fish and proximate composition of fish feed,
ingredients, etc., is time intensive by traditional methods. Keeping these limitations
in view, based on the facts from the Research and Developmental agencies, the
companies started working on it and could develop some important instruments and
software. For instance, for feed formulation, NACA, Thailand developed, and other
software are also available such as Win Feed, LOTUS, etc. Furthermore, instruments
such as DK-NMR are providing the sample analysis for extended biochemical
composition in both liquid and powder forms. The instrument is based on memory
of a particular simple that has been analysed and put into the memory so later it can
give analyses in fraction of time when receiving samples for the same nature.
However, these software and instruments are lacking system integration with
advanced AI and machine learning for better delivery of the inputs.

3.5 Precision Fish Farming

The aquaculture sector is highly scattered and the country is always facing natural
calamities. In addition, erratic rainfall and other factors sometimes create uncertainty
in viable aquaculture enterprises. Therefore, it is imperative to develop the prediction
and forecasting model for sustainable fish production and precision fish farming with
following objectives.
• Enhance the precision, repeatability, and accuracy of farming operations.
• Make it feasible to monitor biomass and animals in a more self-sufficient and
continuous manner.
• Improve the reliability of the decision-making process.
• Improve worker safety by reducing the reliance on physical labour and subjective
assessments.
The concept of potential fishing zone is catering the need for assessment of fish
stock and biomass availability. However, it has certain limitations in terms of bad
weather conditions, etc. some of the ICAR institutions like ICAR- Indian Statistical
Research Institute, New Delhi (ICAR-IASRI) are working on precision farming
68 B. K. Das et al.

models for agriculture and now they have started working on precision models for
precision fish farming using GIS coupled with AI applications. This will provide the
precision about the expected harvest and accordingly a management plan can be
fixed for managing the culture practices so that money and expenses could be
reduced up to a great extent. Thus, this technique would in turn enhance aquaculture
production thereby maximising the profit margin.

3.5.1 Climate Smart Fish Farming

As a result of the presence of poikilothermic animals, which are extremely sensitive

to various types of biotic and abiotic stress, freshwater aquaculture is under more
pressure from climate change than terrestrial agriculture. This means that the effects
of climate change on freshwater aquaculture are more complex than those on
terrestrial agriculture. Fish stocks can be directly affected by climate change or
ecosystems can be affected indirectly by changes in primary and secondary produc-
tivity, ecosystem structure, and composition. Fish prices and the cost of fish meal
and other goods and services can also be affected by changes in the price and
availability of these products and services. ICAR-CIFRI, Bararckpore is dedicated
to improving fisheries and aquaculture for the benefit of the local community. In
Thycattussery village, Alappuzha district, Kerala, a case study was conducted on the
black clam Villorita cyprinoides in climate resilient pens. Clam culture’s involve-
ment in decreasing carbon emissions by changing it into blue carbon is a new field of
development as a climate resilient technology. In addition, it makes use of the culture
system’s multitrophics to boost productivity and provide more money and a means
of subsistence. Bamboo and HDPE nets were used to construct a 114 square metre
experimental pen that was separated into two halves. The seeds gathered from the
wild (Paathiramannal) were divided into two categories: little clams (mean length of
15 mm and weight of 1.46 g) and giant clams (mean length of 22 mm and weight of
3.54 g). Clams of various sizes were kept in separate tanks, with a total of 5000 and
2000 shells per square metre, respectively. Over the course of a year, the enclosures
held 650 kg of young clams. An average clam’s weight and length increased by
14.23 mm and 47.98 g/year, respectively (Fig. 3.2). Female clam collectors assisted
in the deshelling process as part of the initiative. The sale of clam flesh and clam
shell brought in Rs. 26,300 for the community. Separation, washing, and sale of the
meat at Rs. 100/kg and the shell for Rs. 3500/tonne resulted in a profit of Rs. 100/kg.

3.5.2 Climate Smart Aquaculture (CSA)

To ensure food security, Climate Smart Aquaculture considers both adaptation and
mitigation. For climate change mitigation, adaptation, productivity, and economic
growth, CSA focuses on minimising potential negative trade-offs while maximising
3 Prospects of Smart Aquaculture in Indian Scenario: A New Horizon in. . . 69

Fig. 3.2 showing harvested black clam, Villorita cyprinoides in climate resilient pens. (Source:
ICAR 2022)

potential positive trade-offs. The following are the prime requirements of smart
aquaculture.
• Improving efficiency in the use of natural resources to produce fish and aquatic
foods.
• Maintaining the resilience of aquatic systems and the communities that rely on
them to allow the sector to continue contributing to sustainable development.
• Gaining an understanding of the ways to reduce effectively the vulnerability of
those most likely to be negatively impacted by climate change.
• Examples of tactics for attaining CSA objectives in respect to fisheries include:
the reduction of excess capacity and the implementation of fishing activities that
are linked with improved fisheries management and healthy stocks; increased
production efficiency through better integrated systems.
• Improved feeding and reduced losses from disease in aquaculture; the reduction
of postharvest and production losses; and the further development of regional
trade.
70 B. K. Das et al.

3.5.3 Nutrismart Fish Farming

3.5.3.1 Small Indigenous Fishes (SIFs) as an Alternate Livelihood

Option and for Better Health Status

A diet rich in micronutrients from small indigenous fish (SIFs) can help avoid
malnutrition in rural populations and provide a source of income (Fig. 3.3). Since
they favour a diet rich in SIFs, the SIFs command a premium price for marginal
fishermen who use traditional gear like traps, cast nets, and the like to supplement
their income. Health and nutrition programmes for women and children in the North-
Eastern states of India have previously been designated as a priority topic by national
policy makers and policy planners because of the difficulties in delivering health and
nutrition programmes due to insufficient infrastructure. Indian and international
research shows that tiny indigenous fish contribute greatly to consumers’ nutrition,
diet fortification, and the reduction of malnutrition. SIFs, such as Mola
(Amblypharyngodon mola), river shads (Gudusia chapra, G. variegata, and
Gonialosa manmina) and others (Fig. 3.4) might help ameliorate the status of
underweight prevalence, stunting and wasting in children, and low BMI and anaemia
in women in the North-Eastern area. It is possible to fulfil the nutritional needs of
women and children by promoting small-scale fisheries and SIF consumption in the
North-Eastern area, where fish is consumed by the majority of the population (95%).
Many researches have been done by ICAR-CIFRI on SIFs, and they have advised
the following dietary recommendations based on their findings. Global average
surface air temperature increases of 1.1–6.4  C by 2100 are expected to reduce
precipitation in most sub-tropical land areas and weaken tropical cyclones,
according to the Intergovernmental Panel on Climate Change (IPCC). Natural
catastrophes such as floods, cyclones, and even draughts are becoming more fre-
quent, and this is having an effect on agricultural productivity. In order to accom-
plish the SDGs, effective disaster preparedness must focus on health, nutrition, and
child safety. Investment in children’s, girls’, and women’s nutrition is a major

Nandus nandus Gudusia chapra Amblypharyngodon mola

Chandanama Puntius sophore Pethia conchonius

Fig. 3.3 Showing important Small Indigenous Fish (SIFs) species

3 Prospects of Smart Aquaculture in Indian Scenario: A New Horizon in. . . 71

Fig. 3.4 Schematic diagram of a bio floc technology system (Mugwanya et al. 2021)

development objective, according to the Government of India’s NITI Aayog’s

National Nutrition Strategy.

3.6 Internet of Things Technology

There are still numerous obstacles to overcome before aquaculture can completely
benefit from autonomous, intelligent, and high-precision farming. Due to the high-
risk nature of aquaculture practices, it is impossible to envision a world without any
human management in the near future. Smart technology such as micro- and
nanosensors to monitor fisheries data, bionic robots to operate production and
automatically check, intelligent sorting equipment, and energy-saving processing
equipment for aquatic goods would considerably automate and save labour at
various phases of aquaculture. The Internet of Things, or “cloud-network-edge-
end”, will link all of the equipment. The “collaborative collaboration” among the
equipment determines whether the autonomous operating ground can function
optimally under production settings in real-time, security, dependability, and preci-
sion (Martin 2019). “Network” technology for aquaculture IoT must be capable of
transmitting data according to this list. In the first place, the network is completely
unrestricted in terms of territory or time, meaning that all network equipment in the
unmanned farm can connect to it. Second, with centimetre-level positioning preci-
sion and microsecond-level network latency, the maximum transmission rate may
approach 100 Mbps/1 Gbps (5G technology). A network outage is less than one
millionth of a percent likely with ultrahigh reliability and a density of 100 equipment
connections per cubic metre. It is also possible to connect to many networks at the
72 B. K. Das et al.

same time. As a fifth benefit, the enhanced sensing, placement, and resource
allocation capabilities of fishing gear will be enhanced by integration with cutting-
edge technology like artificial intelligence (Jenssen 2019). If future aquaculture
information transmission technology is to survive network assaults and trace the
source of attacks, it should be able to do so.

3.7 Digitisation of Equipment, Precision Control,

and Cutting-Edge Computing Techniques

Achieving fully automated operation of an aquaculture system requires precise

control of the equipment. Aeration is a good illustration of this. In the classic
arrangement, the farmer must manually turn on and off the switch in order to adjust
the water’s oxygen concentration. Using computers or mobile phones, farmers may
now transmit orders to control equipment remotely, allowing actuators like the pump
and aerator to be activated and deactivated by themselves. The sensor in the
intelligent aquaculture system may provide the oxygen reading directly to the
system, allowing the system to comprehend the dissolved oxygen in the water in
real time. A water pump, electronic valve, or water treatment equipment can be
turned on or off by the central controller based on data gathered and parameter
thresholds. Both a professional expert database and reliable sensors are clearly
necessary for the equipment to continue to function properly. Using an actuator in
an aquaculture system might shorten its useful life because of the many variables that
can influence it. If a machine breaks down and is not fixed right away, the automa-
tion process will be thrown off. The vast majority of aquaculture equipment trou-
bleshooting solutions are still experimental despite the fact that certain professionals
have done substantial study into them. As a consequence, additional study is
required to boost equipment monitoring in order to improve the precision and
integrity of the intelligent aquaculture system.

3.8 Management of Big Data and Cloud Computing

To monitor, detect, and regulate aquaculture requires an extraordinarily sophisti-

cated system owing to the wide variety of influencing elements and the variety of
aquatic organisms (Rao et al. 2018). Large amounts of data created by aquaculture
practices may be easily analysed and presented to producers and decision makers in
an easy-to-understand style using big data technologies combined with a cloud
platform (Roy 2020). Big data and cloud platform technologies have a subcategory
called aquaculture that falls under this umbrella (Balakrishnan et al. 2019; Figueroa
et al. 2018). The collection, categorisation, processing, administration, mining, and
analysis of aquaculture data can supply producers and decision makers with useful
3 Prospects of Smart Aquaculture in Indian Scenario: A New Horizon in. . . 73

information. Aquaculture makes extensive use of big data and the cloud platform for
data collecting, storage, data mining, and application development (Cruver 2015;
Chen et al. 2019). Data from aquaculture production, processing, and sales can be
gathered using a variety of data collecting technologies, including the Internet,
Internet of Things sensors, industrial management systems, professional databases,
and classic format data in traditional formats. Solutions to data storage and
processing issues in aquaculture are typically found in the realms of information
storage and computation. As a result of the diverse nature of aquaculture, big data
must be integrated before it can be saved in a target database or processed further.
Targeted techniques are required for a wide range of aquaculture datasets, data
storage, and processing methodologies. Aquaculture’s complex production environ-
ment generates a wide range of data that are heterogeneous and uncertain, making it
difficult to build mechanism models using traditional data analysis methods. Human
cognitive ability is used to learn natural laws from data, and mechanism models are
then built (Huang and Wu 2016). An accurate model of the real world may differ
from the one prepared in advance because of human observation. It is possible to
automatically discover hidden patterns in data using data mining and analysis
technology (Ma and Ding 2018), to build aquaculture data models and analysis
tools, to integrate these into the aquaculture big data cloud platform, and to provide
users with analysis results and data services for decision-making. Prior to, during,
and following production, the aquaculture business has employed big data analysis
and cloud platform technologies in tandem (Roy 2020). Aquaculture environment
forecasting and early warning (Diamanti et al. 2019; Qu et al. 2017), disease
diagnosis and early warning (Govindaraju et al. 2019), abnormal behaviour detec-
tion and analysis, market analysis and mining have all been used to create solutions
(Purcell et al. 2018; Freitas et al. 2019).

3.9 Integration of Systems

System integration technology is required for smart aquaculture. Aquaculture equip-

ment and subsystems are linked together to produce a coherent, intelligent system.
Intelligent aquaculture systems are designed to supply farmers with a full, integrated
solution that also ensures that the system’s overall performance is optimal, techni-
cally sophisticated and implementable and adaptable. Equipment and application
system integration are part of intelligent aquaculture system integration. There are a
variety of aquaculture equipment systems, including oxygen enrichment equipment,
sensor-based feeding systems and water treatment systems. All of them require the
same communication interfaces, transmission modes as well as voltages. It is thus
necessary to establish an all-encompassing set of parameters that can be applied to
all aquatic system components, as well as an IoT platform that can monitor and
regulate these components. In order to get the most out of the equipment, the layout
should be optimised as well. For example, the intelligent aquaculture ground
includes a water quality monitoring system, a data intelligence processing system
74 B. K. Das et al.

and a fish pest knowledge base that are all integrated. In order to resolve inter-system
communication challenges, each subsystem must be integrated with each other.
Application system integration can be aided by cloud computing, edge computing,
and other approaches. Finally, intelligent aquaculture system integration is based on
user needs, the design of intelligent aquatic equipment and technologies, and the
employment of auxiliary technologies to handle different system building issues.
Increased system stability, data processing speed, and production intelligence are the
primary research goals in intelligent aquaculture system integration. The present
focus of intelligent aquaculture systems is on 5G and cloud computing, although
long-term robust equipment and trustworthy intelligent algorithms are still being
investigated.

3.10 Mode of Smart Data Processing

Aquaculture relies heavily on individual fish data monitoring and forecasting. Smart
data processing models can be used to do this. Fishery identification has been
addressed fully in recent advancements with satisfactory general solutions, including
fish mass estimation (counting, measurement of size and quality evaluation), as well
as behavioural monitoring. It is more difficult to use IT in aquaculture since the
inspected subjects are sensitive, agitated, and able to move freely, and the environ-
ment is not always regulated in terms of illumination, visibility, or stability. To be
effective, the gear must be both inexpensive and water-resistant. However, commer-
cial applications are not yet extensively utilised or achieving the expected outcomes
since these characteristics complicate model creation and make the process signif-
icantly more complex than other elements of animal husbandry. Intelligent informa-
tion processing models have evolved, but they are not quite ready to be put to use in
the aquaculture industry just yet. There are various limitations with intelligent
models, including the inability to explain biological reasons underlying observed
patterns and substantial mistakes in projected findings for data beyond the model’s
range. It is possible that the technique might be used in aquaculture to improve
product quality and production efficiency. For the most part, investigations using
intelligent models in aquaculture have been undertaken in a relatively simple context
with limited interfering factors and are still in the experimental stage. Nonlinear
calibration models, data mining and information technology integration, support
vector machines and memory-based learning, artificial neural networks, and deep
learning, need to be studied further to improve the aforementioned technologies for
commercialisation and adoption by industry sectors.
3 Prospects of Smart Aquaculture in Indian Scenario: A New Horizon in. . . 75

3.11 Rearing System Innovation Need Coupling with AI

When it comes to aquaculture in India, it is all about keeping fish in ponds and tanks
full of water. Percolation, evapo-transpiration, and waste thrown away during and
after culture all contribute to a large portion of the water and nutrients needed in
these systems being wasted. However, given the declining supply of water from a
variety of sources, this cannot continue for the ongoing expansion of aquaculture.
Since water conservation is an issue, farming must be done in a way that either uses
little water or recycles it for the sake of water quality in culture tanks and before it is
finally discharged. In order to provide these provisions, the following systems have
been developed: However, each have their advantages and disadvantages. Using an
aquaculture recirculating system (RAS), tank water may be recycled indefinitely and
continuously via a pipeline. Using mechanical, bio, and UV filters, the system
eliminates suspended particles and excessive nutrient levels from the fish tanks
and maintains water quality that is safe for the fish to eat. When compared to a
high-throughput system, this one only needs to change 10% of the water every day,
allowing for greater water savings. Fisheries may be kept at an extremely high
density thanks to regular monitoring of the water. Despite the fact that both outdoor
and indoor recirculation systems are available, the indoor system is preferred
because of its better and more convenient water treatment capabilities. This system’s
isolation from the external environment means that all of the water’s properties may
be managed, including temperature, pH, salinity, free carbon dioxide, dissolved
oxygen, ammonia, nitrite, and nitrate, as well as disinfection from pathogens. This
is a huge benefit. The treatment of organic waste before it is released into the
environment is also made possible by this technique. One disadvantage is that it
uses a lot of energy and is dependent on complicated technologies. There are a
number of high-value fish and shellfish species that can be grown using this
approach, Since RAS has become widely used for intensive salmon and trout
production in several nations in recent years, it has been procured for 2–4 times
more productivity and more efficient fish pond systems than previously used. RAS
has lately attracted Indian entrepreneurs and a few such units have been energised in
various areas of the nation where pilot-scale production of striped catfish, GIFT
tilapia, singi (Heteropneustes fossilis), and magur (Clarias magur) has been com-
menced with excellent results. In India, pilot-scale experiments have shown that the
technique is appropriate for seed production and grow-outs of high-value flora. For
high-value fish, however, the main issues are its high price and a lack of seed supply.
Cobia, pompano, grouper, and snappers are very susceptible to diseases when grown
in open tanks and cages, and the technique has been proven to be highly favourable
for the development of brood stock of marine species such as these. Similar to
biofloc and aquaponics, new fields of aquaculture production are biofloc and
aquaponics, respectively. It is a farming technology that combines aquaculture and
hydroponics, making use of no soil at all. The water from an aquaculture unit
containing nutrients is fed to a hydroponics unit where the by-products from the
aquaculture are broken down by nitrification bacteria into nitrites and nitrates, which
76 B. K. Das et al.

are utilised by the plants as nutrients, and the water is then recirculated back to the
aquaculture system. When hazardous chemicals like ammonia released by fish and
shellfish are transformed into proteinaceous feed, known as biofloc, it is an innova-
tive and cost-effective method (BFT). Artificial intelligence and smart aquaculture
concepts may be used in conjunction with these rearing techniques to dramatically
boost yield.

3.11.1 Aquaponic System

The system allows for a 90% reduction in water consumption while consuming very
little energy. On the other hand, getting recycled water and nutrients for the crop
results in significant cost savings. People are stepping forward to adopt this tech-
nology as the demand for organic food grows. Several countries, including the USA,
China, and Australia, have shown a strong interest in the technology in recent years,
and hundreds of such units have been built around the world. China has also
advanced this technology by developing floating wetland units with a surface area
of about 4 acres in one of the most polluted and third largest lakes in the world,
“Taihu”, which had a severe problem with algal blooms due to eutrophication of the
water. It is now possible to culture fish and grow a commercial crop of rice on
floating platforms in a large body of water using this system. Aquaponics is a
relatively new concept in India, but it is gaining traction in some areas. With the
financial support of Pallipuram Service Co-operative Bank, MPEDA has taken the
lead in developing aquaponics in Kerala’s village Pallipuram, where over
200 farmers have successfully adopted the technology in a cluster area. The system
enables integrated farming of a large number of vegetables and flowers, as well as
lower-cost fish production and water quality improvement. Thus, if we were able to
couple this technique with AI may suffice the purpose of sustainable aquaculture.

3.11.2 Biofloc Technology (BFT)

The BFT is a culture system when exposed to sunlight, the wasted feed and excreta
are converted into natural food by bacteria, algae, fungus, invertebrates, and detritus,
among other microorganisms. This protein-rich live feed is known as BFT as shown
in Fig. 3.4. Fluctuations in the floc size range from 50 to 200 μm In order to maintain
a desirable ammonia concentration level, just a little quantity of water needs to be
changed, which is why the BFT system has been referred to as a “zero water
exchange system”. Biofloc contains a dry weight protein content of 25–50% and a
fat content of 0.5–15%. High in vitamins and minerals, particularly phosphorus. It
has a probiotic impact as well. To substitute fishmeal or soybeans, dry biofloc is
being considered. An important part of this method is encouraging heterotrophic
microbial growth, which metabolises nitrogenous waste and provides nutrients for
3 Prospects of Smart Aquaculture in Indian Scenario: A New Horizon in. . . 77

the cultured organisms to utilise as food. While treating wastewater, BFT also
supplies food for aquatic life. Molasses is used to keep the greater C:N ratio and
to produce high-quality single-cell microbial protein, which improves water quality.
These circumstances encourage the growth of dense bacteria, which serve as a
bioreactor and as a source of protein food. Due to their increased growth rate and
synthesis of microbial products per unit substrate, heterotrophic nitrogen-fixing
bacteria have a significant advantage over autotrophic nitrogen-fixing bacteria.
Since its introduction to India’s aquaculture industry in the 1990s, BFT has proven
effective in the production of several species of fish and shrimp, including the
popular GIFT tilapia, the striped catfish, the singi and the striped murrel. Shrimp
grown in the biofloc technology showed a higher breeding success rate than shrimp
reared in traditional methods. The growth of the larvae was also improved.

3.11.3 Recirculatory Aquaculture System

In order to increase fish output in India, diversification of horizontal and vertical

aquaculture, species diversification, scientific farming, high-quality feed and seed,
and effective disease management are among the techniques. To raise carp, a variety
of techniques can be used. These include the use of wastewater recycling, IAA, and
various short-term procedures. In India, however, pond-based culture techniques
remain dominant in freshwater aquaculture. It is possible to find floodplain wetlands
and lakes as well as rivers and streams inside India’s inland waterways. Increased
output can be achieved without the need for additional land-based fish farms when
fish are cultured in open water bodies such as tanks and pens. It is also a big problem
for cage farming to receive enough high-quality seed in the proper amount and on
schedule. A low cost recirculatory system developed by ICAR-Central Inland
Fisheries Research Institute. Only Pangasius hypophthalmus (Pangas) is currently
being grown in freshwater cages commercially in India due to its rapid growth,
well-developed culture systems that allow for omnivorous feeding habits, high
acceptability of artificial diets, high stocking densities, improved disease resistance
capacity, and tolerance of a wide range of environmental parameters. Pabda survival
was previously studied by several authors using various feeds and stages of culture,
and the findings of our study exceeded those of other studies by ICAR-CIFRI in a
trial of Ompok bimculatus rearing. Previously, survival rates ranged from 5% to
90%, but in this research, the highest ever reported, it was 96%.

3.11.4 Algal Aquaculture

It is called “algaquaculture” when algae are produced in conjunction with fish

husbandry. For the most part, it is an aquaculture and algae farming system in one.
Using an algal culture, the fish farming system’s effluent is bioremediated. This
78 B. K. Das et al.

method substitutes algae for green crops. When it comes to aquaponics setups, it is
similar to this one. For aquaponics, the most crucial advantage is that algae cultiva-
tion can utilise all nitrogen forms (including total ammonia nitrogen, or TAN) while
still maintaining appropriate levels of ammonia, pH, and dissolved oxygen (Addy
et al. 2017). It is possible to replace fishmeal in fish diets with micro and macroalgae
(seaweeds and phytoplankton), which are generally referred to as “plants”. It is
important to keep in mind that algae make up the foundation of aquatic food chains
that give fish with the nutrients they require. Biochemical diversity among algae can
be far larger than that of terrestrial plants, even if “blue-green algae”, or
cyanobacteria, are eliminated from the equation. This demonstrates the early diver-
gence of algae species in the history of Earth’s existence. The green algae family of
algae created a lineage from which all terrestrial plants descended. Therefore, it is
impossible to generalise about the nutritional value of alga because of the particular
properties of every algae. Debbarma et al. (2022) also fed microbial waste to Ompok
bimaculatus and reported a better growth performance of species in treated groups as
compared with control group. This includes aquaculture, algal cultivation (which
includes algal harvesting), and aeration. This effluent is pumped over the algae
production unit, where fish waste (ammonia) is released into it. Algae eat ammonia
and turn it into high-protein, polyunsaturated-fatty acid (PUFA), and other mineral-
rich compounds. For this reason, the bio-aerator algae consume carbon dioxide from
aquaculture effluent as well, which will also release oxygen into the water, making
the water more oxygenated. Other suggestions for improving water quality included
the addition of algae-bacteriaceous consortia. In the end, it was recirculated in the
aquaculture unit.

3.12 Challenges in Smart Aquaculture

3.12.1 Lack of Information Exchange

There is a lack of information on aquaculture being disseminated. Progress in data

gathering technologies and industrial scale is limiting the amount of aquaculture big
data that can be made available. Because of the wide variety of aquatic species and
the ever-increasing complexity of their habitat, it is challenging to collect data. The
laboratory is still the primary location for the majority of current scientific investi-
gation. Big data in aquaculture is hindered by the difficulty of capturing video
images in natural (often murky) conditions, such as during sickness and aberrant
fish behaviour.
3 Prospects of Smart Aquaculture in Indian Scenario: A New Horizon in. . . 79

3.12.2 An Analytical Model and Technologies That Is Not Up

To Date

There aren’t enough sophisticated models and tools for aquaculture analysis. The
sources of aquaculture big data have been considerably enhanced by the growth of
Internet and IoT technologies, creating the foundation for aquaculture big data.
Research into aquaculture big data, on the other hand, requires further advancements
in intelligence.

3.12.3 Correlation Analysis Have Not Been Performed

Data correlation analysis is inadequate throughout the aquaculture value chain.

Although big data technology in aquaculture may be applied in a wide range of
ways, it is difficult to integrate the whole data chain in correlation analysis, because
of the discrepancies in volume and quality resulting from varying application depths.
A lack of interoperability between aquaculture data and data from other industries
makes it difficult to examine the implicit linkages, such as quality traceability, that
cannot be integrated throughout an entire industrial chain in aquaculture.

3.12.4 High Investment

Although AI is more beneficial, it has drawbacks. Because AI is significantly more

expensive, many people are unable to afford it. The upkeep of an AI system is also
costly. Another significant disadvantage of AI is that it causes workers to lose their
jobs. Farmers may gain, but fishermen will lose.

3.12.5 Lack of System Integration

Still a long way to go before aquaculture can catch up to more conventional food
production methods. The use of big data, robots, the Internet of Things, and
simulation software is becoming increasingly common in production. The corner-
stone of intelligent aquaculture is an artificial intelligence technology platform that
combines data from diverse sources. Advances in technology might be characterised
by terms like “digital,” “industrial,” “mechanised,” or “big data.” As a result, many
“Smart Aquaculture” choices have been taken purely on the basis of past experience.
Because of this long-term cognitive growth. All parts of aquaculture, including
breeding technology and intelligent technology, must be integrated into traditional
aquaculture. Additionally, aquaculture production and management may be totally
80 B. K. Das et al.

automated because of advances in computer technology. In addition, the long-term

viability of a country is strongly influenced by policy and organisation. Scientific
research systems and innovation mechanisms are critical to the growth of smart
aquaculture, but without change, policy and organisation will be a major
impediment.

3.12.6 Complexity of the Culture System

In aquaculture big data, the rising complexity of intelligent fish farms and the
expanding diversity of aquatic creatures provide challenges to data collection.
Many studies are conducted in a laboratory environment. It has always been difficult
in aquaculture to collect accurate data using traditional video image capture tech-
nologies to watch for the appearance of fish diseases and abnormal fish behaviour in
natural environments. Big data analysis for aquaculture is lagging behind the
market’s expectations due to a misunderstanding of aquaculture’s specific charac-
teristics while using big data technologies. Increasing the breadth and depth of
aquaculture intelligence applications such as deep learning, knowledge computing,
swarm intelligence, hybrid-augmented intelligence, and other emerging intelligence
technologies are also necessary for improving aquaculture intelligence. Aside from
these shortcomings, the current study on aquaculture big data is limited in scope and
does not address the whole aquaculture industry.

3.12.7 Lack of Adoption of Advance Technology/Techniques

Deep learning is the defining feature of AI in intelligent fish farming. By combining

deep learning with agricultural technology, it is possible to extract more useful
information from photos and structured data than classical machine learning can
provide. The following issues have been discovered in the use of deep learning to
aquaculture: Cameras or sensor equipment must be built to capture data in a variety
of contexts for deep learning model training, verification, and testing. Complexity in
the design of deep learning algorithms is exacerbated by underwater imaging
systems’ ambiguity and instability. Most aquaculture issues based on deep learning
require labelled sample data, therefore this is an important consideration. Hand-
marking the target category by more specialists is usually necessary. It should be
noted, however, that while deep learning is excellent at learning the properties of
training datasets, it cannot be extended beyond that dataset’s capacity to express
itself.
3 Prospects of Smart Aquaculture in Indian Scenario: A New Horizon in. . . 81

3.12.8 Lack of Solid Decision Support System

Intelligent fish farms may be categorised into three levels based on how far they have
progressed in information technology. Most of the labour in fish farms is done by
aquaculture professionals who run and control them from a distance using their
knowledge and expertise. It is possible for the Internet of Things (IoT) to run
autonomously at the intermediate stage, which is referred to as the “unattended
fish farm”, because the equipment in the monitoring room does not need to be
operated remotely 24 h a day. At this point, the fish farm may be produced entirely
without the input of any human beings at all. Using a cloud-based management
platform, all operations and management activities are planned and chosen freely,
and robots and intelligent equipment run independently as well. This is a fish farm
with no workers at all. Intelligent digital technology may be used by the intelligent
fish farm to address the issues of aquaculture labour shortages and water pollution as
well as high risk and low efficiency.

3.13 Way Forward

1. The benefits of AI in the future are undeniable, but full automation of this process
has yet to be achieved. In the development of an untethered automation device for
automated devices, scientists are working on a technology that can function
without human assistance. 95% of the time, the accuracy is close to perfect. If
AI is used correctly, it can result in an increase in aquaculture production.
Fisheries and aquaculture appear to be the most reliant of all industries on future
technological, scientific, and social advancements, and there appears to be no way
around it.
2. Reduce, recycle, and optimise resource utilisation are all possible outcomes of
smart aquaculture. In other words, it can cut feed consumption while also
enhancing waste and water quality control through big data analysis and real-
time modifications. Renewable energy facilities and enhanced innovation and
interaction with other systems like aquaponics, BFT, or RAS are needed to
produce ecological aquaculture that is green and sustainable.
3. Aquatic goods may be considerably improved in quality, safety, and productivity
with the use of smart aquaculture techniques. As an example, smart monitoring
and management can keep an eye on the health of the environment and fish.
Faster growth and better quality are possible outcomes. Although labour costs can
be reduced, there are trade-offs such as greater capital and energy expenses,
therefore more study and economic analysis will be needed throughout to find
ways for intelligent aquaculture to be financially feasible. As a result, more robust
models are required to establish a completely autonomous operation system,
especially in aquaculture, where a sensor or other component failure might
have disastrous results, including crop loss.
82 B. K. Das et al.

4. Climate and aquaculture environmental information management can help boost

aquaculture production while reducing losses. This is a win-win situation for both
the preservation of natural resources and the increasing demand for seafood.
5. Worker productivity can be improved while labour expenditures are reduced
thanks to automation and intelligent equipment. In addition, intelligent aquacul-
ture may promote economic growth by supporting smart industry and workforce
transformation in response to the demand for technological skills.

3.14 Conclusion

In many nations, such as Malaysia, internet and digital technology has made great
developments. This progress can aid the aquaculture business. It provides as an
example of how aquaculture may be used to improve seafood production systems
that are becoming increasingly significant. Water quality can be continually moni-
tored with a payload of numerous sensors thanks to advances in instrumentation
technology over the last few decades, such as those supplied by YSI. Accurate
digital and real-time monitoring of aquaculture water quality (temperature, salinity,
pH, dissolved oxygen) in a local or remote manner may be achieved by aligning
sensors to a wireless communication system and building an integrated sensor
network-wireless platform A different hardware design and operating programme
are required for the corrective action, which is handled by a neural arc coupled to a
robotic facility. Additionally, this sensor-digital combo will make it easier for farms
to share information via common devices and applications, allowing for more data to
be analysed via cloud computing from more sources. The integration of artificial
intelligence (AI) with robots is seen as the logical next step. Artificial Intelligence
(AI) is the most intriguing and significant topic of robotics. Data and facts are
gathered by sensors or human input while using an AI system placed in a computer.
When a computer programme is written, it runs through the potential actions and
determines which one is best for the situation, using the obtained data. There is no
doubt in my mind that a computer will solve the issues that it has been programmed
to address. It is not possible for a computer to perform analytic functions on its own.
A mechanical device (such as a robot) can be programmed to execute certain
functions and control its activities through the use of software commands. An
aquaculture-ready robot’s information flow must be efficient. When one variable
(such as dissolved oxygen) changes, other variables (such as water dissolved gases
or fish survival) should also change, and the robot should respond mechanically to
ANN to find a solution to the problem. This is the foundation of programming.
Android-based smartphones, tablet PCs, and desktop computers may all be used to
control the system. Aquatic farming as a primary or secondary food production
system will benefit from the ubiquitous availability of mobile phones in all segments
of society, including rural regions, thanks to this handy instrument. In the aquacul-
ture process, numerous elements come into play. Take, for instance, dissolved gas
concentrations, pH levels, stocking densities, and food intake as just a few. The
3 Prospects of Smart Aquaculture in Indian Scenario: A New Horizon in. . . 83

functions that robots software programmes will play would be complex. In other
positions, the robot may not be able to address the problem except to tell the hatchery
operators to act despite the information it gets. Consider the issue of hatchery tank
fish stocking density. Using just a camera and a computer’s image detector, a single
species and how many of them survived a treatment may be determined using
artificial intelligence. It is possible to remove dead fish specimens and introduce
fresh specimens to the hatchery by human workers at this point of understanding. In
the current century, as we move away from coastal aquaculture to the deep sea,
where sea conditions are rough and extended human presence is neither economical
nor practical for operations such as feeding the fish or regular daily monitoring, AI
and robotics will increasingly find application in aquaculture. Another key activity
that is ideally suited to fish farming is biofouling control. A lack of oxygen-rich
water enters a marine cage due to biofouling, which is well-known in the industry. In
addition to decreasing growth, this also raises the risk of sickness and increases the
likelihood of fish death. In addition to being heavier, the cage’s lifetime is also
shortened. Keeping the cage free of biofouling necessitates a significant amount of
manual labour. Robots are capable of cleaning the nets.
For sustainable fishing in the face of climate change, there is a pressing need to
implement a climate wise approach that includes adaptation and mitigation strate-
gies. Furthermore, the climate smart method has a low level of public awareness. The
ability of farmers to provide for themselves and their families in the long term may
be improved if more people in the fishing industry become aware of climate savvy
practices.
Solid infrastructure and generous government policy support will be necessary in
the future for intelligent fish farm development to move forward quickly. Potential
aquaculture enterprises must first be pushed toward upgrading their operations with
the introduction of advanced agricultural technology and high-tech talent, as well as
the regulation of aquaculture sustainability indicators, to ensure the healthy and
green operation of intelligent fish farms.

Acknowledgement Authors duly acknowledge Dr. P. Barik, Assistant Professor, College of

Fisheries, Kawardha, Chhattisgarh for his inputs for the article.

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Chapter 4
Smart and Automatic Milking Systems:
Benefits and Prospects

Suvarna Bhoj, Ayon Tarafdar, Mukesh Singh, and G. K. Gaur

Abstract Manual milk harvesting in a farm is labor-intensive and is one of the

major reasons for the long working hours which takes about 25–35% of the annual
labor demand. Milking activities add substantially to the costs of the farm enterprise.
Over time, dairy industries have incorporated new technologies with a motto of
enhancing efficacy resulting in the implementation of automatic milking systems
(AMSs) to reduce associated labor. AMS offers a future alternative to allow higher
milking frequencies and monitoring of each individual cow based on its productivity
level to bring flexibility in the daily farm routine. Biosensor-enabled AMS technol-
ogy combined with a data management system formulates a better farm management
program. Pasture-based mobile AMS creates a new spectrum of challenges, different
from those of indoor-based feeding systems. Integrated AMS is the most extensively
acknowledged configuration and involves a defined protocol from cow entry to exit.
The industrial robotic arm is a proven technology that can aid two cows at a time
when set up side by side while the automatic rotary milking system generally
possesses five robots, which impressively progresses the efficacy of the procedure.
The automatic teat-cleaning and milking cup attachment process has the potential to
affect milk variables and udder health along with a positive effect on animal welfare.
These set examples display AMS as a promising technology if used effectively, in
the times to come.

Keywords Robotic milking · Automated milking system · Biosensors · Mobile

AMS · Milking frequency · Milk quality

S. Bhoj · A. Tarafdar · M. Singh · G. K. Gaur (*)

Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute,
Bareilly, Uttar Pradesh, India

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 87
S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies,
https://doi.org/10.1007/978-981-19-1746-2_4
88 S. Bhoj et al.

4.1 Introduction

Dairy farming includes cow milking, barn cleaning, and sanitation, maintaining herd
health, continuous checks on hygienic milk production, and improved milk quality
while focusing on the main motto i.e. to gain a respectable consequent turnover. The
activities on a dairy farm are relatively complex and time-consuming and generally
handled by the farm family. When labor is hired, it often adds to the production cost
and on certain occasions, it becomes difficult to even hire labor for a longer duration
due to their ever-increasing demand in other lucrative sectors with greater pay and
benefits. Automatic milking systems (AMS) offer a solution to this long-existing
problem to relieve the farmer from this labor-intensive farm routine.
Historically, all the cows in the ancient farming system were milked by hand. A
survey of this practice showed that 40% of the work-wages workers suffered from
health issues with reported back pain and 30% reported neck and shoulder injuries
(Rossing and Hogewerf 1997). However, it was strongly noticed in the early 70s that
the prerequisites of the dairy industry were not being completely achieved by the
concurrent conventional practices, and much investigation had to be carried out for
advanced technical innovations to ease the hardships associated with dairy farming
and assist in decision making. Robotic milking systems became the center point of
the recent advances providing a greater push to strengthen the dairy farming
business. Lately, AMS is believed to be crucially important for refining working
environments, increasing time flexibility, and saving on expenditures on wages.
AMS can thus be associated with lowered labor costs, more quality time, and better
herd management practices, among other advantages (Carolan 2020). The incorpo-
ration of modern techniques in dairy farming was recognized during the industrial
revolution. Early illustrations of milking machines were seen in the mid-nineteenth
century. Over time, AMS has become a modern-day digital technology in the
contemporary dairy industry and is gradually turning into conventional practice. It
exemplifies a unique and most generous scientific innovation in the dairy enterprise
due to its competence to lower human involvement in several phases of milk
production.
Besides mechanization of milking procedures, AMS also regulates the entry and
exit of cows in the milking box with none of the manual labor required. This not only
cuts the cost of labor but also results in the improvement in milk frequency and milk
yield, enhanced quality of milk, udder health, and marked animal welfare. The first
commercial AMS in dairy farms was introduced in the Netherlands in early 1992,
and by 2020, the installation of 50,000 units across the globe mainly in Europe
(90%), Canada (9%), and other countries (1%) has been reported by AMS manu-
facturers’ estimate. It is expected that by 2025, 50% of dairy cows in North-Western
Europe will be equipped with AMS. In the USA, DeLaval, Lely, and GEA are the
largest firms, but AMS-Galaxy and Boumatic Robotics are also operating in the
market. In Europe, AMS is manufactured by SAC (Sacmilking) and Fullwood
Packo.
4 Smart and Automatic Milking Systems: Benefits and Prospects 89

4.2 Automatic Milking System (AMS)

AMS refers to the mechanization of all phases of the milking operations and farm
management which are commenced manually in a non-automated traditional
milking system (de Koning et al. 2004). In general, the AMS includes a milking
machine, a teat position sensor (commonly a laser sensor), a robotic arm for
automated teat cup application and removal, and a gate system for limiting cow
traffic. A cow ID sensor scans the identification tag (transponder) of the cow and
passes it to the control system to regulate the entry of cows (Maculan and Lopes
2016). After sensory identification of milch animals, AMS tracks the last milking
time and if the cow is supposed to be milked, it is given entry into the AMS box,
there the cow is fed with good quality concentrate as an incentive, and the robotic
arm initiates its working with the following activities: (a) udder and teats detection;
(b) teat washing with water, air jets and rollers; (c) dipping of teats to disinfect;
(d) teat cup attachment; (e) detachment of teat cup when lesser milk flow decreases
to avoid chances of over-milking; and (f) disinfectant spraying as post-dip.
It is not necessary that the same protocol is followed by all AMS models and not
all models are set with the pre-dipping step. On the other hand, it is possible that they
may be installed with only the teat-cleaning step. It should also be noted that
individual teat cup removal after the reduction in milk flow is not a universal step
in all AMS models and full milk flow as a criterion to remove all four teat cups, is
preferred by only a few AMS models similar to CMP.
The basic device of the AMS is the robotic manipulator in the milking unit
(Fig. 4.1). The science behind the working of the milking system is the pulsation
mechanism which releases milk from the teats at regular intervals or pulses while
massaging the teats to avoid teat injury (Fig. 4.2). A robotic arm for attachment and
detachment of the teat cups to the udder, automatically, devoid of human involve-
ment is provided which results in a 20–30% reduction of long working hours in a

Fig. 4.1 Representation of the operation of an AMS. (Adapted from Hogenboom et al. 2019)
90 S. Bhoj et al.

Massage phase Expansion phase

Teat

Stainless
steel
teat cup

Rubber
inflation

Vacuum off Vacuum on

(air in) (air out)

To milk To milk
flow flow

Fig. 4.2 Science behind the automated milking system

dairy farm (Kragten 2014). It includes a single stall system with built-in robotic
functions and milking programs and a multi-stall system with a transportable robot,
pooled with milking and detachment tools at every stall. Single stall structures are
capable of milking 55–65 cows numerous times a day, while multi-stall structures
are facilitated with 2–4 stalls that can milk 80–150 cows thrice a day.

4.2.1 AMS Operations

To have a better understanding of AMS, it must be analyzed in a different way than

conventional milking methods. The underlying principle behind AMS is to let cows
be milked voluntarily with no human intervention, at any stage of the milking
process. Therefore, the animals must enter the milking box without being forced.
Animal herding patterns were also introduced to improve the efficiency of the
system, increase the number of visits to the milking area, and reduce the number
of cows to be gathered into the AMS, as they do not go themselves voluntarily. A
guided pattern was thus developed which consisted of two systems. The first one is
the “feed first” system, which is based on independent access of cows from the
resting area to the feeding area. But to return to the resting area, cows must pass
through a selection gate, from where animals that are allowed to be milked are sent to
the waiting area. Cows that are not capable of being milked return to the resting area,
while only those cows that have to be milked reach the AMS. Another relevant
4 Smart and Automatic Milking Systems: Benefits and Prospects 91

system is “milk first,” in which the cows must pass through a selection gate to access
the feeding passage. From this area, cows allowed to be milked are directed to the
AMS; thus, they access back to the resting area only after being milked.

4.2.2 Types of Animal-Flow

Milking permission is different for each individual cow as per the lactation stage and
milk yield. Different animal-flow models are installed accordingly in the voluntary
milking system. The important flow models have been discussed here.

4.2.2.1 Free-Flow Model

The simplest of all models is the free flow, in which the milch cows have open access
to the milking area, resting space, and feeding alley except a gated entry at the AMS.
Bach et al. (2009) in their study found that the AMS visits were less when cows were
kept under a free-flow system than in a guided-flow system. The research team also
observed that cows in this system were directed to milking 0.5 times a day, whereas
cows kept in a guided-flow system were directed to milking only 0.1 times a day.
Animal welfare and feed intake are estimated to be higher in the free-flow system,
with the rise in dry matter intake (DMI) (Melin et al. 2007). A free-flow system
provides more accurate feed formulation as per the animal requirement. Free-flow
systems function with fewer cow lines, lesser waiting time, and a lesser interface
between dominant and inferior cows (Thune 2000).

4.2.2.2 Guided-Flow Systems (Feed First and Milk First)

As discussed earlier, there are two types of guided-flow systems: (a) feed-first system
(b) milk-first system. In the feed-first system, the cows have access to the feed then
they are forcefully made to enter the AMS box via selection gates and drivers so they
could return to the resting area. While, in the milk-first system, the cows first pass
through the milking area following which they access the feeding alley, and later
return to the resting area.
In the guided-flow systems, there is a decreased DMI and lower feed intake time
(Bach et al. 2009; Melin et al. 2007). Furthermore, in this system, the variations in
feeding behavior cause ruminal acidosis because of a significant reduction in feed
intake time due to which the cows tend to eat more in the feeding alley, where feed is
available as an incentive to milking. Rumen acidosis leads to reductions in milk fat
while decreased DMI causes lower protein levels (Bach et al. 2009). Long waiting
time taken to enter the AMS in the guided-flow area is associated with serious hoof
and welfare issues (Thune 2000) while a major benefit of this system is the reduction
92 S. Bhoj et al.

in fetch cows, i.e. more and more cows readily adapt to AMS and are self-motivated
to enter the milking area voluntarily (Bach et al. 2009).

4.3 Types of AMS

Automatic milking is preferably known as robotic milking. Primarily, there are three
principal robotic milking machines that are developed for refined working condi-
tions, enhanced time flexibility, and savings on labor expenses: integrated AMS;
industrial robotic AMS; and the automatic milking rotary system. The details of
these systems have been described in this section.

4.3.1 Integrated AMS

Integrated AMS is a highly accepted computerized setup and several manufacturers

supply machinery under this type (Fig. 4.3). In this type of configuration, there is a
milking area that receives the milking cows while the robotic arm is attuned to a spot
closer to the milking space. When initiated, the arm moves outward to locate and
attach the teats (Kragten 2014). This AMS operation involves various steps from
cattle entry to exit:
• Cow entry: The AMS contains a gate for cow’s entry which checks its entry to
the milking box. When the animal moves voluntarily to the AMS for getting
milked, the entrance gate will trace the identification details of the cow and allow
it to come inside.
• Feed and teat preparation: After access, the cow is fed with a good quality
concentrate cow to compose the animal during the milking process. Animal’s ID
tag and tracking information of particular teat locations for attachment of teat cup,

Fig. 4.3 Integrated AMS available at DeLaval (left) and Lely (right). (Courtesy: High Contrast and
Anoek 2012)
4 Smart and Automatic Milking Systems: Benefits and Prospects 93

gets automatically registered at the entrance gate. The robotic arm traces each teat
using laser, ultrasonography, or image analysis (Artmann 1997) to recognize the
precise point of attachment. Later, the teats are cleaned and dried with the help of
special teat cups. Other cleaning procedures like cleaning using brushes are also
practiced by integrated AMS.
• Attachment of milking cup and auto-cluster removal: The robotic arm
clutches one or more than one teat cup in a single turn and fixes them to the
teats taking about 12 s to complete (Hogeveen and Ouweltjes 2003). Milking is
initiated almost immediately after the milking cups are on the teats, and it is
regularly checked for the rate of milk flow, yield, and conductivity, etc. per teat
with the help of biosensors tagged. In case the flow of milk is disrupted, the cup is
removed and again fixed back to the same position.
• Teat spray and cow exit: After milking, teat disinfection is executed by the
robotic arm via the spray method. Then the exit gate is opened to allow the animal
to go out. Amidst the last exit and the successive entrance, thorough rinsing of
teat cups is carried out with water for sanitization.

4.3.2 Industrial Robot AMS

Industrial robot AMS is installed with a specially designed industrial robotic arm for
attaching milking cups, located in line with the milking box (Fig. 4.4). Its worldwide
manufacturers are ProFlex, USA, Futureline MARK II from Denmark, and Galaxy
Starline in the Netherlands with ProFlex as the leading manufacturer. The typical
characteristic of this AMS is that once set up alongside as a double-box configura-
tion, one robotic arm can attend to two cows which moderates the milk production
cost considerably. After the milking arm is detached from the teats, the rest of the
schedule is alike as in integrated AMS.

Fig. 4.4 Robotic arm with teat cup attachment in an AMS. (Courtesy: USDAgov)
94 S. Bhoj et al.

4.3.3 Automatic Milking Rotary System (AMR)

The workflow of AMR is based on the concept of rotary milking parlors. AMR
(DeLaval, Sweden) was first conceptualized in 2010, which seems analogous to the
DairyProQ (GAE, Germany) with respect to configuration and functioning
(Fig. 4.5). A double bar gate checks the entry of not more than one cow at a single
point of time on the platform, and then the platform moves to the next spot so that the
next cow enters the platform.
This system is generally well equipped with five robots, two engaged in teat
preparation and two allotted for teat cup attachment, while one focused on spraying
disinfectant after milking. Thus, four robots assist four cows at the same time, thus
making the system appreciably proficient.

4.3.4 Mobile Automatic Milking Systems (MAMS)

Some farmers want their dairy herd to stay long on pastures (mainly from spring to
autumn). They believe that this will not only give them access to fresh air and natural
grasses, but it will also eliminate problems with manure disposal, with natural
fertilization of land. The need of a movable milking robot was felt for cows grazing
in large pastures in bigger herds on ranch systems. This allows the cows to spend

Fig. 4.5 Automatic milking rotary system. (Courtesy: Csand12 and Thomas Fries)
4 Smart and Automatic Milking Systems: Benefits and Prospects 95

Fig. 4.6 Representation of a pasture-based mobile AMS system

more time on pastures thus enhancing feed intake with the least manual handling
(Gaworski and Kic 2017). Moreover, lease or share-farming like enterprises can
come into existence without facing labor issues for the milking process (Neal 2014).
All across the globe, AMS is working in free-stall or compost barns. However,
AMS was first incorporated in pasture-based dairy systems in 2001 in Australia and
New Zealand’s dairy farms (Lyons et al. 2013). Later, this advancement propagated
to pasture-based farming in Australia (Wildridge et al. 2018; John et al. 2019),
Ireland (Shortall et al. 2018a, b), and the USA (Nieman et al. 2015).
Recent research in Belgium, Denmark, France, and the Netherlands has proved
MAMS as a highly encouraging development for pasture-based dairy farming. AMS
is always in a fixed position inside the milking parlor while MAMS follows the cows
to the pasture (Fig. 4.6). The Danish system (Oudshoorn 2008) works with a
transportable mechanism that enables it to be shifted from one location to another
with minimal harm to the pastures (Lenssinck and Zevenbergen 2007). It is well
fitted with the entire necessary gears like a bulk tank, vacuum pump, and cleaning
apparatus, etc. so it can operate efficiently for a maximum of 2 days.

4.4 Sensors in AMS

AMSs are also facilitated with sensory tools assembled with data analyzing tools to
supervise and regulate the entire event. It utilizes wearable sensors on the cow to
record milking and feeding behavior allowing distant monitoring of cow health
(Neethirajan 2017). The use of biometric sensors helps AMSs compile large
amounts of data, which gets automatically stored in a database and later processed
with suitable algorithms (Hogeveen and Ouweltjes 2003; Ouweltjes and de Koning
2004). The dairy owners, with the help of a management program, are capable of
regulating the settings and situations for the milch cows. Detailed reports are
received by the farmer on a screen or as printer messages which can be used further
to take suitable action (Fukatsu and Nanseki 2011). Carbon electrodes serving as
electrochemical sensors are generally used in the robotic milking machine. Radio
frequency identification (RFID) tags embedded in animal bodies trace the milking
management behavior.
96 S. Bhoj et al.

In addition to controlling the milking process, modern automated systems have

in-line biosensors that investigate milk quality in numerous ways by estimating the
composition of milk, Somatic cell counts (SCC), blood detection, progesterone
levels, electric conductivity, etc. It manages to evaluate the energy and protein
intakes of cows along with metabolic imbalances which are major health constraints
in dairy cows and suggest better utilization of grasslands. These milk quality factors
can be assessed precisely (milk fat, R2 ¼ 0.95; milk lactose, R2 ¼ 0.83; milk protein,
R2 ¼ 0.72; Somatic cell count (SCC), R2 ¼ 0.68) by a near-infrared (NIR) spectro-
scopic sensing method or with biosensors or sensor displays or optical methods
(Kawasaki et al. 2008).
Automated cow drafting systems which employ the information assimilated from
different sensors to individually isolate cows that demand particular attention can
also be installed on farms (Wagner-Storch and Palmer 2002). In addition, sensors
incorporated with in-parlor feeding set up offer individual cow exact quantities and
types of concentrate as per the productivity parameters (Bach and Cabrera 2017).
This system is generally coined as a feed-to-yield system. In pasture-based dairies,
the milking parlor is generally equipped with personalized feeding systems. Such
sensors assist the farmer in making correct decisions through smart data handling
solutions. Special guidelines for automatic milking and the use of sensor technology
within the framework of the International Standards Organization (ISO) have been
developed and are being continuously updated.

4.5 Benefits of AMS

4.5.1 Labor Savings

Most of the manual labor associated with dairy farming is involved in the milking
process. As per the reports of Castro et al. (2015), milking thrice a day and its
management by the farm workers takes more than 4 h a day. Installing AMS in a
dairy farm can significantly reduce the need for manual labor which can be
uninvolved or can be used elsewhere in the farm for a better economy. The world’s
first AMS was established in the Netherlands in 1992 because of the significant rise
in labor costs in the country (de Koning 2010). AMS enhances the competency of
labor by auto-cluster removal, and self-managed entry and exit gates to regulate cow
traffic. Rodenburg (2017) observed that automated milking decreases labor require-
ments on all dairy farms irrespective of size and offers a more flexible lifestyle for
farm owners with up to 250 cows.
Farm operations like teat cleaning, milking, and distinction of normal milk from
abnormal milk are integrated into the robotic unit of AMS. The implementation of
AMS technology brings about 20–50% labor savings which is the major reason for
the increased rate of AMS adoption across the globe. In Europe and the USA,
18–46% labor saving has been recorded with the use of AMS (Rotz et al. 2003;
Mathijs 2004; Bijl et al. 2007). Moreover, the lesser the time spent in the milking
4 Smart and Automatic Milking Systems: Benefits and Prospects 97

parlor, the more is the improvement in the quality of life of the wages-workers after
the introduction of AMS. Households involved in the dairy business often face
problems taking part in social activities outside the farms. AMS can alter this
situation and help them to participate in other productive activities.

4.5.2 Increased Milk Yield and Frequency

AMS is said to have a positive attitude on milk yield with increased milking
frequency as there is a higher comfort level in cows and lower stress on the udder.
However, the attributes may vary, because of external influences like farming
environment, animal health, and climate. The installation of milking robots is trailed
by a considerable rise in the volume of production per farm. Bijl et al. (2007)
recorded that Dutch dairies with AMS had an average of 74 cows per full-time
employee while those with conventional milking parlors (CMPs) had an average of
59 cows per employee. Minnesota’s farm business management association
observed that dairies in the upper Midwest averaged over 1 million kg of milk per
full-time employee in a year due to automation while non-automated farms had only
700,000 kg of milk per worker (FINBIN 2016).
The positive influence on milk yield may be because of more frequent milkings
per day when using AMS (Melin et al. 2005). A new idea to fetch all cows into the
AMS with the help of an automatic herding system (AHS) consisting of slow-mobile
fences was introduced by Drach et al. (2017). It was seen in Israel when an AHS was
used in a large commercial dairy enterprise, the functioning time of bringing the
cows to the AMS was lowered by 80% in relation to the experimental set of cows.
They also used this tool to raise milk frequency and milk yield. Hence, economic
benefits are anticipated from this technique. Furthermore, Canadian research
reflected that after AMS installation, dairy farms increased their herd size from
77 to 85 lactating cows, although this change seemed irrelevant for small dairy
enterprises (Tse et al. 2017). In Europe also a hike of 5–10% in herds being
automatically milked is registered (Bach et al. 2007; Bijl et al. 2007; de Koning
and Rodenburg 2004). Lopes et al. (2014) reported an average increase of 14.75% in
the milk yield after adequate management of AMS by allowing a higher frequency of
visits to high-yielding animals. Salfer et al. (2017) also suggested that the AMS
should be managed in a way that the right animal occupies the AMS at the right time
so that the early lactating cows get greater access to AMS to be milked more
frequently. Such an approach is important to bring an upsurge in milk yield in
AMS. This intensified milking frequency appears to be more effective in the case of
multiparous cows rather than heifers. The average number of milking in AMS is
generally in the range of 2.5–3.0 per cow in a day, but considerable differences in
milking intervals are recorded in large dairies (de Koning 2011).
Few commercial farms find it hard to reach the desired milking frequency with
AMS. In such scenarios, a forced-traffic system is implemented to compel cows to
visit the milking box which has a negative influence on the resting time, feeding
98 S. Bhoj et al.

Table 4.1 Alterations in milking frequency, milk yield, and labor reduction post-AMS adoption in
comparison to non-AMS
Drop-in
Country/ labor Milking Increase in
region services frequency milk yield Methodology References
France – – 3% (<2 years), 44 large farms Veysset
9% (>2 years) et al. (2001)
European 19.8–21.3% – – Interview Mathijs
countries schedule (2004)
The 29% – Nearly same 62 farms Bijl et al.
Netherland (2007)
Denmark 50% 2.7 in summers, >19% 9 AMS and Oudshoorn
2.4 in winters Non-AMS et al. (2012)
Finland 30% – Not higher Varied from Heikkila
Con to AMS et al. (2010)
Iowa (US) 75% 2.9 times/day 12% higher 8 dairy owners Bentley
et al. (2013)
Poland – 2.5–3.0 times >12% 50 cow farm Bogucki
et al. (2014)
Poland – – Higher 2 farms, Sitkowska
60 cows et al. (2015)
Canada 20% 3.0 times/cow Higher Phone survey, Tse et al.
530 AMS farms (2018)
Poland – – Higher 16 herds, 3398 Kolenda
cows et al. (2021)

time, or after-feeding behavior (welfare parameters). This can also cause a negative
energy balance and increased risk of subclinical ketosis (Bogucki et al. 2014; de
Marchi et al. 2017; Sitkowska et al. 2015). Endres and Salfer (2015) emphasized the
usage of minimum employees and lesser fetch cows as much as possible to achieve
maximum milk output in the system. The herd must consist of high yielders which
stay in the AMS box for as little as possible, for larger volumes of milk flow in
kg/min.
Well-balanced economic feeding, improved genetic pool with high reproductive
traits, decent udder conformation, proper AMS maintenance can extract maximum
benefits with AMS in terms of productivity, profitable returns, and better decision
making. While studying milk yield of buffalo in comparison to the AMS and
conventional tandem milking parlor, Sannino et al. (2018) observed that that the
AMS milked buffaloes had suggestively higher milk yield per day with increased
persistency of lactation. This establishes the idea that mere installation of AMS does
not guarantee higher milk yield, rather a well-managed and properly implemented
AMS can lead to better gains. A country-wise summary of milk yield as affected by
AMS has been shown in Table 4.1.
4 Smart and Automatic Milking Systems: Benefits and Prospects 99

4.5.3 Less Construction Work Requirement

The civil construction work in a robotic milking facility is very less as compared to a
traditional milking parlor. AMS facilities are less complex; the cost involved in
facilities can be significantly reduced both in terms of smaller built-up areas and
lesser building complexity. AMS installation is therefore much more economical
with respect to civil work involved (Carregosa and Almeida 2015). However, the
design of AMS is quite important because it can affect a cow’s accessibility to AMS.
Location site of gates and corridors in an AMS can affect cow traffic and behavior,
thus time available for milking is also impacted. The significance of a good design
and layout has been emphasized in earlier works (Lyons et al. 2013; Rodenburg
2017) and therefore must be taken seriously.

4.5.4 Greater Economic Viability

It is well-known that the introduction of AMS in a farm requires high initial

investments but there are some factors that must be taken into account to evaluate
the economic feasibility of AMS. Salfer et al. (2017) suggest that labor costs, rise in
milk yield, milk flow management, investment cost, and lifespan of instruments are a
few factors that should be studied to evaluate the profitability of AMS. It has also
been emphasized that installation of AMS is more profitable and has a lower break-
even point than CMP (Hyde and Engel 2002), which is basically due to saving on
wages and increased milk yield. Salfer et al. (2017) replicated a few cases to compare
economic performances between farms practicing AMS and CMP with labor cost
increases ranging from 1% to 3%. They found that with up to 240 lactating cows,
AMS-equipped dairy farms were more capable to support variations in workforce
wages than CMP.

4.5.5 Increased Feed Utilization

Another important gain in farms using AMS was reported by Salfer et al. (2017)
where they found that providing pelleted feed to cows in the AMS box strongly
influences animal’s body condition which in turn raises the milk productivity. This is
attributed to individual herd management where every dairy animal is identified
individually by the system. In this system, the quantity of concentrate supplied is
based on individual nutritional demands calculated by the milk yield of the cow.
The free-flow AMS manufacturing company, Lely, suggests a protocol to offer
PMR (partial mixed ration) on the feed pad by which about 80% of the nutritional
requirement is met. Rest 20% of the feed should be given in the AMS preferably as
pelleted concentrates as an incentive to enter the AMS box (Data from the company
100 S. Bhoj et al.

Lely Industries N. V.). AMS manufacturing companies, DeLaval and Lely Industries
collected actual data from client farms and reported a feed conversion ratio (FCR) of
1 kg concentrate feed for each 3.3 kg of milk produced in AMS farms while in
conventional farms, where total mixed ration (TMR) diets are fed to cows (divided
into standard production batches), the best FCR rates of 1 kg concentrate consumed
per 2.5 kg of milk yield were achieved.

4.5.6 Quality Working Environment and Staff Health

Many dairy farmers or dairy workers faced health issues such as pain in knees, back,
hips, and shoulders concern, etc. Such conditions pushed dairy owners to invest in
AMS to improve the life quality of their workers. Salfer et al. (2017) stated that many
dairy owners chose to invest in AMS keeping health in mind the health concern of
their milkers and as a step to improve the working environment in the farm
enterprise.

4.5.7 Information Management and Decision Making

The main features of AMS that make it a digital age machine are computer-based
monitoring, individualized analysis, complete transparency, absolute online
approach, and data processing of individual cows with exceptional recorded details.
According to King et al. (2017a, b), AMS generates enormous data and vital
information from routine farm operations. This data when processed with digital
monitoring of behavior, rumination, and activity level systems create detailed reports
of the farm status which assists the managers in taking decisions and defining
strategies for smooth management of farm operations and profitable returns.
Measurements of rumination, daily activity, and milk production can assist in the
early detection of farm issues and give a strategic solution of the existing problems in
a planned manner thus lowering economic losses. Such measurements are possible
to be carried out only by AMS in collaboration with special digital monitoring
systems. Auto-sensors can store useful information regarding daily operations,
udder health, milking schedule, rumination time, and feeding pattern in the AMS
(Jacobs and Siegford 2012a) while cartesian teat coordinated with AMS records
udder conformations, facilitates research on genetic and phenotypic variation
between parities (Poppe et al. 2019). The recorded data helps the farmers to take
prompt action and make quick management-related decisions to minimize the impact
of any farm crisis. The efficient use of such a digital age technique is highly
associated with the learning skills of the dairy farmer and willingness to process
and manage such a large amount of data (King et al. 2017a, b).
4 Smart and Automatic Milking Systems: Benefits and Prospects 101

4.6 Effect of AMS on Milk Quality

Milk quality is a subject of primary concern when it comes to milking via automated
systems. Clean and hygienic milk production with a balanced nutritional composi-
tion including all the important factors such as somatic cell count (SCC), total
bacterial count (TBC), fat content, free fatty acids (FFA), freezing point (FP),
protein, lactose, casein, and urea levels should be in desired limits. Milk quality
has been observed to be significantly lower in AMS introduced farms in the first
6 months after installation (van der Vorst et al. 2002). However, this concept has
been highly debated. Pomies and Bony (2001) reported no substantial influence on
the hygienic quality of milk, while Berglund et al. (2002) suggested that AMS milk
quality was as good as manual milking and in a few cases was even higher. This
section discusses the effect of AMS installation on different quality characters of
milk. A country-wise summary of the effect of AMS on milk quality has been shown
in Table 4.2.

4.6.1 Effect on Somatic Cell Count of Milk

SCC is a widely accepted indicator of udder health and milk quality. It is a major
sign of the possibility of inflammatory reactions in the cow’s udder, mainly mastitis.
Bulk milk having high SCC (usually >300,000 cells/mL) is not suited for cheese
manufacturing and has detrimental effects on the quality and sensory characters of a
finished product (Barbano et al. 2006). The influence of AMS on milk SCC is very
variable; authors have reported an upsurge in SCC once the cows are subjected to
robotic milking (Rasmussen et al. 2002), while few others observed no effect on
SCC (Berglund et al. 2002; Mollenhorst et al. 2011). In a Dutch study, SCC was
found to be low in cows milked in such a system (Ipema 1997). In another study, de
Marchi et al. (2017) did not find any difference in somatic cell score for cows in
automated and non-automated parlors, though they were found in greater number in
milk received by AMS. The investment in AMS In the late nineties’ amplified the
TBC and SCC in the bulk tank as compared to the situation prior AMS introduction.
A trial in Europe concluded that both SCC and TBC reduced gradually with time and
experience. However, an increase in washing frequency of the milking system to
twice or thrice a day lowers TBC in the bulk milk samples (van der Vorst et al.
2002). An increase in milking frequency results in higher chances of bacterial
invasion as the teat sphincters remain open after every milking, exposing quarters
to the outside bacteria. Irregular milking interval is also considered as a contributing
factor for higher SCC in the milk from AMS.
Table 4.2 Analysis of milk quality factors post-automatic milking system adoption in comparison to non-AMS
102

Anaerobic FFA Protein

Country SCC TBC Freezing point spores Fat content content content References
The Higher Higher Higher – – Higher – Klungel et al. (2000)
Netherlands
Israel Clearly lower – – – – Lower No Shoshani and Chaffer
variation (2002)
Denmark Higher higher 0.007  C – – – – Rasmussen et al. (2002)
higher
The A bit higher Slightly Slightly – – Slightly – van der Vorst and
Netherlands higher higher higher Ouweltjes (2003)
European A bit higher Slightly Slightly – – Slightly – de Koning et al. (2003)
countries higher higher Higher
Finland Higher higher significantly No variation Higher Higher – Salovuo et al. (2005)
higher
Denmark No variation – – No variation No variation Higher No Oudshoorn et al. (2012)
variation
Iowa (US) 36% lower – – – 2.7% lower No Bentley et al. (2013)
variation
Poland Lower – – – – – – Bogucki et al. (2014)
Latvia Clearly lower – – – Lower – Lower Petrovska and Jonkus
(2014)
Chez Clearly lower Higher Significantly – Significantly – Clearly Tousova et al. (2014)
lower higher higher
Poland – – – Higher – Slightly Sitkowska et al. (2015)
higher
Poland Lower in high – – – – – Sitkowska et al. (2017)
yielders
Poland Lower – – – Lower – No Kolenda et al. (2021)
variation
S. Bhoj et al.
4 Smart and Automatic Milking Systems: Benefits and Prospects 103

4.6.2 Effect on Total Bacterial Counts of Milk

Anaerobic spores often originate from the dung layer over the teat surface. Teat skin
is regarded to be the most important source of milk microflora along with secondary
sources such as herd feces, bedding material, and milking equipment (Derakhshani
et al. 2018). Thus unsatisfactory robotic cleaning of the teats is held responsible for
bacterial growth in milk. In 28 farms, Klungel et al. (2000) observed that the average
TBC raised from 8000 to 19,000 cfu/mL, while the occurrence of bulk milk samples
with TBC >50,000 cfu/mL amplified from 4% to 15% and those with TBC
>100,000 cfu/mL from 1.6% to 6.8% after the introduction of AMS. It is not always
the case that complete teat sanitation before milking is reached in the AMS and no
techniques have been established to evaluate the teat dirtiness in the current AMSs.
Moreover, teat-cleaning failures are quite frequent. This explanation is supported by
Janstova et al. (2011) and Tousova et al. (2014) who found an improvement in
microbiological properties of milk with AMS after emphasizing on hygienic milking
practices, including regular brushing and cleaning of teat and milking cup, as well as
frequent disinfection of the milk piping and bulk tanks. Besides the milking system,
TBC is influenced by certain other parameters too, such as shed cleanliness, equip-
ment hygiene, and largely on milking interval, which influences the time of bacterial
growth in the teat cistern. Optimization of all of these parameters will certainly
decrease the spore count.
In some cases, minor counts of E. coli, S. aureus, and Enterococci were observed
in milk sampled from AMS installed farms. AMS which is not in continuous use can
have some residual milk in its pipelines that can get mixed with warm fresh
uninterrupted milk supply, providing a favorable medium for bacterial growth and
ultimately increasing the total bacterial count.

4.6.3 Effect on the Fat Content of Milk

Minor changes in milk composition can cause pertinent economic loss in the long
term especially for the milk chosen to manufacture cheese. For this reason, many
studies have investigated the effect on milk fat content after the use of AMS. Wirtz
et al. (2004) in his study reported the fat content to be 0.23% less in cows undertaken
by AMS. In contrast, Salovuo et al. (2005) reported an average increase of fat
content from 3.85% to 4.20% after the introduction of robot milking. However,
the increase was statistically insignificant and the change was related to shorter
milking intervals in the AMS. Janstova et al. (2011), Innocente and Biasutti (2013),
and de Marchi et al. (2017) assessed the fat content for milk samples obtained with
AMS and a conventional milking system with different herd sizes, different stages of
lactation, and at different periods of the year to conclude that no significant effect of
milking system on the fat content of milk was noticed.
104 S. Bhoj et al.

4.6.4 Effect on Free Fatty Acid and Composition of Milk

An increase in robotic milking frequency elevates the quantity of free fatty acid
(FFA) (Klungel et al. 2000; Wiking et al. 2006), which adversely influences the milk
flavor and cheese-making process. However, no significant fat quantity difference in
samples taken from AMS and CMP was observed, except for C16:0 and FFAs,
which were found to be higher in AMS than CMP. Svennersten et al. (2002) also
suggested an increased milk FFA profile with improved milking frequency. Higher
content of FFA in robotically obtained milk is also reported by Wiking et al. (2006).
The variation in FFA with lactation seems to be related to milk yield. Higher levels
of FFA in AMS were observed in the first 3 months of lactation when per day milk
yield is higher. Abeni et al. (2005) conducted trials on Holstein Friesian (HF) milk to
find out that lipolysis was influenced by practice followed and concluded that FFA
was in a higher ratio in milk fat obtained from AMS than CMP. FFA is derived after
lipolysis of milk fat-generating glycerol and FFA. Chilling and mechanized condi-
tioning of milk can cause disruption of fat globule; subsequently, an upsurge in FFA
is reported. Shorter milking intervals are also related to bigger fat globules which are
much liable to lipolysis than small ones (Abeni et al. 2005; Wiking et al. 2003).

4.6.5 Effect on Freezing Point of Milk

The freezing point (FP) of milk is relatively constant because it originates from the
osmotic equilibrium between blood and milk. A FP near 0  C signifies admixing of
water to milk; hence FP is considered during the milk payment system. Many
researchers have indicated that the increase in FP is due to increased water content
after the adoption of robotic milking. While producing good quality milk, this aspect
is of major concern because dilution of milk, even in small quantities, results in a
lower concentration of nutrients and degraded technological performance. Two
independent investigations indicated the same entity of rising in average FP
(0.520 to 0.517  C) after the introduction of AMS with the level remaining
substantially higher afterward (de Koning et al. 2004; Klungel et al. 2000). The rise
is attributed to the frequent cleaning and rinsing of the system which adds residual
water to the milk. Janstova et al. (2011) also experienced a minor increase in FP in
Czech dairy farms. Innocente and Biasutti (2013) received similar FP values in
repeated milk samples from different AMS manufacturers. On the contrary, Tousova
et al. (2014) recorded a lower FP in milk from 300 robotically milked cows as
compared to 200 cows milked by CMP.
4 Smart and Automatic Milking Systems: Benefits and Prospects 105

4.6.6 Effect on the Protein Content of Milk

Wirtz et al. (2004) showed that cows milked with AMS had no alterations in protein
content to that from CMP. Short milking intervals were related to higher milk
production per cow per hour with improved protein yields (Hogeveen et al. 2001).
According to Innocente and Biasutti (2013), no effect of the management system
was seen on the protein content of AMS milk. Lower protein content, if observed,
was attributed to higher milking frequency in robotic milking. In a study, Smith et al.
(2002) stated that milk protein ratios were substantially lower in dairy herds milked
thrice a day than in those milked twice a day. Johansson et al. (2017) reported milk
protein composition varies with an average of 2–7% reduction in total casein and
4–6% lesser β-casein content in milk from the robotically milked herd as compared
to CMP. Dietary factors such as the availability of limiting amino acids also
significantly affect the milk protein content (Schwab and Broderick 2017).

4.7 Effect of AMS on Udder Health

Besides the obvious benefits of mechanization, animal health and welfare is also an
important aspect that is to be studied with respect to AMS. After investment in AMS,
cows are milked more frequently, which does not provide adequate time for the
harmful bacterial spores to grow.
Early studies suggested poor udder health with damaged teat orifices in AMS led
farms in comparison to CMP. Conventional AMS models usually have extended
machine-attachment times as compared to modern-day AMS, which is suspected to
be the major cause of deleterious udder health. Recent studies reveal that AMS is
incapable of detecting subclinical and clinical mastitis without prompt tracing of
dirty udders and thorough teat cleaning (Hovinen and Pyorala 2011). In an AMS, the
cleaning decision is no longer in the hands of the herd’s person. There are four types
of devices used by various AMSs for teat cleaning: (1) simultaneous scrubbing of all
teats by a horizontal rotating brush, (2) successive washing by brushes or rollers,
(3) instantaneous washing of all four teats in the same teat cup used for milking, and
(4) successive cleaning each teat with a distinct cleaning apparatus. It is noticed that
not even one of the four schedules dried the teats beforehand the initiation of
milking, thus providing another chance for bacterial growth in the teat orifice.
Jago et al. (2006) studied 130 teat-cleaning procedures in AMS to conclude that
only 67% of all the four teats were thoroughly brushed. Similarly, Hvaale et al.
(2002) recorded nearly 10–20% of the teat scrubbings per cow to be incapable of
removing all dirt and manure from teats before milking. On the contrary, Berglund
et al. (2002), Wirtz et al. (2004), and Abeni et al. (2008) indicated no significant
effect in udder health in robotically equipped dairy farms with respect to
non-automated farms by recording mastitis incidence or evaluating SCC, while
Lopez-Benavides et al. (2006) reported a greater prevalence of udder infections
106 S. Bhoj et al.

with milking machine in comparison to robot operated parlor, this suggested that
switch to AMS is a positive change for udder health.

4.8 Effect of AMS on Milk Let Down

Bruckmaier et al. (2001) suggested that in AMS, teat-cleaning devices stimulate the
release of oxytocin and milk letdown prior to the start of the milking process. In
CMP, tactile stimulation of the mammary gland leads to alveolar milk ejection
through a neuroendocrine reflex (Dzidic et al. 2004a). Thus, a proper stimulus to
the udder is of much more importance in AMS than in hand milking practice because
of short or irregular intervals between milkings (Bruckmaier et al. 2001; Dzidic et al.
2004b; Maþuhova et al. 2004).
AMS should be programmed to stimulate teats based on the expected degree of
udder fill to make milk let down more effective. Alternatively, the threshold for
allowing a cow to be milked can be set to accept cows with udders expected to be
>60% full (Dzidic et al. 2004b). In AMS, the time gap between cleaning and neither
the initiation of milking nor the sequential attachment of teat cup negatively affect
milk ejection (Bruckmaier et al. 2001; Maþuhova et al. 2003).

4.9 Effect of AMS on Milk Leakage

A persistent visual and auditory stimulus from AMS leads to the release of oxytocin
that could also increase the chances of milk leakage. Milk leakage is related to a
greater risk of mastitis (Persson-Waller et al. 2003). It is often observed in cows
being milked by AMS. Milk leakage is frequently observed in the resting area before
taking entry into the milking parlor. Intra-mammary pressure (IMP) is considered a
strong factor responsible for the leakage of milk (Rovai et al. 2007). Although IMP is
yet to be measured for AMS, it may vary to a large extent in milking intervals and is
expected to be higher with AMS. Much study is required to identify the accurate
interpretation of milk leakage with respect to teat end condition and prior milk
leakage history.

4.10 Effect of AMS on Animal Welfare

Animal welfare in a dairy farm is influenced by several related factors. Social

interfaces along with herd mates, manual intervention, farm managing strategies,
nutrition status and feed supply, housing, and further ecological surroundings can
impact animal welfare in both deleterious and progressive manner (Wiktorsson and
Sorensen 2004). Unlike cows reared in non-automated farms, cows in AMS are
4 Smart and Automatic Milking Systems: Benefits and Prospects 107

independent to regulate their day-to-day events and are more interactive with their
surroundings. However, seclusion with the herd mates in an unacquainted environ-
ment is supposed to cause stress in dairy cattle (Rushen et al. 2001). As a result,
different animal welfare factors are to be taken into consideration with AMS.

4.10.1 Lameness

Lameness can be regarded as among the utmost disturbing issues in the dairy
business causing production losses and economic issues on a farm. Lameness results
in production losses in milk and hence fat and protein components of milk are also
decreased which is held responsible for economic losses in a dairy farm. It can be
tagged among some of the principal welfare and financial concerns in the contem-
porary dairy industry, which may be the root cause of a drop-in milk yield 4 months
before diagnosis and continue to affect the yield 5 months even after its clinical
diagnosis (Green et al. 2002). It is therefore known to cause the highest losses in a
dairy farm, after mastitis and fertility issues. This problem is highly associated with
the type of housing system, condition of the barn, and state of the floor in the sheds.
Lameness in cattle is a debilitating disorder in which it experiences foot lesions and
pain. In the majority of lameness cases in cattle, foot lesions are a cardinal sign. The
affected animal attempts to reduce the weight tolerated by a particular limb by
frequently shifting their own weight from one leg to another leg in order to bear
the pain during lameness (Neveux et al. 2006). Pain is a serious factor in lameness
which is often concealed by the enduring nature of cattle which results in late
detection of lameness by the farmers and often leads to life-long losses. In the
USA, the incidence of lameness is recorded in 15% of the adult dairy animals
(Rajkondawar et al. 2002a) whereas, in 75% of the cases, farmers fail to recognize
the illness (Whay et al. 2003). Reducing the walking activity can be taken as a
measure to minimize the chance of lameness.
Pain experienced from lameness could act as a stressor in dairy cattle. Lameness
influences the motivation and frequency of voluntary visits to AMS hence the
milking interval and the productivity of the cows are affected (Borderas et al.
2008). All these factors collectively sum up the negative impact on herd profitability,
as well as on the health and welfare status of the cows. Therefore, it becomes
predominantly significant to address cases of locomotion and lameness concomitant
to AMS in time. Rajkondawar et al. (2002b) developed a scoring system to detect
lameness in limbs in AMS. However, the discussion on the scoring system is beyond
the scope of this chapter.
Bach et al. (2007) observed a lower tendency to visit AMS with increased fetch
rates for cows with high locomotion scores (scores of 3) on an increasing severity
scale of 1–5 in comparison to low score cows. Similarly, in another research, Danish
cows identified as lame had a low milking frequency than healthy cows being milked
with AMS (Klaas et al. 2003). Cows that entered the AMS less are reported to have
higher gait scores (mean  SD: 2.5  0.8 vs. 1.8  0.4, respectively) than the cows
108 S. Bhoj et al.

that visit AMS frequently (Borderas et al. 2008). They were scored for gait using the
method described by Flower and Weary (2006) which suggests that cows with
lameness (high score in severity scale of 1–5) tend to be reluctant in entering
AMS because of painful conditions.
While renovating an existing building to accommodate an AMS, necessary
alterations may result in increased lameness because of faulty abrasive concrete
floor which can add a negative impact on hoof health. On the contrary, no significant
observations were made with the severity or quantity of lameness after switching to
an AMS, keeping all other structures of the barn and management practices fixed
(Hillerton et al. 2004; Vosika et al. 2004). It can be concluded that lameness is more
a result of faulty housing design and ill management rather than the form of a
milking system. AMS can be facilitated with the distinct examination of the force
applied on each load cell which auto-senses the variations in the distribution of
weight which is a strong indication of lameness (Pastell et al. 2008). This can be a
very helpful and influential tool to detect the ailment in the early stages when
medication is quite effective. Some AMS has four load cells on the floor of the
milking stall to sense weight shifts in the cow limbs. This feature lets the robotic arm
stay directly beneath the udder every time.

4.10.2 Estrus and Its Detection

In general, AMS is not found to affect most of the reproductive parameters in a cow.
However, variations were seen in conception rates and the number of services per
conception 1 month after setting up AMS in a farm, and a non-significant decline in
fertility level was recorded up to 12 months after installation (Dearing et al. 2004;
Kruip et al. 2002). This condition may resolve with time as AMS is equipped with
estrus detection mechanisms that function better once the cows get accustomed to
the newly introduced system and the dairy owners dedicate more time to observing
the cattle behavior. Most of the studies undertaken in this field are short term and
longer trials are required for a complete explanation for any changes in fertility
parameters. Transponders identifying the cow on its entry to the parlor are often
coupled with activity and rumination monitors. The activity monitors record the
number of steps taken by a cow each day. Increased activity is highly correlated with
low progesterone levels during estrus which can be used to determine the correct
timing of artificial insemination (AI) (Durkin 2010). A study conducted with six
trials by an Afikim/DeLaval activity monitor achieved an average of 82% estrus
detection specificity with a range of 73–92% (Durkin 2010). The low detection rates
were most likely attributed to the presence of lame cows that showed the least
activity during estrus and negatively affect the data. Auto-detection of increased
activity can facilitate visual monitoring of estrus; however, the data interpretation
from the AMS has to be learned by the farmer on his own.
4 Smart and Automatic Milking Systems: Benefits and Prospects 109

4.10.3 Stress Responses to Different Milking Systems

4.10.3.1 Physiological Stress Response

In AMS, milch cows are handled in a manner to give adequate motivation for the
self-regulatory and effective approach of cows to entry and exit to the milking stalls
independently without the help of herd handlers. Gygax et al. (2010) suggested that
when there are no movement restrictions, animals have more freedom to choose their
partners. Hopster et al. (2002) compared the stress response of primiparous cows
while milking in an AMS and a tandem milking parlor. Cows under AMS reflected a
low heart rate (HR) as well as lesser plasma epinephrine and norepinephrine levels
indicating decreased stress during milking while the HR increased significantly just
before entering the AMS as well as the tandem milking parlor. However, HR drops
by the end of milking in both the milking systems (Wenzel et al. 2003). The
accelerated HR may be attributed to anticipation of feed or feeding behavior offered
before or during milking in AMS. Hagen et al. (2005) found no difference in the HR
variables between AMS and parlor milking systems. Higher cortisol levels were
reported in milk obtained from AMS-milked and parlor-milked cows (Abeni et al.
2005; Hagen et al. 2004; Wenzel et al. 2003) which is indicative of chronic stress due
to forced-guided cow traffic permitting access to feeding or resting areas in the AMS.
Gygax et al. (2006) and Lexer et al. (2009) found no differences in the milk cortisol
levels between the systems, although HR was elevated in both free or guided-/
forced-traffic AMS types.

4.10.3.2 Behavioral Stress Response

Vocalization, defecation, and urination are acute stress indicators in cattle. These
factors have been reported particularly on the first day of transition when milking
was done by AMS. However, the occurrence of these stress factors quickly dropped
to zero on subsequent days (Jacobs et al. 2012). An increased activity like kicking
and stepping reflects anxiety in animals. Hopster et al. (2002) stated no variation
while analyzing steps and kicks during the milking between AMS and a CMP,
whereas Hagen et al. (2004) found less stepping and kicking in AMS. It is said that
step-kick rates are expected to be highest at the end of milking during teat cup
detachment (Jacobs and Siegford 2012b). In this regard, Wenzel et al. (2003)
observed that the step-kicking occurred significantly in all phases of milking by
AMS as compared to CMP.
110 S. Bhoj et al.

4.11 Disadvantages of AMS

4.11.1 High Initial Cost of Investment

The cost of AMS is the primary obstacle in the adoption of such a system. The initial
investment borne by the farmer is often two to three times more in comparison to the
cost incurred on a traditional milking parlor. Each single stall AMS is estimated to
cost $150,000 to $200,000 and can serve approximately 60 cows depending on the
number of milkings the dairy owner wants to gain for each cow per day. However,
building a new traditional parlor is also not always an inexpensive affair. At times it
may involve a heavy investment of $4000 to $15,000 per milking stall. However, the
CMP is expected to be a single cost incurred while farmers might require buying
several AMS to accommodate their herd (Bijl et al. 2007). The farmer needs to put
significant funds to install as well as maintain the machinery in AMS. Moreover, the
operating cost of AMS is high due to increased electricity consumption whereas
water and chemical consumption is reduced to 50%. Initial returns from AMS are not
as expected at least 10–15 years post-installation as compared to the cost-benefit
ratio (BCR) for CMPs. The introduction of AMS also affects the cost incurred in
milk production, feeding, energy usage, and labor requirements. Therefore, it is
important to find a logical scientific outlook to minimize costs and enhance profit in
the long run (Jiang et al. 2017).
According to Maculan and Lopes (2016), investment in AMS in Brazil can cost R
$19,000 (or about US$5250 at the time of the survey) per head, and return may take
8–10 years. A recent quote by DeLaval in 2018 (access granted to authors) presented
value in the order of R$740,000.00 (or US$195,000.00) for acquisition and imple-
mentation of the newest AMS unit (DeLaval VMS V300). It consists of a milk-first
system capable of milking about 70 cows on average in a compost dairy barn.

4.11.2 Alterations in Milk Quality

Another potential disadvantage is in milk quality. In AMS, an increase in milk yield

is achieved through frequent milking intervals due to which the milk fat is estimated
to slightly decrease than the milk obtained with conventional hand milking practice
twice a day (Klungel et al. 2000). This accounts for a major economic loss as the fat
content is the major factor in the milk payment system. Furthermore, to achieve a
higher level of production, the more concentrated feed was to be consumed which in
turn may raise the feed costs.
4 Smart and Automatic Milking Systems: Benefits and Prospects 111

4.11.3 Alterations in Milk Composition

High levels of milk FFAs are undesirable for promoting sensory changes and
shortening shelf life and milk yield. Several studies have suggested that an increase
in FFA in AMS farms can be associated with increased frequency and lower milking
interval, interfering with the fat globule size and making them more vulnerable to
lipolysis (Wiking et al. 2003, 2006). Bach et al. (2009) suggested that milk first or
feed first like guided-flow systems are likely to reduce milk solid levels (fat and
protein). Fat content is intended to fall due to an increased chance of ruminal acidosis
and reduction in protein is suggested as a result of a reduction in dry matter
consumption of cows in forced flow systems, especially when water access is
restricted.

4.11.4 Lack of Flexibility

It is a huge challenge for a dairy owner to bring change in the size of herds while
using AMS. AMS is already mechanized in such a manner that it can entertain only a
fixed number of cows with provided infrastructure. Thus AMS lacks flexibility with
respect to modification in herd size. While choosing whether to invest in an AMS or
a traditional milking parlor, dairy owners must compare the savings on labor
achieved in AMS with respect to the increase in fixed costs and depreciation rate
(Bijl et al. 2007).

4.11.5 Increase in Incidence of Subclinical Ketosis

According to Tatone et al. (2017) and King et al. (2018), chances of subclinical
ketosis are found to be higher in dairy herds with robotic milking. The reason is
associated with the increase in the number of milking events and milk production
integral to AMS. King et al. (2018) also stated that AMS managed dairy cows are
more likely to develop negative energy balance as they produce more milk than
those milked by CMP. Since these cows do not tend to surge feed intake to the same
extent, the incidence of subclinical ketosis may eventually happen. This study
established that AMS herds have higher proportions of β-hydroxybutyrate than
those in CMP (Tatone et al. 2017). King et al. (2018) concluded that cows with
higher milk yield have larger amounts of β-hydroxybutyrate circulating in the blood
with more likelihood of subclinical ketosis.
112 S. Bhoj et al.

4.11.6 Requirement of Specific Body Conformation

Few cows in the herd may show specific behavioral or conformational characteristics
which are not appropriate for incorporating them into a robotically milked herd.
Undesirable teat position and udder size create problems for cluster attachment in
AMS. During a study, teat variation and cluster attachment was observed on all
15 North American dairy farms, causing 0–3 extra culls per year from herds with an
average of 94 cows (Rodenburg 2002). Distance between rear teats is also consid-
ered a major complication for cluster attachment by AMS. According to Rodenburg
(2002), a very high rear udder is also held responsible for causing cluster attachment
failure as it was tough for the sensors to get to the high rear teats in a horizontal
plane. In New Zealand, 8% of productive new cows were culled due to detrimental
conformations that were expected to cause difficulty in washing and milking
(Woolford et al. 2004). Although technological advancement has brought 85–98%
higher success rates in AMS cluster attachments with commercial herds (Gygax
et al. 2007), a 7.6% teat cup attachment failures has been reported to at least one
quarter during milking, even after successful teat positioning by the sensors (Bach
and Busto 2005). Thus, udder and teat conformation of cows need to be checked
before including it to the herd. Alternatively, genetic selection of cows with desirable
teat positions has to be done to avoid cluster attachment hitches and unsuccessful
milkings. Teat cup attachment failures bring about 26% losses in milk production
during subsequent milking (Bach and Busto 2005). Milk productivity of unaffected
quarters is also said to be reduced because of increased milking interval between
quarter failure and subsequent milkings. However, milk production reaches previous
levels within seven milkings following a failure.

4.11.7 Long Transition Period from CMP to AMS

Dairy managers are required to devote time to train their herds on how to enter the
milking system and experience the AMS in the beginning. It takes about 3–4 weeks
of intense labor to get a herd acclimatized to enter voluntarily with a success rate of
80–90% (Rodenburg 2002; Jacobs and Siegford 2012b). However, the adaptation
period may differ between individuals in the herd depending on the response, age,
and experience of the herd mates (Munksgaard et al. 2011; Weiss et al. 2004). Prior
exposure to the typical sounds and movements within the milking parlor may help
the animal to get accustomed to the new setup with ease; although, primiparous cows
seemingly familiarize themselves with the AMS more readily than multiparous cows
(Jago and Kerrisk 2011). Net returns or profitability, and management of the setup
have been observed to be the main objectives of small and medium farmers in view
of the transition period for the AMS (Hansen and Jervell 2015).
4 Smart and Automatic Milking Systems: Benefits and Prospects 113

4.11.8 Lack of Motivation for Voluntary Entry and Exit

Milking frequency and maximum amount of feed distributed to the cows at each
milking is at the dispense of the computerized management system. There may be
situations where the milch cow is not willing to enter the AMS box. Moyes et al.
(2014) and Tse et al. (2018) surveyed 217 farmers in eight Canadian provinces and
observed that on an average, 2% of a herd had to be culled for not being able to adapt
to voluntarily milking, although the physical and behavioral features are in the
acceptable range. It may be possible to take manual help to manage animals in the
milking area and feeding passage, to execute the milking and feeding processes.
Therefore, motivating the milch cows to individually access the milking parlor
voluntarily is vital for accomplishing the task and overall worth of the system
(Hogeveen et al. 2001).

4.12 Future Prospects of AMS

There is an upsurging interest in automation because of the speedy evolution of

machine learning and artificial intelligence. Milking robots are not ideally those
devices that substitute human resources to create a life of leisure; rather, it seems to
yield a new type of work opportunities for farm labor under AMS. The automation of
dairy farms cannot be assumed as a one-way process but can also be studied with a
“process-relational” perspective which suggests approaching the dairy business as a
“dynamic socio-physical process” where humans and animals are intertwined
together.
After the COVID-19 pandemic, in order to reduce the chances of human inter-
vention, robotic milking is the “new normal” in the dairy business with Norway
recording the highest number of AMS installed, out of all the Nordic countries. It has
been reported that more than 47% of the milk production in Norway is conducted by
milking robots by the end of 2018 (Vik et al. 2019). In the past, many studies have
been conducted to assess the aftermath of AMS installation on milk productivity,
labor savings, and animal welfare. Although an enormous amount of generous
research is being carried out over the above-mentioned factors, exotic studies over
many prospects can be done to evolve better solutions. The main objective of AMS
installation, i.e. labor reduction is not always feasible due to a significant number of
cows that are not willing to enter the milking area and need to be fetched to the AMS
manually. Thus, to make the machine 100% efficient, strict strategic innovations are
required to modify animal behavior, reduce fetching rates, and reduce the labor
usage to nil. The stockmen are essential in the setting of new milking and farm
schedule after AMS installation as the main motto of the machinery is to bring
modifications in workload, not lesser work.
Cows that are meant to be fetched each day to the AMS may show a tendency to
develop lameness or other health issues which is another area of concern that
114 S. Bhoj et al.

requires investigation. It may be a possibility that AMS can only detect subclinical
lameness not before it becomes prominent in gait. Thus increasing sensitivity for
early detection of lameness in cows, automatically, may prove beneficial for cows
that have an increased probability of being fetched daily.
AMS in the pasture-based farming systems need to be explored more to improve
the competence by effectively co-coordinating both the systems. Researchers can
explore further to study the cow behavior in pastures with respect to changes in
energy balance and milk yield. Higher stocking rates, effects of weather in AMS
pasture, and motivating factors for pasture-based cows for voluntary actions are the
areas that require more attention.
Technological advances in teat cup attachment and precise identification and
sanitation of muddy udders need to be pushed further to increase their effectiveness
to elevate cow health and milk quality standards. Further studies on milk leakage
should be conducted to determine whether this is truly a problem for cows in AMS
systems. Clean and hygienic milk practices and ways to motivate voluntary move-
ments of cows to be milked in AMS box should be further sorted to reduce
undesirable FFA content in AMS milk. Decision making and management practices
should be analyzed to take maximum advantage of the large data pool generated by
AMS to predict the highest milking frequency for each cow to maximize the milk
yield.
Although AMS has been widely adopted in European countries, it has yet to be
recognized in other regions of the world that have a considerable cattle population.
For instance, it is important to continue looking for opportunities for its extension in
Indian conditions for farms of all sizes and locations. A constant matter of attention
should be to understand why AMS is approved only in specific areas or countries.
Other than quantitative and structural aspects, the focus should be more on the
feasibility of the location whereby individual farmers want to establish practices
related to AMS. It should be taken into consideration as to what factors encourage or
hinder the implementation of such equipment. To this effect, biosensors in AMS
should be studied to exploit their ability in the detection of significant changes in
herd health and feed changes that might signal ill management. More extensive
research regarding metabolic and immune responses of AMS supported cows needs
to be done. However, few evidence suggests that AMS is not responsible for
negative energy balance in AMS-milked cows despite more frequent milking, but
they do not offer any conclusive explanation.
AMS designs should be modified according to the stocking rates of cows per
AMS, stalls and feed passage, breeds, and structural conformations which are
distinct for individual breeds found in specific regions. Animals of different breeds
belonging to different geographical areas differ in temperament and may show
different behavioral patterns when put in AMS. Efforts should be made to decrease
the time spent by milch cows in the selection and waiting areas to encourage the
voluntary movement of cows through AMS. Modern AMS stalls open in the front
and rear to permit cows to enter and exit the milking area in a straight line, without
turning or bending themselves.
4 Smart and Automatic Milking Systems: Benefits and Prospects 115

4.13 Conclusions

The integrated approach of learning about AMS involves three factors, i.e. humans,
machines, and animals. Concerns arising after incorporating AMS to the herds
cannot merely be explained by taking farm owners and animals into account. It is
also required to consider the human–machine–animal nexus. It takes enormous
technological innovations to influence everyday farm operations in a dairy. The
rise in milk production, labor savings and flexibility in farm schedule, better life
quality, reduced feeding cost, better reproductive performance, improved milk
quality, herd health, less equipment maintenance, and profitable turnover on invest-
ment are the main factors to be considered by farmers who are likely to invest in
AMS. Installation of the milking machine to reach its maximum efficiency requires
intricate work by the servicemen who fix up the entire setup and also the efforts of
the farm supervisors in smooth management for a better functioning AMS. For
efficient AMS operation, factors such as reproductive traits, animal genetic potential,
and diet formulation should be improved in order to gain maximum returns. AMS
not only extends its help in milking operations with in farm but also on a larger
network in running a dairy business. Although AMS is a digital age automated
technology, its proficient working requires the physical presence of the dairy owner
and strategic management without which the project is likely to fail.

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 Part II
Smart Food Manufacturing
Chapter 5
Smart Technologies in Food Manufacturing

Rahul Vashishth, Arun Kumar Pandey, Parinder Kaur,

and Anil Dutt Semwal

Abstract The concept of “smart food factory” is also known as “food industry 4.0”
or “connected food industry” which relies on the up-gradation of existing facilities to
modern technologies and their integration with the internet, cyber physical system,
artificial intelligence, big data, cloud computing, etc. Smart food industry is a
digitally connected, automated food processing environment where the real-time
monitoring of physical operations, collection and sharing of data throughout the
processing line, storing and processing the data through neural networking and
algorithm, communicating and cooperating with humans in real time, along with
precise control over the operation through actuators and robots are done simulta-
neously. As compared to others, food industries are lagging behind in adoption of
modern technologies. However, increasing market requirements and strict compli-
ance of food safety regulation in food industries around the world like, grain
processing industry, fruits and vegetables processing industry, meat, fish and poultry
processing industry, dairy and beverage industry, etc., upgraded their existing
facilities at different levels with modern technologies. This chapter illustrates a
deep insight on transformation of different food processing industries from a tradi-
tional processing environment to a digitally connected modern technologies based
environment over the decades.

Keywords Automation · Artificial intelligence · Food industry 4.0 · Internet of

things · Processing

R. Vashishth (*) · P. Kaur

Vignan Foundation for Science Technology and Research, Guntur, Andhra Pradesh, India
A. K. Pandey
Department of Food Science and Technology, MMICT & BM (HM), Maharishi
Markandeshwar (Deemed to be University), Ambala, India
A. D. Semwal
Grain Science and Technology Division, DRDO-Defence Food Research Laboratory, Mysore,
India

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 125
S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies,
https://doi.org/10.1007/978-981-19-1746-2_5
126 R. Vashishth et al.

5.1 Introduction

Today, automation in the food processing industry is becoming critical due to the
rapidly changing global scenario. Several factors such as increasing competition due
to globalization and mergers, consumer requirements for high-quality food, govern-
ment emphasis on cleanliness, hygiene and safety factors, and flexibility in
manufacturing operations for more diversified product lines took the attention of
food industries towards up-gradation of existing systems. Moreover, precise control
over the processing line from a simple step to a highly sophisticated operation is also
of utmost importance to achieve the above-said predetermined goals. For example,
in today’s competitive market, it is becoming very common for manufacturers to
facilitate a variety of their existing products by changing the product formulation or
processing conditions. However, this needs flexibility and control over the system
through which a food technologist can easily modify the recipe.
To remain competitive, food factories look for up-to-date manufacturing tech-
nologies that meet the increasing market demand for variability in products with
high processing capacity, efficient supply chain, and optimized energy consumption.
Apart from building an effective control system and maximizing operational flexi-
bility, automation of processing lines is one of the important ways for food industries
to tackle future challenges.
The rise of a concept of “smart food factory” also known as “connected food
industry” or “food industry 4.0” relies on the up-gradation of existing facilities to
modern technologies and their integration with information technologies for digita-
lization of processing lines. The core concept of a smart food factory includes
efficient and sustainable production by building a smart ecosystem where
employees, machines, and electronic devices can interact with each other through
information technology (Konur et al. 2021). Smart factory enables the
transformation of manual and disconnected operations into a digitally interconnected
and inter-operable system within the ecosystem that allows making decisions based
on real-time data and real-time communication between operators, sensors, and
machines to execute necessary actions and accelerate the overall processing activity.
In a smart food factory, a high rate of productivity can be achieved by collecting and
evaluating the real-time data of machines and sensors, and utilizing the inferences to
build a flexible advanced process with reduced error and costs. Such an integrated
approach also offers unprecedented real-time management of problems, optimiza-
tion of unproductive set-up times, and taking rapid actions against the threats and
opportunities throughout the processing lines (Konur et al. 2021).
5 Smart Technologies in Food Manufacturing 127

5.2 Components of Smart Food Factory

Usually, modern food processing technologies, sensors, and information technolo-

gies are considered as major components of smart food factories as they together
play an important role in creating a smart ecosystem throughout the food chain
ranging from the purchase of raw materials to the supply of finished products.
From the last decade, food factories are continuously shifting from conventional
practices to more advanced processing technologies such as vacuum microwave
heating, ohmic heating, extrusion, irradiation, high-pressure processing, pulsed
electric field processing, plasma processing, pulsed magnetic field processing, ultra-
sound processing, etc., to achieve their sustainable development goals (Ghoshal
2018; Chauhan 2019). These technologies are capable of producing microbiologi-
cally safe minimally processed food with the same or improved quality attributes as
compared to conventionally processed ones. Even modern food processing technol-
ogies are more efficient than their conventional counterparts in terms of energy
consumption, emission of carbon dioxide, impact on the environment, waste gener-
ation, and recycling. Modern food processing machines are more flexible and
provide control overprocessing due to their programmable logical control (PLC)
and sensor-based automatic system. Today, most automated food processing
machines come with an inbuilt PLC system in which user-oriented instructions can
be programmed and stored in internal memory to perform specific functions such as
continuous monitoring and control over processing time, temperature, pressure, etc.
A PLC system generally contains a processor, internal software and data memory
areas, input/output (I/O) interface, and electronic circuits. In an automated
processing machine, the PLC system continuously evaluates the data received
from the sensors by scanning the loaded software and then conveys the I/O status
through a digital screen to the operator and allows him to take necessary action
(Yeole et al. 2017).
The application of each technology has its advantages and disadvantages; how-
ever, smart sensor-based technologies are also considered more prominent for the
present and future development of sustainable food processing and automation
(Jambrak et al. 2021). In modern food processing system sensors play an important
role as they are designed to detect and alarm towards the unusual change in their
physical environment and keep informing the operator through PLC about the
situation, and thus facilitates better control over the processing technologies,
processing conditions, and the quality of the final product (Yeole et al. 2017).
Smart sensors can utilize the available data to analyze or describe the situations
and take corrective decisions or control processing conditions. Here, the “smart”
concept refers to those devices that are intelligent or can be connected to other
devices. Today, several types of chemicals, biological, and electronic sensors are
used at different levels of food processing lines depending on their specific func-
tions. However, electronic wireless sensors (radio frequency sensors) are of utmost
importance in the smart food factory due to their ability to connect from distant
devices through information technology (Miranda et al. 2019).
128 R. Vashishth et al.

The most important part of a smart food factory is information technology

(IT) which play a pivotal role in digitalization or creating a smart ecosystem by
integrating machines and sensors at a different level of intelligence through the
Internet of Things (IoT), cyber physical system (CPS), artificial intelligence (AI), big
data, and cloud computing, etc. Together, these IT tools offer real-time monitoring of
physical operations, collecting and sharing data throughout the processing line,
storing and processing the data through neural networking and algorithm, commu-
nicating and cooperating with humans in real time, providing precise control over the
operation through actuators and robots, and thus facilitating the smart and automated
food processing environment (Otles and Sakalli 2019). Today, both perishable and
non-perishable food processing industries took a step ahead and adopted several
innovative technologies at different levels of food processing and management to
increase their efficiency and productivity through automation. The technological
automation in different food processing is as follows:

5.3 Cereals, Pulses, and Oilseed Industry

The demand of the food grains is expected to increase by 1 billion Mg by the year
2100 owing to the rapidly increasing population and thus food demand (Stewart and
Lal 2018). Also, there is a rise in the production of pulses, however, there has also
been a stagnant per capita availability from 1980 to 2013 (Joshi and Rao 2017).
Similarly for food grains, there is an increase of 2% per annum in the demand;
however, a declining trend is observed in the per capita consumption (Chand 2007).
There is thus a slow growth which may be attributed to a number of reasons such as
lack of policy neglect (Singh et al. 2016), global climate change, decrease in plant
communities’ biodiversity, risk of epidemics. Advance methods and digital innova-
tions which can help improve forecast, control epidemics, monitor crop cultivation,
etc., i.e., Industry 4.0 and technologies such as artificial intelligence, Internet of
things, etc. can help improve the agri-food system (Butsenko et al. 2020). Moreover,
the food industry is believed to have a possibility to automate most of its tasks
(Chmielarz 2020). For modeling pulse production models such as ARIMA
(Autoregressive Integrated Moving Average) and GARCH (Generalized
Autoregressive Conditional Heteroscedastic) can be used (Gudadhe et al. 2018).
Similarly, many more models, automation, biosensors, etc. are making its way in the
cereal and the pulses industry.
5 Smart Technologies in Food Manufacturing 129

5.3.1 Automation in Identification and Classification of Seeds

Quality

There is a wide range of genetic diversity among the edible legumes crops world-
wide and the ascertainment of best quality seed is the major problem for the seed
distributors as well as farmers. The knowledge of legume seeds quality and variety
before sowing is another important aspect for agribusiness operators as it influences
the overall crop production. Today, market value of grains and their products are
determined by their physical features including size, color, appearance, physical
defects, etc. (Tian et al. 2020). However, manually sorting and grading of seeds
including beans is a very difficult and time-consuming process and even inefficient
when performed at large production volume. Therefore, automation in legumes
seeds classification is essential for both marketing as well as crop production and
for sustainable agriculture.
Application of Computer Vision System (CVS) or Machine Learning Technology
is a big breakthrough in identification of seed health and distinguishing between
different varieties of the same beans having similar features. Moreover, machine
vision systems are very useful in inspection and quality evaluation of food grains
with higher speed, consistency, and accuracy. Mahajan et al. (2015) reported that the
machine vision system is a key to the successful inspection of legume seed quality
through image acquisition. The quality of legumes can be inspected without destruc-
tion using visible, infrared, and other bands of the electromagnetic spectrum. As
compared to conventional techniques which are based on manual inspection,
machine vision technique is fast and automatic, and seeds characteristics like
external surface examination, moisture content, oil content, insect infestation detec-
tion, and internal structure visualization can be done without destruction. De Araújo
et al. (2015) developed a computer based visual inspection system for distinguishing
different varieties of beans present in a same batch using a correlation-based multi-
shape granulometry. They found their system extremely successful in distinguishing
grains based on their size, eccentricity, and rotation angle with an accuracy of
99.97% and suggested the application of developed technology for automatic
inspection of beans. In another study, Koklu and Ozkan (2020) developed a user-
friendly SVM using a MATLAB graphical user interface to identify quality and
distinguish the varieties of dry beans to obtain a uniform seed classification. Their
study showed that CVS with support vector machine (SVM) classification model
resulted in highest accuracy for Barbunya, Bombay, Cali, Dermason, Horoz, Seker,
and Sira bean varieties, i.e., 92.36%, 100.00% 95.03%, 94.36%, 94.92%, 94.67%,
and 86.67%, respectively. Apart from grain variety and quality, computer vision,
machine learning, and image processing are also useful in automation in discovering
diseases and supplementing medication on time to agriculture crops including
cereals and legume crops to avoid heavy economic losses (Urva 2021).
130 R. Vashishth et al.

5.3.2 Automation in Cereals Processing

Immense advancement is making its way in agriculture smart technology. Technol-

ogies including UAV cameras, satellite data, IoT sensor networks, ground sensors,
robotic platforms, etc. (Donaldson et al. 2019) will play a great role in boosting the
production.
Technologies such as biosensors find use in order to detect components like
starch in the wheat flour sample (Mello and Kubota 2002). X-ray systems also find
use in grain bulk (Mahalik 2009) for information such as detecting the size of grain.
Material discrimination X-ray (MDX) technology has also been used to differentiate
materials of different densities such as detecting contaminants of different density in
case of breakfast cereals. They offer the advantage of being able to detect food in
non-linear packaging which might not be possible in case of traditional X-rays
(Massaro and Galiano 2020).
The traditional milling equipment such as hand stone (chakki), mortar, and pestle
have now been replaced by modern milling equipment for pulses and cereals such as
pre-cleaners including drum scalpers, aspirator, reciprocating air screen cleaners,
etc., milling equipment such as rubber roller huller for rice, carborundum roller mill
for pulses, etc. Dehullers used for pulses include barley pearlers, tangential abrasive
dehulling devices, etc. (Tiwari et al. 2020). Other sophisticated and advanced
equipment such as color sorter to sort rice or the milled product from product that
still contains the adhered seed coat, stone separators, plan sifters for wheat, hydro-
cyclones for maize, etc. are utilized. Large modern commercial plants involve
automation of the milling process from grain intake to grain packaging/bagging.
There are flow metering devices, various milling machines performing dehulling and
splitting simultaneously and help in achieving high throughput.

5.3.3 Automation in Legumes Processing

In the grain processing industry, soaking is a most widely used pre-treatment for
cereals and legumes before applying other processing methods. Soaking plays an
important role in hydration of grains to provide a required amount of water for
further processing, such as the removal of husk, bran, and splitting of grains by
breaking the close contact between cotyledon from the outer husk/bran part by
increasing grain volume, and at the same time strengthens the grains through
pre-gelatinization of starch and denaturation of protein during pre-cooking which
later reduce the damage of cotyledon of legumes during splitting/milling. It is also an
important step in different processes such as malting and/or fermentation of grains.
To mark the proper hydration of grains it is important to measure their volumetric
expansion individually, which is a time-consuming process. However, disregarding
the variation in volume of individual grain, bulk analysis method is a preferred
traditional method. Therefore, Valerio Cubillo et al. (2020) evaluated an automated
5 Smart Technologies in Food Manufacturing 131

digital imaging processing pipeline for measuring individual grain volume and
modeling the hydration kinetics of Phaseolus vulgaris (Matambu bean). Their
study suggests that the technology is potentially useful for the automation in grain
processing industries and can be applied for future studies on food properties, grain
quality, processing and packaging design. However, Holopainen-Mantila et al.
(2021) used hyperspectral imaging technology for the automatic monitoring of
early-stage moisture uptake and germination of two different varieties of faba beans.
Furthermore, extrusion processing is a fully automated technology. Recent inno-
vations made this technology more efficient in terms of energy consumption during
cooking and process control. It is a complex process which requires a specifically
designed instrument called “extruder” in which mixing, forming, cooking, and
shaping of snack foods including texturization of legume proteins can be done
through programmable logical control (PLC) based automatic operational control
system. Today, extrusion technology is widely used in industries for the removal or
reduction of anti-nutritional factors in cereals, legumes, and oilseeds as well as
retention of more nutrients in snacks as against conventional processing (Nikmaram
et al. 2017; Sánchez-Velázquez et al. 2021).

5.3.4 Automation in Oilseed Processing

Oilseed’s processing industry is in its initial stage of automation. Several studies

have been conducted to increase the productivity of oilseed processing industries in
terms of processing methods, combination technologies and byproduct utilization.
Most of the studies are focused on the use of different pre-treatments such as
microwave heating, ultrasound, pulsed electric field, high-pressure treatment, etc.,
to the oilseeds to increase the oil extraction efficiency of existing systems. However,
as such the oilseed processing industry is lagging far behind in application of
computer technology, IoTs, cyber physical system, smart sensors, etc. in current
scenario.

5.3.5 Automation in Quality Control

Many new technologies have started to make their way to ensure better quality
control. Methods utilizing portable sequencing devices, for instance, “MinION” as
well as mobile PCR can detect the pathogen such as rust fungi in cereal crops such as
wheat (Donaldson et al. 2019).
Detection of fungal activity in grains is of importance to prevent the spoilage of
grains and production of mycotoxins. Many techniques are used for the same
including DNA and immunoassays, immune-fluorescence, electrochemical
methods, photo-acoustic FTIR methods, etc. (Magan and Evans 2000). E-nose can
also prove to be an immensely useful tool for detecting the mycotoxins and thus help
132 R. Vashishth et al.

in ensuring safety and quality during cereal production and processing (Cheli et al.
2016). Automated fluorometric sensor for the determination of zearalenone myco-
toxin is also being used (Llorent-Martínez et al. 2019).
Gas chromatographic (GC) analysis using multiple selective detectors, such as an
electron capture detector (ECD), a flame photometric detector (FPD), etc. are utilized
in order to detect the pesticide residue in grains (Mastovska et al. 2010).

5.3.6 Automation in Preservation

The newer technologies are also used in the preservation of food grains and help
achieve higher shelf-life and stability over storage by preserving the grain along with
making them chemical free. Ozone also finds use for the preservation of food grain
(helpful in reducing the fungal, mold, and bacterial contamination) and also provides
the advantage of minimizing/eliminating the chemicals (pesticides, fumigants) used,
and thus help in preservation along with producing chemical free grains (Tiwari et al.
2010). Similarly, UV radiation is also used for sterilization of the stored food grains
which acts by altering the nucleic acid directly, making it impossible for the
microbes to read the genetic code and thus causing them to die (Irradiation et al.
2006).

5.3.7 Challenges

The automation, advancement, and the digitalization provide immense benefit to

help increase the production; however, it comes with its own challenges. However,
these technologies due to situations such as insufficient or unstable energy supply
pose a threat of heavy financial loss due to production shutdowns (Butsenko et al.
2020). Methods to detect fungal growth or mycotoxins such as electrochemical
methods, etc. are not sensitive enough to detect fungal activity early and are also
expensive and time-consuming (Magan and Evans 2000). The use of biosensors and
e-nose possess a number of challenges such as the results being affected by the
ambient gas as well as due to sensitivity of these sensors to changes in the relative
humidity and temperature (Wojnowski et al. 2017). Other challenges continue to
hold food manufacturers back from upgrading completely to Industry 4.0. Problems
such as lack of proper infrastructure and internet availability in all the areas allowing
to utilize IoT and related techniques truly as they are meant “anywhere, anytime”
still is somewhat far and continuous up-gradation for the same is required. To
conclude, for crops such as cereals, a new approach requiring methods which can
integrate physical engineering and biology while working with breeding, agronomy,
etc. are required (Donaldson et al. 2019).
5 Smart Technologies in Food Manufacturing 133

5.4 Fruits and Vegetable Industry

Automation is a process in which fast and precise technologies are used to perform
repetitive work in an organization or industry, such as management of material,
process control, packing of items, management of supply chain, etc. Automation not
only optimizes the overall profitability by reducing production, processing, and
handling costs but also assures produce delivered to the appropriate market in its
sound condition. Moreover, smart automation significantly reduces manual labor,
offers robust control over the process by increasing accessibility and connectivity in
processing steps, and facilitates data-driven decisions using computer capabilities
(Martinez et al. 2019). The fruits and vegetables processing industry includes several
activities ranging from postharvest to retail of fresh/processed produce. Their
processing includes different workstations to perform several steps such as grading,
washing, peeling, cutting, antimicrobial treatments, packaging, transportation, and
waste management, etc. The automation at different levels of the fruits and vegetable
industry is as follows:

5.4.1 Automation in Grading

Fruits and vegetables are highly diversified horticulture commodities in terms of

their quality parameters. For example, any two fruit or vegetable grown at the same
time, in the same field, and picked from the same tree at the same time may differ in
their shape, size, skin color, texture, aroma, and other attributes such as the presence
of blemishes and diseases. Furthermore, in commercial operations, these quality
characteristics significantly influence the appearance, nutritional content, and organ-
oleptic properties of processed products and even their suitability for preservation.
Therefore, grading of fruits and vegetables, based on one or two or more of the
aforesaid parameters, is usually performed to increase their commercial viability.
Traditionally, these parameters are evaluated manually through visual inspection and
the sense of touch by the trained operators, which is a tedious and time-consuming
process. Moreover, the decision taken by the operators can be influenced by psy-
chological factors such as acquired habits or fatigue, or inconsistency due to human
error in the classification process, which sometimes could lead to repeat the entire
inspection process (Cubero et al. 2011; Paulus et al. 1997). In manual grading, the
error in fruits and vegetable classification increases with the increase in the number
of quality parameters considered for the decision-making process. Therefore, such
type inconsistencies raised the need for automation in fruits and vegetables
processing industries to speed up the grading process with high classification
accuracy.
Machine vision or computer-vision-based systems have received more attention
from researchers to reduce human errors and simplify the tedious fruits and vegeta-
bles classification process. Today, machine vision-based systems and new optical
134 R. Vashishth et al.

technologies, such as ultraviolet, near-infrared, hyperspectral, and multispectral

imaging, made it possible to develop potential tools for non-destructive quality
monitoring of fruits and vegetables with the adequate accomplishment of predefined
standards. As compared to manual inspection, non-destructive technologies are
much faster allowing automatic inspection of the whole processing lines with
defined objectives. The use of ultraviolet and near-infrared based systems also
made it possible to inspect those defects of fruit and vegetables which are beyond
the sensibility of human eyes. The creation of machine vision-based tools required
the inclusion of multiple technologies and knowledge that are ranging from image
acquisition techniques to specially designed algorithms for the analysis of spectral
images. Machine vision-based fruits and vegetable inspection systems are specially
targeted towards desired goals such as in-line sorting for commercial grades, detec-
tion of spoilage, or the distribution of chemicals on the fruits and vegetable surface
(Blasco et al. 2017).
The application of most machine vision systems is similar to conventional
techniques, and uses visible information to determine the external quality of fruits
and vegetables. However, its efficacy largely depends on the type of camera used
and the illumination of the scene as they are closely associated with the quality and
resolution of the acquired images. The system requires illumination with a good
color rendering index to compare and measure the difference in object color from the
reference standard. During image acquisition, proper illumination of the scene
avoids specular reflection as it can mask the certain blemishes on the surface by
producing bright spots. Apart from color, image acquisition also allows analyzing
several other external properties of fruits and vegetables related to their maturity and
quality. For instance, Prabha and Kumar (2015) developed an image analysis
algorithm to predict the maturity of bananas. By the application of the color intensity
algorithm and surface area algorithm, they achieved 99.1% and 85% accuracy in
defining the maturity stage of bananas. Moreover, Vélez-Rivera et al. (2014) devel-
oped a ripeness index to classify pre-climacteric, climacteric, and senescence stages
in the “Manila variety” of mango using a non-destructive computer vision system.
The external defects of fruits and vegetables such as skin damage and diseases can be
also analyzed through a machine vision system using color information alone.
Blasco et al. (2007) identified different types of defects including differentiation
between calyx or stem-end from the skin defects in orange. They developed a region-
growing algorithm to identify defects when the color of a region diverged from the
homogenous color of the largest region of orange skin, by assuming it to be the
sound skin. They achieved 94% efficiency in identifying surface defects and 100%
efficiency in distinguishing stems from the surface defects. Recent advances in
hyperspectral imaging technique have also shown the potential of real-time auto-
mated in-line inspection and quality control as it offers to analyze the chemical
composition, internal quality or detection of invisible damages of produce. However,
hyperspectral imaging needs to be studied more for its effective implementation for
the automatic grading of fruits and vegetables in industries.
5 Smart Technologies in Food Manufacturing 135

5.4.2 Automation in Washing, Peeling, Cutting, Disinfection

Today, consumers prefer either fresh or fresh-cut fruits and vegetables over
processed ones due to increasing awareness about a healthy lifestyle, nutritional
losses during processing, and desired fresh-like sensory attributes. In the fresh-cut
industry, one of the major problems is the high perishability of fruits and vegetables.
However, the quality of fresh-cut produce can be maintained for a longer duration of
the processing, and distribution conditions are optimally maintained. The imple-
mentation of innovation and automation in different processing stages, such as
washing, peeling, cutting, packaging, and storage, could be one such way to increase
the shelf-life of fresh-cut produce (Tapia et al. 2015). Today, most of the fruits and
vegetables processing industries rely on electricity-based semi-automatic batch or
continuous type mechanical systems for washing, peeling, and cutting operations
(Shirmohammadi et al. 2012; Ansah et al. 2018). Although, several modern
non-thermal technologies such as high-pressure processing, plasma processing,
ultrasound processing, irradiation processing, pulsed electric field processing sys-
tems are being adopted in many commercial units for the automation of sanitization
and decontamination step of fresh or fresh-cut fruits and vegetables (Chauhan 2019).
These novel non-thermal technologies help in extending the shelf-life of fresh-cut
produce by arresting enzymatic reactions and inhibiting microbial growth with high
precision and with no or marginal effect on nutrition and sensory properties of
produce.
Drying fruits and vegetables up to optimum moisture content is one of the
effective ways of extending the shelf-life of produce by arresting the microbial
growth, chemical, and enzymatic reactions. However, conventional dryers have no
such system to monitor the changes in physico-chemical properties and moisture
content of products, when the product is under drying process. Furthermore, separate
facilities and extra manpower are required to estimate the physico-chemical proper-
ties and moisture content of produce and to assure the completion of the drying
process. In conventional drying, sometimes repeated drying is needed to achieve
optimum moisture content, which increases the processing time as well as overall
processing cost. The integration of conventional dryers with non-destructive quality
evaluation techniques, such as machine vision, low frequency nuclear magnetic
resonance (LF-NMR), visible near-infrared (Vis-NIR), and hyperspectral imaging
system, could be one such most recent innovation in developing smart dryers to
overcome the above-said problems (Su et al. 2015). For instance, Sampson et al.
(2014) used a computer vision system for in-line monitoring of changes in color,
texture, and moisture content of apple slices during drying. Pu and Sun (2015)
successfully investigated moisture distribution in mango slices during vacuum
microwave drying by combining it with the Vis/NIR hyperspectral imaging system.
Romano et al. (2016) successfully investigated the changes in browning, hardness,
and moisture content of mango and litchi during in-line drying operation by inte-
grating Vis/NIR spectroscopy with the existing drying system. Chitrakar et al.
(2019) used the low field nuclear magnetic resonance (LF-NMR) technique for
136 R. Vashishth et al.

automation in monitoring the water activity of produce during the in-line drying
operation. However, Sun et al. (2019) used LF-NMR integrated microwave vacuum
drying system and developed a BP-ANN (Back Propagation—Artificial Neural
Network) model for rapid real-time monitoring of changes in moisture content of
banana, carrot, and a pleurotus eryngii. These studies show that integration of
non-destructive and computer vision techniques with existing dryers can facilitate
automation in fruits and vegetables drying industries as well as can reduce the
overall operational cost.

5.4.3 Automation in Packaging and Supply Chain

Fresh fruits and vegetables trigger a series of stress-related physiological changes

after harvesting and exhibit a greater variance compared to processed products,
which complicates their automatic packaging and control over quality during the
supply chain. Before transportation or sale, some fruits are usually placed into
packaging trays or into cartons, which requires manual inspection of visual defects
and correct orientation of fruits, for example, the direction of the stem. The manual
inspection of such type of operation is tedious and time-consuming and even
increases the overall operational costs due to the low level of automation. Giefer
et al. (2020) used a combination of line laser and charge-coupled device (CCD)
camera for constructing three-dimensional images and developed a convolutional
neural network (CNN) model based on an automated orientation determination
system. However, further studies are required for the complete automation of fruits
packaging lines in industries.
The cold supply chain is usually preferred for storage and long-distance trans-
portation to avoid postharvest losses by maintaining the metabolic activities of fruits
and vegetables. For proper functioning, the cold chain needs desired controlled
atmospheric conditions in the supply chain. Automatic real-time monitoring of
temperature and humidity is important to improve the transparency and assure the
quality of fruit and vegetables during storage and cold supply chain. Today,
radiofrequency identification (RFID) technology in combination with wireless sen-
sors is extensively used for the automation of traceability, management of supply
chain, monitoring of cold chain of moving goods and retails (Biji et al. 2015). RFID
is a wireless sensor-based automatic identification technology that identifies items
and gathers data without human intervention. In this technology, information can be
retrieved from the database using identification numbers stored in RFID tags which
can be useful for taking necessary action. RFID tags are in-built with specific
hardware and software which offers real-time monitoring, environment sensing,
tracing, and tracking automation in the supply chain. According to the requirement
of the fruits and vegetables supply chain, RFID tags can be embedded with specific
sensors, such as moisture sensor, temperature sensor, humidity sensor, turbulence
sensor, and placed inside the boxes and containers for real-time monitoring of
temperature, humidity, pH, shock/vibration, and the presence of light. Moreover,
5 Smart Technologies in Food Manufacturing 137

RFID has an advantage over other conventional technologies, such as barcode and
data loggers, as it does not require visual contact, and information can be read over
100 m of distance (Kumari et al. 2015). Nowadays, gas sensing technology is also
gradually evolving to get more precise information about the quality of products
during the supply chain as they can detect or sense the changes in volatiles, such as
ethylene or acetaldehyde, formed due to the metabolic activity of fruits and vegeta-
bles (Wang et al. 2021). However, gas sensing technology needs further develop-
ments for its commercial application in real-time monitoring of the supply chain.

5.4.4 Automation in Waste Management

Wastage is one of the major problems in fruit and vegetable processing industries
across the world which restricts their availability or increases shortage in the market.
Here the term “wastage” includes the life span of fruits and vegetables which ranges
from the production to its edibility for consumers. Therefore, sensor-based manage-
ment systems are employed to sense the quality of products and assure it is available
to the customer’s prior degradation. Today, wireless sensors are in higher demand to
reduce the spoilage of fruits and vegetables by continuous monitoring of quality till it
efficiently reaches the customer. Wireless sensors in integration with IoTs increase
the resource knowledge among the stakeholders by remotely providing real-time
status of perishable produce and thus, allowing them to take timely decisions in the
supply chain (Sangeetha and Vijayalakshmi 2020). RFID-based sensing technology
is widely used for remotely trace and track storage conditions and the supply chain of
fruits and vegetables and minimize the losses by timely taking necessary action.

5.5 Dairy Industry

The dairy industry is one of the largest growing food sectors, holds six billion
consumers around the world (FAO 2020). However, the profit of the dairy industry
is going down as per the demand for milk which is due to the problems related to
milk hygiene, accuracy, production rate, etc. Milk processing in the dairy industry
includes different workstations to perform various activities such as livestock man-
agement, milk collection, disinfection, packing, storage, assembling, transportation,
etc. Conventional processes are not enough to handle bulk processing efficiently as it
requires huge manpower to perform various activities. However, automation of
processing facilitates operators/humans to perform multiple tasks within the best
time management (Sain et al. 2020). Therefore, depending on the processing appli-
cation requirements different levels of automation such as fixed, programmable, or
flexible automation are adopted in the dairy industry. The commercial application of
robotics and automation in the dairy industry is ranging from monitoring of milking
138 R. Vashishth et al.

animals to retail vending of milk for end consumption (Meshram et al. 2018). The
automation at different levels of the dairy industry is as follows:

5.5.1 Automation in Livestock Management

Livestock management is one of the laborious tasks of the dairy industry which
requires skilled and dedicated manpower for feed, water arrangements, and assessing
the health of farm animals. The advancement in artificial intelligence (AI) and
computer vision (CV) technologies provided opportunities for automatic surveil-
lance of the behavior and needs of each animal. The manual inspection of animal
body conditions and other health problems is subjective, time-consuming, and often
requires experienced employees. Furthermore, finding and treating lameness and/or
other health problems in cattle is very important for farmers, as these are associated
with milk safety and productivity. The identification of such problems in animals at a
herd level requires a trained observer; however, it often remains undiagnosed until
the problem has become severe. An artificial intelligence-based machine vision
system reads ear tags to identify facial/muzzle/coat pattern features of animals for
identification (Halachmi and Guarino 2016). A 3D vision system can automatically
diagnose the behaviors associated with lameness or other health issues of the animal
using locomotion scoring and body condition scoring algorithm and alert farmers on
a real-time basis to take necessary action. However, the automatic measurement of
animal behavior using a 3D vision system is still in its early development stage and
requires more research for efficient application at animal farms (O’Mahony et al.
2019).
Proper feeding of cattle is another big challenge in farm management as it is
associated with animal health and milk production. A conventional feeding system
(CFS) is tedious, requires high manpower for mixing and placing the feed for each
animal, and has a rigid work schedule. However, in large farms, mixing and
distribution of feed are usually done through tractors. Today, many dairy farms are
shifting from a CFS to AFS (automatic feeding system) to reduce the overall
operational time. AFS includes a stationary feeding hopper, a mixing unit, and a
distribution wagon operating on a rail. The main advantage of AFS over CFS is low
labor requirement with a possibility of high feeding frequency (Da Borso et al.
2017).
The accurate and timely identification of the estrus cycle of an animal at the farm
level is another important aspect of livestock management. Traditionally, the estrus
cycle of dairy cattle is identified by repetitive monitoring of animals at standing state
while being mounted. It requires a considerably trained and experienced farmer to
achieve a reasonable level of efficiency in identification. Several automated estrus
identification systems have been developed to overcome the limitations associated
with traditional methods. Automated estrus monitoring technologies can identify
even slight changes in animal behavior during both day and night, and recommend
optimal insemination time to the farmers. However, the low rate of adoption of such
5 Smart Technologies in Food Manufacturing 139

automated technologies by farmers is due to the lack of knowledge about the benefits
of investing in such detection technologies (Adenuga et al. 2020).

5.5.2 Automation in Milking of Dairy Animals

Milking of animals is a challenging task due to the rapidly increasing number of

animals and hygiene conditions during the handling of milk, mainly at the farm
level. In a conventional process, a substantial number of trained human resources is
required for milking a large number of cattle. It affects the overall profitability due to
slow production rate, compromise with hygiene conditions, and high labor cost.
Dairy industries are shifting towards automation and robotics-based milking pro-
cesses to control problems associated with conventional milking. Robotics in
milking facilitates adequate hygiene with high production efficiency per cow and
reduces the labor cost of processing (Sain et al. 2020). Milking robots are integrated
with lasers and vision system technology to automatically locate the cow teats and
extract milk from them. In western countries like Sweden, robotic milking of animals
received a wider acceptance. A Swedish dairy equipment company established the
world’s first commercial laser technology-based robotic milking rotary with a
milking capacity of 90 cows/h. Such type of robotic system performs multiple
tasks ranging from the milking of animals to precisely filling of milk in retail
containers without human intervention (Meshram et al. 2018).

5.5.3 Automation in Cleaning and Hygiene of Equipment

and Working Area

Cleaning and hygiene of equipment and working area are the most important aspects
in the dairy industry. Conventional cleaning of plant equipment involves human
interactions which are time-consuming and prone to hygiene risk. However, today
most of the dairy industries implemented automatic or semi-automatic cleaning-in-
place (CIP) systems to achieve a higher hygiene level of production facilities without
dismantling the system. CIP is defined for cleaning and sterilizing processing
equipment mainly holding tanks, valves, and pipes using cold and hot water, and
acid and base solutions with high energy efficiency. Automated CIP is a sensor-
based centralized cleaning system in which the operator can select the desired
washing program or alter the program and/or increase the number of the washing
cycle according to the requirement, through digital PLC and SCADA (supervisory
control and data acquisition) system (Kale et al. 2017). The CIP system reduces the
requirement of water and detergent for cleaning of dairy equipment by reducing
flushing time and/or reuse flushed water and detergent (Dhage and Dhage 2016).
140 R. Vashishth et al.

5.5.4 Automation in Quality Testing

Milk quality inspection in the inbound supply chain is important to produce pre-
mium quality milk products. However, the existing milk quality monitoring systems
are based on manual testing of milk in labs located at the processing plants.
Therefore, milk supplies are mixed before reaching quality testing to ease the
transportation and delivery system (Sain et al. 2020). Today, several IoT types of
tools and sensors are under development for automatic monitoring of milk quality at
the farm level before pick up. Ahmad and Jindal (2006) developed a mathematical
model for automatic rapid online assessment of raw milk microbiological quality.
The estimated standard plate count and methylene blue reduction rate by measuring
the change in output voltage through a specially designed light-sensing probe and a
mathematical model. Near-infrared (NIR) spectroscopic sensing systems could be
another effective tool for assessing the quality of milk from an individual cow during
milking at dairy farms. Iweka et al. (2020) developed a NIR spectroscopic sensing
system and successfully assessed the major constituents of non-homogenized raw
milk, i.e., fat, protein, lactose, solid not fat (SNF), milk urea nitrogen, and somatic
cell count using at a wavelength ranging 700–1050 nm. The further development
and application of such technologies could be helpful for farmers in getting real-time
information of milk quality and physiological condition of each animal and for
managing farms more efficiently.

5.5.5 Automation in Packaging

Today, robots and sensors became an important part for automatic packaging of fluid
milk and milk products. Robots are mainly used for picking and placing the milk
from one place to another. However, sensors are integrated for real-time monitoring
of package position during filling and sealing as well as for signaling the machines
and robots when to perform the activity. The most commonly used robots in milk
packaging are as follows:
1. Articulated robots: These are usually six-axis robots having ten or more
interacting arms, providing more flexibility in milk packaging operations like
filling, sealing, handling, assembling, etc. (Sain et al. 2020).
2. Delta robots: It is the category of modern-day robotics also known as Parallel
Link Robots. These types of robots are employed to work in extreme conditions,
such as very high or low temperature and pressure conditions, where manual
work is not possible to get efficient output (Meshram et al. 2018; Sain et al. 2020).
3. SCARA (selective compliance assembly robot arm): These are stationary robots
with movable arms which perform certain functions, pick and place operations,
with a very high speed and accuracy. In the dairy industry, such types of robots
are used for picking, placing, and rearranging the milk containers (milk bottles,
5 Smart Technologies in Food Manufacturing 141

packets) according to the need in the processing line (Meshram et al. 2018; Sain
et al. 2020).
4. Palletizing robots: Palletizing robots, e.g., Kuka robots, are used for arranging
the products according to prefixed angle during packaging and labeling. These
robots are also used for palletizing operations in cold stores, freezers, removing
frosting, etc., where manual palletizing is difficult due to extreme conditions
(Abdeetedal and Kermani 2019; Sain et al. 2020).

5.5.6 Automation in Retail Milk Distribution System

In a conventional system, raw milk is often distributed at the doorstep to regular

customers either early morning or/and nighttime by the milkman and also sold at
milk stores. The selling of milk at milk stores often results in long lines hanging tight
for getting milk. To avoid the big queue for collecting milk and paying money at
milk centers, an automatic milk vending machine is one of the recent advancements
in the retail milk distribution system. Initially, the automatic milk vending machines
were integrated with sensor-based milk measuring and dispensing systems with
currency recognition and collection systems. However, later the RFID-based user-
friendly smart milk vending machines were developed for cashless transactions and
eliminate human intervention (Manmohan et al. 2019; Vijayaragavan et al. 2020).
Smart vending machine card systems are used for cashless transactions and dispens-
ing of milk. The cards used in smart vending machines are embedded with RFID
prepaid tags, which could be recharged, and an RFID reader present at the milk
vending machine detects this card identity for payment before dispensing the milk.
However, such types of milk vending systems are currently not in use due to some
associated milk safety risks and awareness among farmers and customers. Tremonte
et al. (2014) reported that the refrigeration conditions applied to milk stored in
automatic vending machines could not guarantee its microbiological safety.
Giacometti et al. (2012) highlighted that many consumers did not carry raw milk
at home in insulated bags and even transport time often exceeds 30 min. Hence after
carrying the milk at home, it must be heated or boiled for sufficient time before
consumption. Furthermore, the lack of awareness among farmers and customers,
lack of processing and marketing capacities, the difficulty of networking and col-
laboration with other key holders are also some of the major reasons for the failure of
milk vending machines (Pereira et al. 2018).

5.6 Meat, Poultry, and Seafood Industry

With increase in the population, the demand for meat and poultry has also seen a
substantial surge. Similarly, there is also an increase in seafood consumption (Abad
et al. 2009) and fish consumption which may also be attributed to their nutritional
142 R. Vashishth et al.

properties. Meat, poultry, seafood, and fishes are also quite perishable thereby
requiring newer innovative technologies to help increase their shelf-life and also to
help ramp up the production to cater to the increasing demand. To increase the
production, the manufacturers have started investing in technologically advanced
machinery due to the above-said factors and many other factors such as shortage of
labor and increasing competition among companies, etc. (Chooi et al. 2013).

5.6.1 Automation in Meat Processing

An increase as high as +25% in productivity has been observed by food industry

manufacturers when the work done by humans was replaced by using robotics (Iqbal
et al. 2017). Robots have started to be used in different areas in the meat industry
such as meat processing which includes meat cutting, animal slaughtering, and meat
selection (Chooi et al. 2013; Khan et al. 2018). In India, many types of machinery
from basic ones such as grinders, mixers, bowl choppers, tumblers, etc. to some
advanced ones such as an automatic machine for deboning, patty making machines,
multi-needle injectors, etc. are now being used (Zhang et al. 2017). Many other
automatic types of machinery such as hide pullers as well as robotic carcass splitters
are not being replaced by manual operation as they increase production (Khan et al.
2018). To determine the cutting position based on skeletal structure, imaging of
vision techniques such as X-ray, Tera-hertz scanning, ultrasound, etc. are required
(Chooi et al. 2013; Kohler et al. 2002). The production line also utilizes metal
detectors along with X-ray machines which are used to detect the nonmetallic
contamination such as bone, glass, fibers, and plastics (Caldwell et al. 2009).
Another latest trend in the manufacturing process is to coat the conveyor belt with
blue habilene. This modified polyolefin containing antimicrobial additives can be
immensely useful to prevent the development of bacterial biofilms such as staphy-
lococcus, salmonella, etc. on the conveyor belt surfaces (Mahalik and Nambiar
2010).
Certain digitized Food Waste tracking systems based on Internet of things (IoT)
have also been developed and utilized in the meat and the poultry sector which help
in tracking and reducing food wastage significantly (Jagtap and Rahimifard 2019).
Many artificial intelligence techniques have also been designed to grade eggs.
Artificial neural networks and certain algorithms have also been used to determine
egg properties such as volume, weight, and size which help in egg grading. Image
processing has also been explored in this area where digital cameras are utilized to
capture images of candled egg in a dark room and this image is further used for egg
grading (Thipakorn et al. 2017) and has also been able to detect blood spots and
cracks (Omid et al. 2013). Usui (2003) also suggested that near IR spectroscopy can
be utilized to find out blood spots in eggs as the hemoglobin particles in the blood
will have a different absorbance band and thus can be detected. Another technique
that can help detect cracks in the eggs is the acoustic impulse technique. This
technique involves the use of a pendulum that strikes the egg producing an acoustic
5 Smart Technologies in Food Manufacturing 143

signal which is received with the help of a microphone and then amplified. Digital
signal processing is then done by transferring it to a computer and further egg quality
is determined with the help of neural network or regression models (Cho et al. 2000;
Omid et al. 2013).
3DP (Three-dimensional printing) has also made its way in the meat industry.
There are generally very few cuts in the meat considered of higher quality. Small
off-cuts and trimmings are many times considered as waste or low-value products.
Hence, in order to utilize such meat cuts, 3DP which utilizes computer-aided design
(CAD) software to fabricate customized meat is gaining importance (Dick et al.
2019). As a result, many researchers are working continuously to produce even
better 3DP for these fibrous food products.

5.6.2 Automation in Safety and Quality Control

Maintenance of meat quality even during its storage has utmost importance and thus
automation can help provide a great opportunity for quality control. One relevant
method to determine the freshness of meat during storage can be by determining the
concentration of two main metabolites produced by microbial decarboxylation of
amino acids, i.e., biogenic amines and sulfurous compounds (Li and Suslick 2016;
Xiao-Wei et al. 2016). A significant measure of meat deterioration will thus be to
measure the emitted relevant volatile organic chemicals (VOC) (Liu et al. 2015;
Salinas et al. 2012). For the same, several advance analytical methods such as FT-IR
spectrometry (Chae et al. 2015; Ellis et al. 2004), GC-MS (Nicolaou et al. 2012;
Sirocchi et al. 2014), HPLC (Argyri et al. 2011), and even chemifluorescence (Gao
et al. 2016; Hu et al. 2016) have been utilized. For the same purpose, one of the latest
technologies which has come into use is the electronic nose which is an instrument
consisting of an array of chemical sensors which are capable of detecting odors
(Musatov et al. 2010; Wojnowski et al. 2017). Even biosensors play a similar role in
detecting the amines in the meat, prawn, and fish (Mello and Kubota 2002).
Indicator devices which are integrated with the smart packaging are also used.
The most common in the meat industry is time-temperature indicators which have
been used for meat, poultry, and seafood as quality indicators (Lu et al. 2013). They
provide visual information about the safety of food for consumption by irreversible
color change as they monitor and record the thermal history of a food (Ahmed et al.
2018).

5.6.3 Automation in Traceability

Many automatic identification systems are also being used these days especially for
chicken and poultry (Khashman 2012). TAG systems are being used for seeking
information such as meat origin and animal traceability which utilize the technology
144 R. Vashishth et al.

of DNA fingerprinting. At present, the most common techniques being used include
radio frequency identification system (RFID) and barcoding and utilize unique ID
tags which are printed and then attached to packaging material. These tags store and
retrieve the data using radio waves. These RFID tags consist of two parts—an
antenna that receives and transmits the radio signal and an integrated circuit which
stores and processes the RF signal (Cheruvu et al. 2008). These RFID tags have also
been useful in the fish logistic chain where they can be kept in boxes containing the
fishes. They can help detect temperatures below 0  C and the data can be read during
any time of the logistic chain without opening the boxes and they include relative
humidity as well as temperature sensing abilities (Abad et al. 2009).

5.6.4 Automation in Packaging

Automation machines are being used even for packaging. To extend the shelf-life of
meat products, many techniques such as freezing, canning, the addition of pre-
servatives, chilled storage as well as vacuum packaging/modified atmosphere pack-
aging are being used (Arvanitoyannis and Stratakos 2012). However, among all
these technologies, vacuum packaging/MAP has gained the most interest recently
with the increasing interest in minimally processed food (Paramithiotis et al. 2009).
The antimicrobial properties of CO2 help increase the shelf-life of meat. Also, MAP
is known to maintain and sometimes even help improve the color of meat which is
one important quality attribute for the consumer. The three main gases used in MAP
which are oxygen, carbon dioxide, and nitrogen can be used in the following ways—
(1) inert blanketing using N2, (2) semi-reactive blanketing using CO2/N2 or O2/CO2/
N2, or (3) fully reactive blanketing using CO2 or CO2/O2 (Arvanitoyannis and
Stratakos 2012). Indicators such as Integrity indicator which can help indicate any
leak in the package through visual colorimetric changes can be used along (Ahmed
et al. 2018).
Active packaging includes the incorporation of certain components in the pack-
age which either release certain substances into the packed food or surrounding
atmosphere or absorb certain substances from the food which is packaged or from
the surrounding atmosphere to help extend its shelf-life as well as to maintain its
safety, quality and sensorial attributes. Certain active packaging systems which find
use for meat products include moisture absorbers, carbon dioxide generators/
absorbers, antimicrobial agents as well as oxygen scavengers. Oxygen scavengers
have been found immensely useful to prevent the growth of molds as well as the
aerobic microorganism and also prevent meat discoloration by preventing oxidation
of flavors and pigments. Similarly, in order to maintain an optimum CO2 concen-
tration, CO2 absorbers/releasers are used. With the absorption of CO2, meat products
tend to exudate liquids and thus moisture absorbers such as absorber pads placed in
the package also play an important role. To impart antimicrobial properties sulfites,
nitrites, etc. are also utilized (Arvanitoyannis and Stratakos 2012).
5 Smart Technologies in Food Manufacturing 145

Smart packaging such as the use of nanotechnology in the packaging is also

drawing attention. The poisonous E. coli 0157 can be detected in meat with the use
of nanoscale silica spheres which are filled with fluorescent dye molecules. Even
Salmonella bacteria in the meat can be detected with surface chemistry skills,
immunoassay techniques as well as integrated optics incorporated in biosensors
(Mahalik 2009).
The use of robotics is also slowly making its way in food processing and among
one of the earliest uses is the packaging of meat products which is being utilized in
Western Europe to package fresh meats into trays (Mahalik 2009). Many other
machines such as wrapping machines and automated thermoform-fill-seal systems
which help wrap and label fresh meat, fish, etc. and produce skin tight packaging,
respectively (Cheruvu et al. 2008).

5.6.5 Challenges

The adoption of these automations which undoubtedly come with numerous benefits
has many constraints too, one of the biggest one being difficulty in justifying high
initial cost restricting a lot of medium-scale companies and small companies to
switch to highly efficient and automated machinery or even smart packaging with
indicators and sensors. Many other factors make the adaption of automation in food
difficult as compared to other industries such as the variable composition of food
products as well as its perishable nature. This especially holds for automation in the
meat industry as just like humans, every carcass varies in size and shape (Templer
et al. 2012). Technical reasons such as lack of skilled technical personnel along with
certain other factors such as time, management commitment, and cost also hold food
manufacturers aback. In a survey by Ilyukhin et al. (2001), time and cost were
identified as the biggest obstacles for adopting newer technologies for small pro-
duction plants whereas cost along with management commitment was the biggest
obstacles for larger production plants. Another challenge remains to select the best-
fit technology for your industry.
The responsive surfaces of these in touch with the food material also have to be
completely inert to food and non-toxic which again rules out the possibility of using
some possible options such as sensing devices based on metal or carbon
nanoparticles (Muncke 2014; Parisi et al. 2015).
The use of biosensors and e-nose possess a number of challenges such as the
results being affected by the ambient gas as well as due to sensitivity of these sensors
to changes in the relative humidity and temperature. Electronic noses based on mass
spectroscopy can be used to help overcome this problem. However, other than this, a
few more disadvantages with the use of e-nose persist requirement of frequent
calibration, consumption of high power, insufficient stability in measurement, as
well as changes in the chemical sensor’s response signal becoming less reliable with
the passage of time (Wojnowski et al. 2017).
146 R. Vashishth et al.

Barcodes also possess problems as they can provide inaccurate data due to
reasons such as mist, dirt, and out of line-of-sight. Hence, RFID tags are becoming
more popular as they can withstand harsh conditions (Mahalik 2009). However,
problems such as lack of standardization and cost of tag remain an obstacle (Abad
et al. 2009).
Egg graders based on artificial intelligence techniques also possess certain errors
such as 100% accuracy is rare and a certain percentage of eggs may be misgraded,
detection may be difficult for brown eggs, etc. (Omid et al. 2013). All these also
involve the use of complex mathematical models, digital cameras, computers, and
neural networks; one needs to have proper knowledge of all the related technical
needs.
3DP, though an emerging and interesting automation, comes with its drawback
when it comes to meat and meat products. Meat is a non-printable fibrous material in
nature (Liu et al. 2018) and thus requires prior modification in its mechanical as well
as rheological properties. Hence, in order to convert it into extrudable flow-like
material, the addition of certain flow enhancers such as gelatin solution is required
(Dick et al. 2019).
These problems, however, can be overcome by more studies and research. Once
the problems are overcome, these techniques will surely help in the advancement of
the meat industry and help to meet increasingly higher demands in the most
efficient way.

5.7 Beverage Industry

The beverage industry is growing continuously across the world with increasing
demand for convenient foods and an increasing population. The beverage industry is
a subset of the food industry (Guimarães et al. 2012) and comes under the FMCG,
i.e., the fast-moving consumer goods category. The increasing demand is common to
all the beverages such as tea (Majuder et al. 2010), coffee (Murthy and Naidu 2012),
fruit juices (Priyadarshini and Priyadarshini 2018), alcoholic beverages (Jernigan
2009), milk (Douphrate et al. 2013), etc. Numerous factors such as efficient use of
resources and energy (Otles and Sakalli 2019), lack of labor (Paraschos et al. 2013),
cost reduction, hygienic production, high productivity, etc. accelerate the use of
automation and modernized machinery in the beverage industry. As per the 2015
executive summary of World robotics, there was a tremendous rise of 27% in robot
order for the food and beverage industry in the year 2015 (Khan et al. 2018). Thus,
the latest industrial revolution, i.e., Industry 4.0 is making its way even in the
beverage industry. This is promoted by the reduction in the cost of RFID, wireless
communication, sensor networks, NFC (near field communication), and applications
making their utilization more accessible in the beverage industry (Otles and Sakalli
2019).
5 Smart Technologies in Food Manufacturing 147

5.7.1 Automation in Beverage Processing

One of the biggest shifts in automation and advancement is observed in the dairy
sector. A new innovative technology such as robotic milking to automatic feeding
systems is now being utilized. Electronic cow identification along with temperature,
movement, and location sensors can help provide important information such as the
reproductive health of the animal, the amount of feed to be given, etc. Automation is
also finding its way for tea plucking as it is a tedious, labor-intensive, and time-
consuming task; the possibility of using an automatic harvesting system that can
maintain the same level of quality as manual picking is continuously being explored.
Hence, robots are being trained for the Oritsumi technique of leaf picking, i.e., using
the thumb along with the forefinger to grasp the leaf and pluck the leaf while rotating
the hand (Paraschos et al. 2013).
The production process of both soft drinks and beer shares few common features.
Specialized mixing tanks are used along with many other sophisticated types of
machinery which have different functions such as getting the perfect recipe with the
controlled flow of ingredients, carbonation machines, etc. Towards the end, a filling
line consisting of series of machines and conveyor belt performs all other function
such as washing as well as disinfecting the containers followed by filling, sealing,
and labeling the soft drinks or beer packed in glass bottles, PET bottles or cans of
different sizes (Guimarães et al. 2012; Tsarouhas and Arvanitoyannis 2010).
IoT, i.e., Internet of things-based systems also find use in different areas of the
processing to collect, store, handle, and analyze data. One such example can be a
reduction of energy wastage in a beverage company by using IoT based smart energy
systems (Jagtap et al. 2019). Even when it comes to alcoholic beverages such as
beer, technology is now being used at almost all steps such as malting, fermentation,
etc. Advanced computer vision, image analysis techniques, IoT at various steps,
sensors, etc. are being utilized (Violino et al. 2020). MRI, i.e., magnetic resonance
imaging technique is also useful in determining characteristics of concentrates,
pastes as well as fluids (Mahalik 2009).
Biosensors also play a crucial role in the beverage industry in numerous areas.
They can help detect acetaldehyde glycerol and aldehyde for monitoring fermenta-
tion of alcoholic beverages, ethanol content of beer and wine, lactose content in
milk, fructose in milk, wine, juice, and cold drinks, glucose in soft drinks, milk,
juice, wine, etc. (Mello and Kubota 2002).
Factors such as the type of coffee, its cultivation place, etc. can all affect the taste
and sensory properties of the coffee. Electronic noses or sensors can be integrated
into the humanoid robots making it possible for them not only for the production but
also for quality control of coffee. This method is called coffee-cupping and it
involves the use of ANN, i.e., artificial neural network. It can even give information
regarding the acidity level of coffee and can even take into account the effect of
temperature on volatiles and thus coffee odors (Thazin et al. 2018).
148 R. Vashishth et al.

5.7.2 Automation in Quality Control

Automation finds numerous uses in quality control of products as well. X-ray

technology can be used with a multi-layer detection algorithm to detect contaminants
such as metal, stone, etc. in beverages packed in metal cans, plastic or glass in order.
Biosensors can help sense penicillin in milk trucks while it is heading to the dairy
processing unit (Mahalik and Nambiar 2010). Optimal immune-biosensors capable
of detecting streptomycin residues (antibiotic compound) in milk are also used
(Baxter et al. 2001). Many other sensors have been used in order to detect antibiotic
and pesticide in milk, pesticide in juices, oxalate in tea, phosphate in drinking water,
sulfite, and glycerol in wine, polyphenol in wine and green tea, ethanol in beer and
wine, citric acid in fruit juices and sports drink, etc. Biosensors also find a huge
application in monitoring aroma in alcoholic beverages such as brandies, gin, wine,
etc. (Mello and Kubota 2002).
The growth of microorganisms in milk can lead to production of metabolites
causing sensory alteration in the milk such ass off-flavor and odor. These sensory
alterations can be used to detect and enumerate microbial spoilage by many tech-
niques such as electrical and microscopy methods, nucleic acid probing, polymerase
chain reaction, immune assays, ATP bioluminescence as well as electronic nose.
Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry
(MALDI-TOF-MS) is yet another technique that helps quantify bacterial contami-
nation of milk (Nicolaou et al. 2012). The temperature of milk plays an important
role in maintaining its acidity and thus quality. Therefore, indicators such as time-
temperature indicators (TTIs) play an important role in many beverages especially in
milk to detect the quality (Lu et al. 2013).

5.7.3 Automation in Traceability

When it comes to traceability, barcoding and RFID tags play an important role for
beverages. RFID tags are, however, advantageous over barcoding as they can be
read automatically using sensors (Mahalik and Nambiar 2010). Mu-chip tags which
are the world’s smallest RFID tag (50 mm) help guarantee the genuineness of
valuable products such as wine or liquor which are normally open to counterfeit
abuse. This is done by integrating the mu-chip in the seal cap, where a reader can
read the tag and ensure that no tampering has been done with the bottle’s content.
Also, the removal of the seal cap leads to breakage of the antenna, and the chip’s
unique ID number which is stored in the ROM, cannot be read again. This helps in
preventing the reuse of the bottle once emptied as well (Jones 2006).
5 Smart Technologies in Food Manufacturing 149

5.7.4 Automation in Packaging

Robots also find use in the packaging, form-fill-seal machines are utilized for
packaging milk in LDPE pouches, capping machines which are capable of capping
200 plastic or glass bottles per minute, automatic cartoning machines which can
produce different size boxes, automatic labeling machines (Cheruvu et al. 2008), etc.
are all used immensely in the food beverage sector. Cleaning, counting, filling as
well as arranging of the bottles on the conveyor belt are all performed by automatic
robots in the beverage industry (Iqbal et al. 2017), e.g., beer, flavored milk, etc.
Palletizing robots that can accurately and precisely help in loading and unloading
objects are also used and are ideal to be used even in cramped spaces and can handle
up to four production lines and multiple products simultaneously. X-rays are used
with an artificial neural network for recognition of shape which helps find under-
filled as well as defective packs (Mahalik 2009).

5.7.5 Challenges

In a survey by Drewry et al. (2019), it was found that as low as 1.5% of the surveyed
dairy farm workers were using robotic milking machines, pointing that many barriers
still exist in adapting this automated machinery despite their numerous advantages.
The reasons identified for the same were privacy/security concerns (61%), ability to
keep up with technology change (55%), lack of comfort with technology (54.5%),
and many other reasons such as poor infrastructure, lack of interest, etc. along with
demographic factors such as age, finance, etc. One more source reveals that the
concept of Industry 4.0 is still not known to around 94% of the manufacturers
pointing clearly towards the need to increase awareness regarding the same (Otles
and Sakalli 2019). Small food manufacturers often lack the funds and employees to
switch to automation and digitization.
Techniques such as ATP bioluminescence, polymerase chain reaction, etc. have
slow sample turnaround times and require skilled labor which limits their use
(Nicolaou et al. 2012).
The issue with using robots such as the ability of the robot to work in wet
environment, complexity, non-corrosive nature, and washable when it comes to
coming in direct contact with food are few constraints (Khan et al. 2018). Another
example here can be training robots for tea plucking where factors such as maturity
of leaf, petiole, branch stiffness, etc. will affect the amount of the plucking motion
required and the time of rotation of leaf and thus will require advanced systems such
as Probable Movement Primitives (ProMPs) for acquiring the same (Paraschos et al.
2013). Moreover, robots work as per predestined functions and they lack the
decision-making ability which is needed under certain circumstances.
Other challenges continue to hold food manufacturers aback from upgrading
completely to Industry 4.0. Problems such as lack of proper infrastructure and
150 R. Vashishth et al.

internet availability in all the areas allowing to utilize IoT and related techniques
truly as they are meant “anywhere, anytime” still is somewhat far and continuous
up-gradation for the same is required. However, a lot of advancement in automation
has taken place in the beverage industry. Overcoming the present remaining chal-
lenges thereby being able to use the technology and machinery to their full potential
in order to increase production to meet the rising demand of the increasing popula-
tion and for many other numerous benefits they offer is the current challenge
and need.

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Chapter 6
Non-thermal Food Preservation
Technologies

Ravneet Kaur, Shubhra Shekhar, Sahil Chaudhary, Barinderjit Singh,

and Kamlesh Prasad

Abstract Recent food processing trends and preservation technology mainly focus
on retaining freshness and minimizing nutritional and sensory losses during
processing. Conventional processing techniques involve high temperature (thermal
processing) for microbial inactivation and food preservation. Exposure to high-
temperature results in the loss of heat-sensitive nutritional components and affects
textural and sensory characteristics of foods. Therefore, to obtain high-quality
minimally processed food products, non-thermal techniques are found to be better.
Standard non-thermal preservation techniques include high-pressure processing,
pulsed electric field, cold plasma, supercritical carbon dioxide, irradiation, and
ultrasound. This chapter focuses mainly on the principles, processing, and applica-
tion of non-thermal techniques in food preservation.

Keywords Non-thermal food preservation · High-pressure processing · Pulsed

electric field · Cold plasma · Supercritical carbon dioxide · Irradiation · Ultrasound

6.1 Introduction

Food preservation, safety, and quality are the significant goals of food processing
industries to meet consumer demand as per the recent trends. Commonly used
traditional food processing techniques involve thermal treatment for improving the
production rates and shelf-life extension. Thermal processing is required to get the
desired characteristics in processed food products but involves higher temperature

R. Kaur · K. Prasad (*)

Department of Food Engineering and Technology, SLIET, Longowal, Punjab, India
S. Shekhar
Department of Food Process Engineering, National Institute of Technology, Rourkela, Odisha,
India
S. Chaudhary · B. Singh
Department of Food Science and Technology, I. K. Gujral Punjab Technical University,
Kapurthala, Punjab, India

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 157
S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies,
https://doi.org/10.1007/978-981-19-1746-2_6
158 R. Kaur et al.

exposure, which leads to the loss of various volatile components and nutritional
properties (Singh and Prasad 2012; Kumar and Prasad 2018; Kaur and Prasad
2021a, b). Therefore, to meet the consumer requirement for minimally processed
and fresh food products, the focus is on utilizing processing techniques that can help
to retain maximum nutritional and textural properties of food products during
processing (Pereira and Vicente 2010).
Recently, there has been an increased focus on non-thermal technologies for the
processing and preservation of food products due to their economic benefits. They
are more energy-efficient, have minimal impact on nutritional and sensory attributes
of food products than conventional techniques, and improve shelf-life (Knorr et al.
2011; Morris et al. 2007). In addition, novel non-thermal technologies also offer
environmental sustainability and play a vital role in food security by minimizing the
energy and water requirement, thus reducing the water and carbon footprint
(Khouryieh 2021; Knoerzer et al. 2015).
Major non-thermal food processing/preservation technologies include high-
pressure processing (HPP), pulsed electric field (PEF), cold plasma, supercritical
carbon dioxide, irradiation, and ultrasound. Low power ultrasound waves are com-
monly used for analytical purposes, whereas high power ultrasound waves are used
for processing purposes (Prasad 2015a, b). These technologies do not involve direct
heat treatment but may lead to an increase in the thermal energy of the food product.
Thus, these may also be described as indirect thermal energy input processing (Ezeh
et al. 2018).
Besides the benefits of preventing thermal degradation linked with thermal
processing, they are also responsible for destroying the spoilage-causing microbes
by disrupting the cell membrane structure or destroying the genetic material within
the cell. These techniques are also used for various other unit operations like
freezing, drying, emulsification, sterilization, and extraction (Zhang et al. 2019;
Sharma et al. 2015). Each technology has a different mechanism that determines
its effect on food structure and quality. A combination of two or more techniques
may also be simultaneously used to improve the effectiveness of the treatment. This
chapter describes the mechanism of different non thermal processing techniques, its
effect on food quality, and its role in food preservation.

6.2 High-Pressure Processing (HPP)

High-pressure processing is a promising novel non-thermal processing and preser-

vation technique used for application in the food industry. It involves the application
of high pressure instead of heat to inactivate or destroy spoilage-causing microor-
ganisms. It is also known as high hydrostatic pressure (HHP) processing and
ultrahigh pressure (UHP) processing.
The first application of HPP was carried out to process milk to reduce the
microbial load and increase the shelf life (Hite 1899). Milk was exposed to
600 MPa of pressure for 1 h, resulting in the delayed souring of milk. Afterward,
6 Non-thermal Food Preservation Technologies 159

the application of HPP for processing juices, milk, meat, and egg albumen was also
studied by various researchers (Rendueles et al. 2011). In this technique, the food
products are exposed to pressure from 100 to 1000 MPa. Water is commonly used as
a medium for force transmitting for a specified time interval that can vary from a few
seconds to minutes depending on the type of food material. Apart from processing
and preservation, HPP can also be used for textural changes in various fruits,
vegetables, and meat products. It is also helpful for carrying out the reactions
where modification of chemical structures must be carried out (Norton and Sun
2008; Tao et al. 2012).

6.2.1 Principles of HPP

High-pressure Processing governs mainly on Le Chatelier’s principle and the Iso-

static/Isobaric principle. According to Le Chatelier’s principle, the alteration in
pressure at constant temperature leads to shifting the equilibrium towards the state
that favors reducing volume. It is also known as equilibrium law. Due to high
pressure, the volume for the same mass is reduced due to molecular ordering. Strong
covalent bonds are not affected by HPP at low temperatures, but the damage is
caused to the weaker bonds. Due to this reason, the quaternary and tertiary protein
structures are affected by HPP, and there is no effect on primary and secondary
structures (Muredzi 2012).
The isobaric principle states that pressure is distributed uniformly over the
product irrespective of its shape and size (Torres and Velazquez 2005). On the
other hand, in the case of thermal treatment, heat transfer occurs through different
modes: conduction, convection, and radiation. For products containing high mois-
ture content, the pressure does not impart any changes in the structural configuration
of the product. In contrast, the large air spaces products get deformed during
processing due to compressibility differences (Orellana et al. 2017). Therefore,
HPP’s effectiveness depends on pressure, holding time, and resistance of microbes
towards high pressure. Some microorganisms are more resistant to high pressure
than others because of differences in cell structures (Rendueles et al. 2011).

6.2.2 High-Pressure System and Processing

HPP system comprises the following parts (Hokmollahi and Ehsani 2017; Rastogi
and Knorr 2013):
• Treatment chamber.
• Pressure generating system/pressure intensifier.
• Process control system.
160 R. Kaur et al.

Fig. 6.1 Schematic diagram of high-pressure processing system

• Pressure and temperature monitoring system.

• Temperature measurement system.
High-pressure processing can be carried out in batch or semi-continuous systems.
Batch systems are mostly used for solid as well as liquid foods. Food is packaged in
flexible packaging material and loaded in the pressure chamber, and the vessel is
closed during the process (Fig. 6.1). The pressure transmitting medium is then filled
inside the chamber for pressurization. Pressure is applied either by reducing the
volume of the pressure vessel using a piston or by pumping more of the pressure
transmitting medium into the vessel. After the desired pressure is achieved, it is
maintained for the specified time, and this step is known as holding. After the
completion of holding time, depressurization is carried out, and unloading of
products is done (Balasubramaniam et al. 2008). Thus one “cycle” means the total
time utilized for pressurization, holding, and depressurization.
The most used pressure transmission medium is water due to its non-toxicity and
low cost. However, some other media such as silicon oil, castor oil, corn oil, ethanol,
and glycol blends are sometimes used for anti-corrosion and lubrication purposes.
Different fluids have different compression heating values depending on the physical
and thermal properties such as viscosity, specific heat, and compressibility. This
affects the heat transfer across the fluid and the food product (Balasubramanian and
Balasubramaniam 2003). However, during the process, the pressure is transmitted
simultaneously and uniformly in all directions, so there is no effect on structural
6 Non-thermal Food Preservation Technologies 161

properties. Also, the sensory and nutritional characteristics of food are retained due
to minimal thermal effects.
Semi-continuous systems are used mainly for liquid food products that can be
easily pumped like juices. In this system, three pressure vessels are connected. When
one vessel is unloaded, the second vessel is under pressurization or compression, and
the third vessel is loaded so that a continuous output is obtained. The unloaded or
discharged product can be pumped aseptically into the filling line for packaging
(Ting and Marshall 2002). A commercial semi-continuous HPP system is being used
for grapefruit juice processing in Japan. It has a processing capacity of 600 L/h at a
maximum pressure of 400 MPa (Palou et al. 2002).

6.2.3 Role of HPP in Microbial Inactivation and Food

Preservation

High-pressure processing is used as a preservation technique as it can inactivate

spoilage-causing and pathogenic microorganisms, thus improving the shelf life and
safety of food products (McClements et al. 2001). Moderate pressure level generally
decreases the growth rate of microorganisms by decreasing their reproduction rate,
whereas for complete microbial inactivation, a higher level of pressure is required
(Bajovic et al. 2012). The effectiveness of the process depends not only on the
process parameters but also on the characteristics of microorganisms and the food
matrix (Table 6.1). This is because some of the microbes are more susceptible to
high pressure as compared to others. For example, bacterial spores are more resistant
to high pressure than vegetative cells; eukaryotic cells are more sensitive to high
pressure than prokaryotic cells. Thus, bacterial cells are more resistant to high
pressure than yeast and molds (McClements et al. 2001). Thick peptidoglycan
layer in the gram-positive bacterial cell wall provides more resistance to high
pressure than gram-negative bacteria (Considine et al. 2008). Resistance of the
microbial cells depends on the growth phase of the cell; microbial cells are more
resistant to physical changes in their stationary phase than the exponential phase
(Daryaei et al. 2016).
The mechanism of microbial inactivation by HPP or sterilization is mainly due to
the disruption of cell structure. Pressure treatment results in significant injury to the
microbial cell membrane, which increases its permeability (Casadei et al. 2002). The
disruption of the cellular membrane leads to the outflow of internal cellular constit-
uents (Abe 2007). Disruption of cell membrane and alteration of its permeability due
to pressurization leads to the efflux of ribosomes, nuclear material, and internal
solutes leading to cell death. If the pressure applied is not sufficient, it may lead to
reversible changes in cell structure that may recover after the storage of food for
1–15 days (Koseki and Yamamoto 2014). Irreversible changes in cell structure occur
at higher pressure, where membrane permeability becomes the major reason for cell
destruction. Pressure treatment at 500 MPa for 30 min at 25  C induced the
162 R. Kaur et al.

Table 6.1 Non-thermal processing methods, application, and possible effects

Methods Application Effects Reference
High pres- Drying of fermented – Increased inactivation of E. coli. Balamurugan
sure sausages – Reduction in time required for 5 log et al. (2020)
processing E. coli inactivation.
Carrot juice – Highest enzymatic inactivation of Szczepańska
processing PPO (polyphenol oxidase) and perox- et al. (2020)
idase (POD).
– Enhanced total phenolic content.
Cheese – Accelerating or arresting of cheesed Nuñez et al.
ripening enzymes can be controlled. (2020)
– Elimination of pathogenic bacteria.
Processing of cate- – Nano-encapsulated catechin was Ruengdech
chin fortified coconut used to stabilize catechin during HPP. and
milk – Enhanced antioxidant activity and Siripatrawan
shelf life. (2021)
Pulsed elec- Pre-treatment of – Reduced oil content of chips. Zhang et al.
tric field potato chips before – Improved textural properties, (2021)
frying mainly crispiness and hardness.
PEF assisted osmotic – Used as a pre-treatment for osmotic Parniakov
dehydration of apple dehydration and its impact on freezing et al. (2016)
slices and thawing.
– Acceleration of mass transfer
properties.
– Reduced freezing time
Pretreatment of wheat – Increased water uptake Ahmed et al.
seeds to improve – Increased antioxidant properties of (2020)
germination. plantlet juice from PEF treated seeds.
Beef – Increased protein digestibility of PEF Bhat et al.
treated cooked beef (2019)
– Higher values of soluble protein.
Cold Plasma Pretreatment for dry- – Increased effective diffusivity and Bao et al.
ing of jujube slices drying rate due to formation of intra- (2021)
cellular cavities on treatment with cold
plasma.
– Prevents degradation of
antioxidants.
Fortification of rice – Results in etching of rice surface, Akasapu et al.
with iron thus better penetration of iron. (2020)
– Increased bioavailability of iron
– Reduced cooking time
Microbial decontami- – Reduction in spoilage causing Kumar
nation of fresh cut microflora Mahnot et al.
carrots – Maximum quality retention (2020)
Coconut water – Reduced enzymatic activity Porto et al.
– Increased phytochemical content (2020)
(continued)
6 Non-thermal Food Preservation Technologies 163

Table 6.1 (continued)

Methods Application Effects Reference
Supercritical Pomegranate juice – Pasteurization of pomegranate juice González-
carbon using SC-CO2 Alonso et al.
dioxide – Increased microbiological stability (2020)
and color stability
Pasteurization of lipid – SC-CO2 combined with high power Gomez-
emulsions ultrasound was used for pasteurization. Gomez et al.
– Increased activation capacity (2020)
Extraction of oil from Better extraction yield of polar lipid Arturo-
passion fruit and fraction due to improved solubility Perdomo et al.
blackberry seed (2021)
Irradiation Microbial – Gamma irradiation treatment of pis- Song et al.
inactivation tachios at 0.5 kGy, 1.0 kGy, and (2019)
3.0 kGy reduced E. coli O157:H7 by
0.99 log CFU/g, 1.50 log CFU/g, and
4.32 log CFU/g, respectively.
– Maximum quality retention at
3.0 kGy.
Enzymatic – Electron beam irradiation curbed Duan et al.
inactivation down polyphenol oxidase activity of (2010)
white button mushroom compared to
that in control samples after 10 days of
storage
Quality and shelf-life – X-rays (2 kGy) effectively increased Ricciardi et al.
enhancement shelf-life of ricotta cheese by up to (2019)
20 days, compared with no treatments
lasting for only 3 days
Sprouting inhibition – Electron beam irradiation of five Etemadinasab
different potato cultivars suppressed et al. (2020)
sprouting and decreased loss in weight
and firmness, when stored at 10  C for
180 days
Ultrasound Microbial and enzy- – Microbial inactivation in pear juice Saeeduddin
matic inactivation with sonication (65  C for 10 min) et al. (2015)
owing to acoustic cavitation
phenomenon
– Significant reduction in residual
activity of POD, PME and PPO as
4.3%, 3.25%, and 1.91%, respectively
Emulsification – Fabricated geraniol and carvacrol Syed and
loaded emulsions with increased Sarkar (2018)
solubility
– Stable emulsion formation
– Enhanced antimicrobial efficacy
Drying pre-treatment – The drying time for apple reduced by Fijalkowska
13–17% et al. (2016)
(continued)
164 R. Kaur et al.

Table 6.1 (continued)

Methods Application Effects Reference
Mass transfer – Improved brining efficiency of pork Ozuna et al.
acceleration meat due to bubble implosion near the (2013)
surface producing violent micro-jets
improving mass transfer.
Extraction – Ultrasound assisted pectin extraction Wang et al.
yield from grapefruit peel reported (2015)
higher (16.34%) compared to chemical
extraction
– Extraction time reduction by
37.78% using UAE
Meat tenderization – Ultrasound treatment (300–600 W at Li et al. (2018)
40 kHz) for 30 min dispensed better
tenderization of goose meat
– 600 W treatment displayed lowest
shear force and cooking loss

inactivation of E. coli, S, aureus, Salmonella, and Shigella in milk caused by

breakdown of the peptidoglycan layer of cell membrane (Yang et al. 2012).
High pressure also results in conformational changes in ribosomes, cytoplasmic
proteins, and nucleic acid. It is found to directly correlate with decreased viable cells
of E. coli, as observed using scanning electron micrographs (Niven et al. 1999).
Other than cell membrane, proteins and enzymes are also targeted by HPP respon-
sible for microbial inactivation (Ulmer et al. 2000). High pressure results in irre-
versible aggregation of proteins due to denaturation and structural unfolding at a
pressure above 300 MPa (Kalagatur et al. 2018; Abe 2007). Damage to the cell
cytoplasm and mitochondria is caused at a pressure around 400–600 MPa (Smelt
1998). Inactivation of enzyme activity due to the pressure leads to reduced ribosome
synthesis, which plays a vital role in replicating and transcribing genetic material
(Lakshmanan et al. 2005).
Microbial inactivation by HPP can be described by kinetic parameters like those
used for thermal processing. These include (1) D-value, which describes the time
taken to reduce the microbial load by one log cycle at constant pressure and
temperature and is known as “decimal reduction time,” (2) ZP or pressure resistance
constant that defines the increase in pressure at the constant temperature required for
90% reduction in the D-value, and (3) ZT or thermal resistance constant, that means
an increase in temperature at constant pressure required for 90% decrease in D-value
(Tsevdou et al. 2019).
Inactivation of bacterial spores follows a slightly different mechanism than
vegetative cells due to the presence of multiple protective layers, inactive metabo-
lism, and low water content in case of spores that make them resistant towards high
pressure (Georget et al. 2015). Therefore, as compared to vegetative cells, the
inactivation of bacterial spores requires two processing steps. The first step is carried
out to achieve germination of spores, and it mainly involves the treatment at pressure
up to 600 MPa in combination with temperature up to 60  C. Then, in the second
6 Non-thermal Food Preservation Technologies 165

step, inactivation of germinated spores is achieved using pressure above 600 MPa,
reaching a maximum up to 1200 MPa and temperature range from 60 to 120  C.
Other than increasing the safety and shelf life of food products, HPP also
improves product quality. Bioactive components of grape pomace, such as antho-
cyanins, proanthocyanidins, and soluble dietary fiber, are found to improve during
processing at 200 MPa (Sheng et al. 2017). Pressure treatment between 400 and
600 MPa in cheese manufacturing increases the yield, promotes curd formation,
reduces rennet coagulation time, and accelerates proteolysis at the time of ripening
(Hokmollahi and Ehsani 2017). HPP at low temperature does not affect the ascorbic
acid content but decreases if the processing is carried out at a higher temperature.
During storage of HP treated orange juice at refrigeration temperature, the ascorbic
acid loss rate was less than conventionally processed juice (Polydera et al. 2005).

6.3 Pulsed Electric Field (PEF)

A pulsed electric field (PEF) is a non-thermal processing technique that involves

applying an electric field of high voltage for a short interval of time to the product.
Usually, the electrical pulses of voltage ranging from 10 to 80 kV are used. PEF is an
excellent technique for microbial decontamination and enzyme inactivation. It also
improves the mass transfer phenomenon during drying operations (Toepfl et al.
2006; Onwude et al. 2017). It offers several advantages over conventional thermal
processing treatments, such as better color, flavor, and nutrient retention, reduction
in pathogenic microbes, and improved shelf life (Stoica et al. 2011). It is mainly used
to pasteurize liquid and semi-liquid foods (Soliva-Fortuny et al. 2009). Commercial
application of PEF for pasteurization of fruit juice has been made in the USA by
Genesis juices. In addition, modification of food proteins can be carried out using
PEF to improve their stability and functionality (Zhao et al. 2012). Besides food
processing, PEF is also applied in biotechnology and genetic engineering for electro-
fusion and cell hybridization (Chang et al. 1992).

6.3.1 Principle of PEF

High-intensity pulsed electric fields ranging from 10 to 80 kV are used for micro to
milliseconds. The food product is placed between the two electrodes and exposed to
high-intensity pulses. The gap between the electrodes where the product is placed is
known as the “Treatment gap.” The electrodes are connected using a non-conducting
material. Depending on the processing requirement, the process can be carried out at
room temperature or low or high temperatures (Zimmermann and Benz 1980).
The transfer of electrical pulses through the food is based on the fact that food
contains ions responsible for the electrical conductivity of food. Due to the presence
of charged molecules, the transfer of pulses is relatively more effortless in liquid
166 R. Kaur et al.

foods (Zhang et al. 1995). It can be effectively used for the pasteurization of milk,
juices, yoghurt, and liquid eggs (Bendicho et al. 2003). Several events occur during
food exposure to PEF treatment, including resistant heating, perturbation of cell
membranes (Sitzmann 1995), and electrolysis (Hülsheger and Niemann 1980).
Electroporation of microorganisms is a significant phenomenon that takes place
during the PEF treatment that is responsible for the inactivation of microbes. PEF
also finds its application in enzyme inactivation and extraction of several compo-
nents and can also be used as a pre-processing technique for unit operations like
drying. It causes minimal changes to the nutritional and sensory characteristics of
foods.

6.3.2 Equipment and Process Design

PEF treatment system includes the following:

• Pulse power generation system.
• Temperature control system.
• Material transport system
• Treatment chamber.
• Operating system.
Pulse generation system is used to convert alternating current (AC) into direct
current (DC). The capacitor stores energy and a switch are used to control the
discharge of electrical energy into the chamber (Fig. 6.2). The type of wave or
shape of the pulse may vary depending on the configuration of the discharge circuit
or pulse forming network (PFN). It can be a short square wave, exponential, or

Fig. 6.2 Diagrammatic representation of pulsed electric field food processing system
6 Non-thermal Food Preservation Technologies 167

sinusoidal wave (Morris et al. 2007). Square wave and exponential decay pulses are
commonly used. There is an increase in voltage and current intensity in the expo-
nential decay pulse form that reaches up to a maximum. Then there is a slow decay
approaching zero, resulting in a low electric field. Thus, the sample is exposed to a
spectrum of electric fields rather than a constant electric field. On the other hand, a
continuous voltage peak is maintained throughout the pulse duration in the case of
the square wave (Wouters et al. 2001).
The electric field strength (kV/cm) is defined by the voltage delivered per unit
distance between the electrodes in a treatment chamber. The pulse width of expo-
nential decay pulses is described as the time taken by the input voltage to decay up to
37% of its maximum value. The numbers of pulses applied per unit time are
described as frequency (Hz). Specific energy of the pulse is reported as kJ/kg
depends mainly on voltage and pulse width and geometry and conductivity of
material responsible for the resistance of the treatment chamber (Wouters et al.
2001; Zhang et al. 1995).

6.3.3 PEF for Food Processing and Preservation

The mechanism of microbial inactivation by PEF is based on electroporation. The

cell membrane breakdown due to a high-intensity electric field creates pores in the
cell structure (Simonis et al. 2019). Various theories govern the structural changes of
the cell membrane during PEF treatment, based on electric field-induced tension,
trans-membrane potential, electromechanical compression, and osmotic imbalance
(Toepfl et al. 2014).
During the initial phase of PEF treatment, there are no changes in the cell
membrane. However, as the intensity increases, there is reversible permeabilization
of the cell membrane, just below the critical level of the electric field. Furthermore,
an increase in intensity beyond the critical level leads to irreversible electroporation
of microbial cell membranes (Martin-Belloso and Elez-Martínez 2005).
Dielectric rupture theory was developed to explain the phenomenon of electro-
poration. It was based on equivalent circuit models and physical properties of cells,
where the cell membrane was designated as dielectric capacitor and suspension of
cells was considered as resistors and capacitors (Sale and Hamilton 1968; Zimmer-
mann 1986). According to this theory, the transmembrane potential that occurs
across the cell membrane increases due to an external electric field, and there is a
reduction in membrane thickness. The membrane breakdown occurs when there is
an increase in electro compression compared to restoring viscoelastic forces across
the membrane.
According to the dynamic model for explaining pore formation, it was described
that in order to expand the pores to critical diameter and become irreversible, a
sufficient amount of critical voltage needs to be applied (Schoenbach et al. 2000).
The cell membrane has a lipid bilayer structure. It has its net electric charge. The
conformational changes occur in the lipid molecules due to the electric field that
168 R. Kaur et al.

further expands existing pores and creates new pores (Jeyamkondan et al. 1999;
Tsong 1989). Protein channels are also present in the cell membranes that are
affected by the electric field. Irreversible denaturation of protein channels occurs
due to applying a high-intensity electric field (Tsong 1989).
Eukaryotic cells require less intensity of electric field to cause irreversible
changes than prokaryotes (Heinz et al. 2001). Bacterial spores are highly resistant
to PEF compared to vegetative cells, mainly due to a thicker outer cortex and their
dehydrated state (Barbosa-Cánovas and Altunakar 2006). PEF can be combined with
other preservation techniques to improve its effectiveness for microbial inactivation.
For example, when the inlet temperature of sample was kept 10  C, there was 0.9 log
cycle reduction of E. coli at 30 kV/cm, whereas on increasing the inlet temperature to
30  C, there was 1.7 log cycle reduction (Aronsson and Rönner 2001).
Application of exponential pulse with 20 kV/cm electric field strength at the rate
of 10 pulses of 30 μs pulse width led to a reduction in 3 log cycles of E. coli. No
lethal effect on E. coli was observed when electric field strength below the threshold
value of 3 kV/cm was applied (Evrendilek and Zhang 2005). Application of PEF for
apple juice treatment resulted in an increase in the diffusion coefficient of soluble
solids (Jemai and Vorobiev 2002).
PEF treatment increases the ascorbic acid retention in carrot-orange juice during
storage. For example, treatment of carrot-orange juice at an electric field strength of
25 kV/cm for a pulse width of 280 and 330 μs resulted in 90% retention of ascorbic
acid, whereas 83% for thermally pasteurized juice (Torregrosa et al. 2006).
PEF also finds its application in enzyme inactivation. It results in changes in the
conformation of the tertiary structure of the enzyme. Pectin methyl esterase (PME) is
the enzyme that leads to the degradation of pectin in citrus juices and thus reduces
the cloudiness and viscosity of juice. Therefore, the treatment of citrus juice with
PEF leads to the inactivation of PME and helps maintain cloud stability and viscosity
(Aguilo-Aguayo et al. 2009).
This technique can also be effectively used for the modification of structural and
functional properties of various macromolecules such as starch and protein (Hong
et al. 2016). It can also be used for improving the extraction process from the plant
by-products such as bioactive components and pigments (Azmir et al. 2013;
Boussetta and Vorobiev 2014).

6.4 Cold Plasma

The term “Plasma” was described in 1928 by the chemist Irving Langmuir. It is a
Greek word that means “moldable substances” (Rajvanshi 2008). Plasma is the
fourth state of matter known after three distinct forms: solid, liquid, and gas. There
is a change in state from solid to liquid and liquid to gas on increasing the energy.
Furthermore, an increase in energy of the gaseous state beyond a specific limit leads
to ionization of molecules and yields the fourth state of matter called plasma (Luo
et al. 1998). Plasma is an ionized gas composed mainly of reactive species such as
6 Non-thermal Food Preservation Technologies 169

electrons, protons, neutrons, and various ions such as hydroxyl ions, atomic oxygen,
and nitrogen species (Misra et al. 2016).
Cold plasma is one of the recent technologies that find its application in food
preservation and safety. The generation of cold plasma is mainly carried out by
applying electric current to the mixture of gases or a pure gas, which results in the
formation of reactive species, and plasma glow is generated. Active plasma compo-
nents interact with microbial cells and thus cause damage to the cells. It can be
effectively used to inactivate microbial cells, thus increasing the product’s shelf life.

6.4.1 Principle of Cold Plasma

Plasma can be thermal or non-thermal depending on the source and method used for
plasma generation. Generally, plasma is generated using conventional devices such
as spark plugs and welding arcs, and thermal plasma is generated, where the energy
of particles is sufficiently high that they are in equilibrium. There is no energy
transfer among them, and highly reactive species are in hot plasma (Fridman et al.
2005). The non-thermal or cold plasma, such as in plasma display screens and neon
signs, transfers energy among particles after every collision. Some species are more
reactive than others in cold plasma. The composition of gases in plasma generation is
an essential factor determining the type of chemical reactions plasma can initiate
(Niemira and Gutsol 2011; Lieberman and Lichtenberg 2005). The ionization of gas
is a significant step for the generation of plasma. Ionization can be carried out by
various thermal, magnetic, electric fields, radio waves, and microwaves (Conrads
and Schmidt 2000). The external application of high energy to the atoms present in
gas leads to the stripping away of electrons from atomic nuclei, thus forming reactive
plasma species (Misra et al. 2016). The type and concentration of reactive species
depend on the gaseous mixture’s characteristics used for plasma generation. For
example, the concentration of reactive oxygen species is more if oxygen is present in
the mix (Smet et al. 2018). Cold plasma does not rely on the thermal inactivation of
microorganisms and is generated at room temperature, thus not affecting the prod-
uct’s quality attributes (Fernandez et al. 2013).
Cold plasma occurs in a non-equilibrium state where the massive particles are
generally at room temperature. In contrast, sufficient kinetic energy is present in free
electrons for ionizing collisions and bond breaking. While on the other hand, thermal
plasma exists in completely thermodynamic equilibrium (Kennedy and Fridman
2011).
170 R. Kaur et al.

6.4.2 Generation of Cold Plasma: Equipment and Process

Design

Plasma generation is carried out by ionization of gases using different methods.

Typical plasma generation system includes the following:
• Power source.
• Plasma discharge device.
• Carrier gas.
• Treatment chamber.
• Gas and pressure control system.
Different types of discharge systems can be used for processing operations. These
include dielectric barrier discharge, glow discharge, corona discharge, arc discharge,
radio frequency, and microwave discharge (de Castro et al. 2020; Romani et al.
2019; Tabibian et al. 2020; Corradini 2020). Dielectric barrier discharge involves the
use of metal electrodes covered with a dielectric material. In this system, one
electrode is grounded, while another is connected to high voltage and a mixture of
gases flows between the two electrodes. It is also known as “Silent discharge”
(Shimizu et al. 2018). In glow discharge plasma, alternating current is passed
through a gaseous mixture by applying high voltage. When the pressure inside the
vacuum chamber reaches 2 Pa, plasma generation occurs (Romani et al. 2019). In
corona discharge plasma, the non-uniform electric field is used under atmospheric
pressure. Plasma generated using this method has some luminosity, which is weakly
ionized. The magnetron is used to produce a high-frequency electromagnetic field in
case of microwave plasma discharge. It is commonly used in the case of high-
temperature processing because heat is generated during the collision of electrons
with gaseous atoms and molecules. As a result, a high degree of ionization takes
place (Roy 2017). The plasma that is generated is then discharged for treatment of
food, using micro-needle, plasma jet or plasma chamber (Sakudo et al. 2020).
Cold plasma can be used in three different exposure methods for food preserva-
tion: direct exposure, electrode contact, or remote exposure. In the direct exposure
method, food materials are placed in direct contact with the plasma generation
system, also known as active plasma, which contains long- and short-lived reactive
plasma species. In the case of the remote exposure method, firstly, the plasma is
generated. After that, it is used for treatment, and the food product is not placed in the
same chamber where plasma is generated. Therefore, the reactive species act on food
placed in a separate chamber and are thus not in direct contact during plasma
generation. Plasma generating electrodes are used in the case of electrode contact
method, and food is placed between the electrodes, which emit reactive species, and
ion bombardment takes place (Niemira and Gutsol 2011).
6 Non-thermal Food Preservation Technologies 171

6.4.3 Application of Cold Plasma for Food Preservation

Cold plasma is an emerging non-thermal technology that can be effectively

employed for food preservation without causing any damage to the quality of food
products. The mechanism of microbial inactivation using cold plasma is based on the
action of reactive plasma species on components of the microbial cell (Phan et al.
2017). The presence of reactive free radicals in plasma causes oxidative damage to
polyunsaturated fatty acids present in the lipid membrane, as they are more sensitive
to reactive oxygen species (Alkawareek et al. 2014). Lipid peroxidation leads to the
formation of fatty acid radicals that react with oxygen converted into lipid hydro-
peroxide. The lipid peroxides further cause damage to proteins and DNA through the
irreversible formation of covalent adducts (Del Rio et al. 2005; Joshi et al. 2011). UV
photons are also formed during plasma generation, responsible for causing damage
to the genetic material and inhibiting DNA replication (Guo et al. 2015). In addition,
photons emitted by the plasma lead to the formation of thymine dimers and oxidation
of nucleotides by reactive oxygen species. Apart from protein and lipid alterations,
free radicals also lead to the breakdown of structural bonds like C-O and C-N bonds
present in cell wall components, such as peptidoglycan, leading to damage to the cell
wall (Misra et al. 2016).
Permeabilization of cell membrane leads to the leakage of cellular components
from cell-matrix, and the reactive species further cause damage to the intracellular
nucleic acids and protein. It is also the reason for the bactericidal effect of cold
plasma (Mai-Prochnow et al. 2014). Therefore, scanning electron microscopy was
employed to study the morphological changes caused in bacterial cells, like mem-
brane damage, electroporation, and cell wall damage (Misra et al. 2016).
Cold plasma can effectively be used for microbial inactivation as it is found lethal
to vegetative cells and spores (Feichtinger et al. 2003). In solid foods, the focus is on
surface decontamination, whereas in liquid foods, every particle is in contact with
the plasma (Fig. 6.3).
Cold plasma also finds its application in the decontamination of packaging
materials. For example, radiofrequency pulse discharge on the air inside PET
(polyethylene terephthalate) bottles resulted in 3 log cycle reduction of microorgan-
isms and had deodorization effect in the few milliseconds exposure (Koulik et al.
1999; Deilmann et al. 2008). Various researchers have studied the use of plasma for
microbial decontamination of packaging materials such as polypropylene (Gadri
et al. 2000), PET foil (Schneider et al. 2005), polystyrene, multilayer packages
(Muranyi et al. 2010), glass, polyethylene, and paper foil (Lee et al. 2015).
Reactive plasma species also cause the volatilization of spore surface compo-
nents, also known as “Etching” (Philip et al. 2002). It was found that there was 3.5
log cycle reduction in B. subtilis population on exposure of 5 min by plasma
generated at 200 W (Hury et al. 1998). Products with grooves on their surface
require more exposure time for microbial inactivation (Fernández et al. 2012;
Ziuzina et al. 2014). On exposure of apple juice for 480 s, there was five log cycle
reduction in population of Citrobacter freundii by plasma generated using argon and
172 R. Kaur et al.

Fig. 6.3 Flow sheet depicting the generation of cold plasma and action on microbial cells

0.1% oxygen (Surowsky et al. 2014). Significant reduction of E. coli and Salmonella
was observed on the surface of apples, mangoes, and melons after plasma treatment
(Tappi et al. 2016).
Cold plasma can also be applied to prevent enzymatic browning in fruits and
vegetables. Seventy percent reduction in polyphenol oxidase activity of guava pulp
was observed, which was treated with cold plasma for 300 s at 2 kV (Thirumdas et al.
2015). Plasma leads to the breakdown of peptide bonds, thus changing the 3-D
conformation of the protein (Dobrynin et al. 2009). It also increases the ascorbic acid
retention in tomato juice on treatment for 10–15 min compared to other non-thermal
techniques like ultrasonication (Mehta et al. 2019). Starch modification can also be
carried out using cold plasma technology. Plasma treatment significantly affects the
crystallinity of starch granules (Zhang et al. 2014). Graft polymerization of ethylene
using glow plasma was carried out on rice and cassava starch (Lii et al. 2002). The
germination rate of pulse seeds was found to increase by 10–20% on treatment with
cold plasma (Filatova et al. 2011). Cold plasma can also be applied to change the
functional properties of packaged meat as it decreases the water immobilization in
the myofibrillar network (Wang et al. 2016). Microbial decontamination of eggshells
can be done using this technique (Ragni et al. 2010). Cold plasma technology has
excellent potential for commercialization in the food industry. Optimization of
operational parameters is essential to meet the safety regulations.
6 Non-thermal Food Preservation Technologies 173

6.5 Supercritical Carbon Dioxide (SC-CO2)

Supercritical fluids are among the recent advances in food processing and preserva-
tion. They are characterized by the properties of both liquid as well as gas and are
nowadays widely used as a solvent for extraction purposes. They have high density
like liquids but possess low viscosity and intermediate diffusivity like gases (Knez
et al. 2014; Raventós et al. 2002). When a substance is brought beyond its critical
temperature and pressure, it reaches a supercritical region where its gas and liquid
phases are in equilibrium (Cavalcanti et al. 2012). Carbon dioxide comes under the
GRAS (generally recognized as safe), due to its inert, non-toxic, and non-corrosive
properties. Recent trends in food processing are focused on the utilization of
supercritical fluids. The utilization of carbon dioxide (CO2) for extraction,
processing, pasteurization, microencapsulation, and sterilization is widely studied
(Kulkarni et al. 2017; Osorio-Tobón et al. 2016; Silva and Meireles 2014).

6.5.1 Principle and General Aspects of SC-CO2

Supercritical CO2 is an emerging non-thermal food processing technology and is

considered a sustainable and green technology. It is safe and environmentally
friendly as it can be re-circulated in the system. By reducing the pressure, removing
CO2 from the food matrix can be easily carried out. The critical temperature of CO2
is 31.2  C, and the critical pressure is 7.38 MPa. Due to its low critical temperature, it
maintains the fresh-like quality characteristics of food and does not lead to thermal
degradation of food components. Also, the energy requirement for maintaining
pressure is less than high-pressure processing and other supercritical fluids due to
its moderate critical pressure (Cavalcanti et al. 2012, 2016; Viganó et al. 2015). It is
also known as “Dense phase carbon dioxide.”
SC-CO2 is the state where carbon dioxide exists as liquid as well as gas, and both
the phases are in equilibrium because as the temperature and pressure reach beyond
the critical point, molecules have kinetic energy that is sufficient to overcome the
forces that are responsible for condensation of fluid (Werner and Hotchkiss 2006).
Due to high diffusivity of SC-CO2, it can easily penetrate the microbial cells and thus
finds its application in microbial inactivation. It can also be effectively used to
extract various components from food waste due to its solvent capacity (Wang
et al. 2020). This technique’s mechanism of microbial inactivation is based on
change in cytoplasmic pH, cell wall rupture, modification of cell membrane com-
ponents, inactivation of enzymes, etc. (Fig. 6.4). This is also known as “cold
pasteurization.” It can be used in batch, semi-continuous or continuous systems.
Small changes in temperature and pressure can adjust solvent properties and
chemical reaction rates of carbon dioxide in a supercritical state. A change can
vary the fluid density in pressure as it has high compressibility at a critical point. This
174 R. Kaur et al.

Fig. 6.4 Possible mechanisms of SC-CO2 to microbial inactivation

can further lead to change in viscosity, diffusivity, and solvation properties (Nikolai
et al. 2019).

6.5.2 Equipment and Processing

SC-CO2 system can be operated in batch, semi-continuous or continuous mode.

Processing in semi-continuous and continuous systems offers more advantages
during processing large sample volumes (Paniagua-Martínez et al. 2018). The
SC-CO2 processing system comprises of:
• CO2 Tank.
• CO2 pump.
• Pressure vessel.
• Pressure regulator.
• Exhaust system.
• Temperature controller.
A carbon dioxide pump is used to pump the gas from the tank to the pressure
vessel, where it comes in contact with the food, and a temperature controller is used
to regulate the heating or cooling temperature. An exhaust system is used for
6 Non-thermal Food Preservation Technologies 175

degassing of the product after the treatment. Liquid food products are more effec-
tively treated using this technique. Pressure and temperature are the major parame-
ters that affect the solubility of CO2. Solubility of CO2 is found to increase with the
increase in pressure whereas it is found to decrease as the temperature increases
(Calix et al. 2008).
In a batch system, the food product to be treated is placed in a pressure vessel and
is saturated with CO2 at the required pressure and temperature. The product is kept in
contact with the gas in a vessel for a period. After the desired contact time, the
pressure decreases, and the exhaust system is used for degassing the product (Damar
and Balaban 2006). In a semi-continuous system, a series of vessels are connected,
allowing energy recovery, and reducing the processing time. In this system, CO2
flows continuously through the pressure vessel. During the processing, one vessel is
kept under constant pressure, the second is under depressurization, and the third is
unloading (Porto et al. 2010). In a continuous system, the liquid food product and the
CO2 are mixed after being pumped through the system, and then the mixture is
passed through the high-pressure pump to achieve the desired processing pressure.
The residence time or contact time is maintained by adjusting the flow rate of gas and
the liquid sample (Werner and Hotchkiss 2006; Damar and Balaban 2006).
Co-solvents can also be used along with CO2 to increase the efficacy of the
treatment. Various co-solvents that can be used include ethanol, water, peracetic
acid, and nisin (Park et al. 2013; Sikin et al. 2016; da Silva et al. 2016).

6.5.3 Application of SC-CO2 in Food Preservation

SC-CO2 has potential for application in food preservation due to its non-toxicity and
can be easily removed from food products after processing by depressurization. It
can be effectively used to inactivate enzymes and spoilage-causing microorganisms
and maintain the fresh-like characteristics of food products.
Microbial inactivation due to SC-CO2 processing can be attributed to several
factors such as cell rupture due to CO2 expansion within the cell and rapid pressure
release, lowering of extracellular pH, extraction of lipids from cell membrane due to
solvent properties of CO2, and loss of enzyme activity (Ballestra et al. 1996;
Spilimbergo et al. 2003; Meyssami et al. 1992).
The formation of carbonic acid occurs due to the reaction between CO2 and
intracellular water, thus lowering pH (Shieh et al. 2009). Treatment with CO2 also
causes the “Anesthesia effect,” which is one mechanism for microbial inactivation.
According to this phenomenon, as CO2 penetrates the phospholipid bilayer, it
increases permeability and fluidity. In addition, due to its lipophilic solvent proper-
ties, it can easily bind with lipid molecules in the cell membrane, thus leading to
leakage of cytoplasmic components (Spilimbergo et al. 2003).
Cell rupture, the disintegration of the cell wall, and puncture holes were visual-
ized using SEM images of SC-CO2 treated Staphylococcus aureus and Serratia
marcescens (Hossain et al. 2013). In the case of spore inactivation, initially, there is
176 R. Kaur et al.

disruption of spore coat and cortex, which leads to increased permeability. After that,
subsequent penetration of CO2 into the core of the spore results in the release of
cellular components, thus leading to inactivation (Rao et al. 2016; González-Alonso
et al. 2020).
Processing of mandarin juice with SC-CO2 resulted in 3.47 log cycles reduction
of the microbial population when process parameters were, the temperature of 35  C
at a pressure of 41.4 MPa for 9 min of retention time and 7% CO2 (Lim et al. 2006).
6.93 log cycle reduction of Lactobacillus caseii apple juice was observed when
SC-CO2 was carried at 55  C for 30 min at a pressure of 10 MPa and CO2 to juice
ratio was 70% (Silva et al. 2018).
Enzymatic inactivation by this technique is caused mainly due to protein dena-
turation and disruption of secondary and tertiary structures. Enzyme inactivation of
fresh juice is required to maintain the nutritional and sensory characteristics (Benito-
Román et al. 2019). Conformational changes take place in the protein matrix of the
enzyme during processing. Peroxidase, polyphenol oxidase, and pectin methyl
esterase are the major enzymes that must be inactivated in fruit and vegetable juices
to maintain their freshness.
This non-thermal preservation technique also helps to retain the maximum
amount of ascorbic acid in orange juice. It was observed that 88% of the ascorbic
acid was retrained in orange juice treated at 40  C at a pressure of 25 MPa using
SC-CO2. In contrast, only 57% of ascorbic acid was retained in orange juice
thermally (Oulé et al. 2013).
This is an emerging non-thermal processing technique, and it also finds its
application in the extraction of bioactive components from food products. It can
also be used for drying operations.

6.6 Irradiation

Food irradiation technology has gained interest worldwide and has been increasingly
used on different food products. Food irradiation is an efficacious non-thermal
process for the safety and shelf-life expansion of foods (Fig. 6.5). It is a process
that uses a small amount of ionizing radiation to treat food and feed products to kill
the pathogenic microbes present in the products. The irradiation of food safely
reduces spoilage bacteria, insects, and parasites in certain fruits and vegetables and
efficiently inhibits sprouting and delays ripening (Ashraf et al. 2019; Bisht et al.
2021). Also, in many countries, irradiation is used primarily for spices and herbs. As
known, spices are often heavily contaminated because they are dried out in the open,
and birds and insects contaminate the food. The irradiation technology is eligible and
suitable enough for its utilization in the agro-food sphere. Adequate application by
choosing the right wavelength and dosage these rays can prevent sprouting, maintain
freshness, and eliminate harmful microbes present in foods. They also can rid fresh
fruits and vegetables of insects that might otherwise hitchhike spreading to other
areas where they could instigate harmful effects on the environment and humans
6 Non-thermal Food Preservation Technologies 177

Fig. 6.5 An overview on irradiation technology for food processing

(Lacroix 2014; Pedreschi and Mariotti-Celis 2020). Food irradiation was recognized
by leading regimes associated with agriculture, food, and health (FDA, USDA,
WHO, FAO, etc.) gleamed from extended research work. The Food and Agriculture
Organization/International Atomic Energy Agency/World Health Organization
(FAO/IAEA/WHO) joint committee on the wholesomeness of irradiated food
approved in 1981 the irradiation technology (Junqueira-Gonçalves et al. 2011).

6.6.1 Principle

Irradiation as a food processing approach culminates in the subjection of food to

certain dose radiation, causing the elimination of pathogenic microbial load, which
keeps food product entirely for longer by halting spoilage. The mechanism by which
ionizing radiation inactivates microorganisms is mainly due to the direct or indirect
damage of the nucleic acids (DNA) of microbes, which is affected by free radicals
(OH) derived from the radiolysis water. Food treatment with irradiation allows
energy to pass through food and hit all the molecules that are present in the food.
Microorganisms and insects possess nucleic acids (DNA). When the actuated
178 R. Kaur et al.

radiation hits DNA, it destroys due to ionizing particles but without inciting any
substantial temperature escalation of foodstuff, subsequently avoiding the division
of cells by inhibiting DNA synthesis. In contrast, indirect radiation, the interaction
with water molecules provokes rendering of active molecules such as hydroxyl and
hydrogen radicals, and hydrated electrons, leading to cell lysis (Lacroix 2014;
Ravindran and Jaiswal 2019).

6.6.2 System and Processing

Electromagnetic radiation covers a broad spectrum of wavelengths, and these waves

have disparate uses according to their energy. Radio waves and microwave waves
find wide application in the field of communication, although microwaves are also
used in thermal processing of food, while visible light illuminates and is essential for
food production, X-rays on the other hand used for analytical purposes as well as
treating illnesses through radiotherapy and gamma rays are used as radurization
purposes (Prakash and de Jesús Ornelas-Paz 2019; Prakash 2020). Radiation can be
categorized as ionizing and non-ionizing, depending on its energy. Ionizing radia-
tions are shorter in wavelength but entail higher frequency and energy, contrary to
non-ionizing energy. The visible light spectrum, radio waves, micro-waves, and
infrared waves hold adequate energy to instigate molecular vibrations but not
ionization (Bisht et al. 2021). Contrastingly, X- and gamma-rays comprehend higher
energy, which can potentially discharge electrons from atoms, inducing the ioniza-
tion of molecules. These radiations can also terminate chemical bonding in mole-
cules, inhibiting normal cell functioning. The label “food irradiation” emphasizes the
purposive subjection of eatables to ionizing radiation (Prakash and de Jesús Ornelas-
Paz 2019).
Ionizing radiation factors in different irradiation origins, viz. gamma-rays,
X-rays, and e-beam. Gamma radiations are yielded up employing radionuclide
sources such a cobalt-60 or cesium-137; accelerated electrons (forming electron
beams) with a maximal energy of 10 MeV (Hernández-Hernández et al. 2019).
Gamma irradiation is conventionally acknowledged as a microbial decontamination
agent owing to its property to annihilate covalent bonding of bacterial DNA
(Mittendorfer 2016). Irradiation using cobalt-60 radioisotope is commonly used
for food treatment purposes owing to its insolubility in water, thwarting environ-
mental contamination, and hazards (Bisht et al. 2021). Contrarily, X-rays are
produced without any association with radioactive resources but are generated by
the bombardment of a dense target material using high energy accelerated electrons,
resulting in a continuous energy spectrum. X-rays with a maximum energy of 5 MeV
can be used to irradiate foods, which yield similar penetrating power as Co-60.
X-rays can ably penetrate thick material (30–40 cm), which benefits its utilization for
in-package product treatment, preventing food re-contaminations (Ashraf et al.
2019). X-rays irradiation at 10 kGy dosage can be used to scale down microbial
load (Bisht et al. 2021). Electron beams are propelled as a result of high-energy
6 Non-thermal Food Preservation Technologies 179

electrons in an accelerator (such as a linear accelerator or Van de Graaff generator)

that produce accelerated electrons at nearly the speed of light. Electron beam delivers
higher output at lower cost but also has low dose uniformity and a penetration depth.
Therefore, it is commonly used for the treatment of foodstuffs with lower thickness
(Ashraf et al. 2019). However, food irradiation treatment’s productiveness also
considers the nature of these types of ionizing radiation, medium composition,
water activity, and O2 ubiquity, dose absorbance, thickness, and density of food
matrix (Hernández-Hernández et al. 2019).

6.6.3 Applications in Food Industry

As consumer demands and food safety issues have changed, so have the food
processing technologies to ensure food safety. Irradiation is a versatile technology
that caters to numerous applications. The agro-food realm is highly efficient and
suitable technology in holding comprehensive efficacy against different non-sporing
bacteria and insects. Irradiation treatments of foods do not integrate any utilization of
chemical additives, which is another significant advantage considering the growing
consumer awareness and demand for chemical-free food. Apart from this, irradiation
of food stuffs can be efficiently done in their final packaging without affecting
microbial inactivation potential. Also, in-package irradiation of food products pre-
vents re-contamination of foods (Bisht et al. 2021; Hernández-Hernández et al.
2019).
The food industry deals with the different raw materials and matrices for food
production and processing, which unintentionally invites microbial incidence. This
resultantly leads to the entry of pathogens in the food production and processing
stages. Upon treatment of food with radiation, energy passes through the food and
strikes all the molecules present in the food. Microbes and insects possess DNA, and
radiant energy upon striking DNA induces damage preventing microorganisms from
multiplying. Differences in food radiation sensitivities among the microorganisms
are related to differences in their chemical and physical structure and their ability to
recover from radiation injury. Numerous studies in past times have disclosed the
applicability of irradiation to obliterate microbial populations on various food items.
In an investigation, the aftereffect of gamma irradiation (1, 3, and 5 kGy) on
microbial load of pomegranate arils was analyzed and the obtained results reported
that different irradiation doses considerably downsized the microbial population.
Authors also reported that with elevation in dose, the impact of irradiation on the
decrement of both bacteria and fungi improved markedly, with 5 kGy dosage
yielding leading results (Ashtari et al. 2019). Similar outcomes concerning microbial
population depletion for the tested gamma irradiation doses (1, 3, and 5 kGy) were
observed for date fruits stating better elimination with the highest dose (Zarbakhsh
and Rastegar 2019). A study conducted to evaluate the effect of γ-irradiation (0, 1,
3, 5 kGy at 26  2  C) on the mango juice resulted in curtailed total aerobic bacteria
(TAB) counts of fresh and stored juice from 4.2  0.3  104 and
180 R. Kaur et al.

6.5  0.2  105 CFU/mL to 2.6  0.2  10 and 1.1  0.8  10 CFU/mL,
respectively, when treated with 3.0 kGy dosage.
On the other hand, a higher dose of 5.0 kGy exhibited no bacterial incidence in
fresh and stored samples (Naresh et al. 2015). Red raspberries on exposure to e-beam
(3 kGy) as post-harvest treatment brought about reduction of mesophilic bacteria and
filamentous fungi by 2 log CFU/g and 3 log CFU/g, respectively, in comparison to
untreated samples during 7 days of refrigerated storage conditions (Elias et al. 2020).
Apart from this, irradiation also finds its utilization in microbial elimination in raw
and processed muscle-based products. Meat-based products being nutrient rich
matrices are highly prone to microbial incidence. Enzymatic activity influences the
sensory and nutritional quality of the food. It can be roused at any stage during
harvesting, transportation, storage, and processing. Golden Empress cantaloupe
juice was exposed to cobalt-60 irradiation (1–5 kGy). The obtained results for
enzyme activity determination displayed that lipoxygenase was the easiest to be
inactivated by irradiation, followed by polyphenol oxidase and peroxidases. How-
ever, all enzymes remained active even at 5 kGy (Wang et al. 2006). In another
study, white button mushrooms on exposure to e-beam irradiation (1–4 kGy)
displayed lower polyphenol oxidase activity than in control samples after 10 days
of storage (Duan et al. 2010).
Quality and shelf-life are the crucial factors of concern while dealing with any
preservation technology. The shelf-life of different foods is affected by several
intrinsic and extrinsic factors. However, irradiation can be an important factor in
extending the shelf life of other food products. Various studies have also proved the
applicability and efficaciousness of irradiation technology on the different products
such as raw and processed meat products, fruits, vegetables (Lacroix 2014), nuts,
spices, grains (Hernández-Hernández et al. 2019). In a study, Mulmule et al. (2017)
examined the effect of electron beam irradiation (EBI) at 2.5, 5, and 7.5 kGy and
combination of EBI (2.5 kGy) with thermal-treatment (80  C for 20 min) to prolong
the storage life of Idli (fermented food) and obtained results illustrated an increased
shelf-life stability of 60 days for the Idlis’s irradiated at 7.5 kGy in combination with
thermal treatment. In another study, Yoon et al. (2020) checked the efficacy of X-ray
irradiation (0, 0.15, 0.4, 0.6, and 1 kGy) on different qualitative attributes of Korean
strawberries during storage at 15  C for 9 days and the results showed that irradiation
at 1 kGy effectively retarded decaying and inhibited negative physicochemical
alterations in fruit with improved shelf life but maintaining the sensorial quality of
strawberries. Maraei and Elsawy (2017) treated strawberries with γ-irradiation at
(0, 300, 600, and 900 Gy) different doses and the results revealed reduced weight
loss, spoilage rate at storage, in contrast to untreated ones. Fruits subjected to 600 Gy
irradiation attained maximal total phenolic content and antioxidant activity ahead of
300 Gy. However, all treatments lowered vitamin C extents during storage, but
anthocyanins escalated gradually during storage. Similarly, Aftab et al. (2015)
evaluated the efficacy of gamma irradiation (Cobalt 60) at 0.5, 1, and 1 kGy on
goat meat. Results determined that gamma irradiation at 1.5 kGy significantly
maintained meat quality under refrigerated conditions as it prolonged the shelf-life
by 9 days.
6 Non-thermal Food Preservation Technologies 181

Other than increasing the safety and shelf life of food products, irradiation also
inhibits sprouting. Sprouting activity is a significant problem contributing to reduced
shelf life and elevated sugar content, affecting the marketability of seeds and tubers.
Various researchers have studied the use of irradiation technology for the suppres-
sion of germination. In a study conducted to analyze the effect of electron beam
irradiation (0, 500, and 1000 Gy) on five cultivars (Arinda, Burren, Sante, Agriya,
and Marfona) of potatoes resulted in the suppressed sprouting. It decreased loss in
weight and firmness, irrespective of the varieties, when stored at 10  C for 180 days
(Etemadinasab et al. 2020). In a different study, exposure of potato tubers to electron
beams (200 Gy) at 4  C or ambient temperature resulted in inhibited sprouting up to
110 days of tested storage (Blessington et al. 2015). Although, sprouting suppression
of tubers is reported to require lower irradiation doses which permits substantial
shelf-life increment (Roberts 2014), but among the other critical factors which
influence sprout-inhibition are rate of radiation, time delay between harvest and
irradiation, variety, and the storage conditions (temperature, relative humidity, and
duration). Apart from potatoes, garlic and onions are also treated with irradiation to
prevent sprouting. They are sprouting inhibition, better quality retention, improved
shelf-life under the dearth of chemical additives, and associated harmful residues set
up irradiation as productive technology for the shelf-life expansion of tubers, onions,
and garlic (Prakash 2020).

6.6.4 Consumer Perception on Food Irradiation

In the past decades, irradiation has gained considerable importance for its utilization
in the agro-food industry in different countries. Food irradiation is reliably endorsed
as a promising preservation approach and is affirmed by various associated agencies.
Although irradiation proposes numerous benefits such as eliminating microbes and
insects, quality and shelf-life enhancement, in-package treatment, no chemical input,
and does not instigate any toxic changes to the food itself, etc., however, this cannot
be denied that this technology also equates to some limitations such as higher
investment cost for commercial-scale application of technology to foods. Also,
irradiation applies to only a certain range of foods but not all genres. Apart from
that, one of the focal issues regarding the acceptance of technology is the consumer
perception about the irradiated foods and their psyche that foods might contain
radiation which ultimately leads to rejection (Lima Filho et al. 2015; Ravindran
and Jaiswal 2019). Associated fraternity such as FAO and WHO have already
validated for utilization and safety of irradiation technology for food up to 10 kGy
dosage. Labelling of irradiated foods is also made mandatory by governing associ-
ated bodies; however, only a few countries make its use obligatory, while a few
states permit the optional use. Stamping of “Radura” a green-colored symbol, is used
for irradiating treated food products to heave consumer confidence and acceptance of
irradiated products. Apart from that, there is a certain need to raise awareness and
shift consumers’ attitude (Junqueira-Gonçalves et al. 2011; Roberts 2014).
182 R. Kaur et al.

6.7 Ultrasound

All sound is created when molecules in the air, water, or other medium vibrate in a
pulsing wave. Ultrasound waves are more often like sound waves but vary in terms
of frequencies. The distance between each peak determines the frequency which is
measured as cycles per second or hertz. Sound waves that are detectable to the
human ear (16 Hz to 16–20 kHz) lie to a somewhat lower frequency range than
ultrasound frequency above 20 kHz (Gallo et al. 2018; Bhargava et al. 2021).
Ultrasonication is a dynamic and serviceable technology, applicative in numerous
fields. It is used as a non-invasive way to examine inside patients’ bodies; ultrasound
imaging is used to evaluate organ damage, measure tissue thickness, detect tumors
and blood clots, etc. Apart from application in the medical realm, this non-thermal
technology is utilized in biotechnology, food, nutritional enhancement, and cos-
metics. Ultrasound embodies waves of mechanical attributes that require an elastic
medium for their movement and spreading. Ultrasound waves instigate via an
oscillation pattern in an equilibrium position, channeled with energy shift from
one to another particle. These longitudinal genre waves travel through a certain
medium in a continual compression and rarefaction motion, dispensing pressure
differences in the medium, which yields benefits of interest (de São José et al. 2014).

6.7.1 Principle

The fundamental principle of low-power ultrasound comprehends propagation of

sound waves across the food matrix, bearing mechanical character inducing alternate
compression and decompression, distinguished by specific wavelength, velocity,
frequency, pressure, and time-period (Dolas et al. 2019). When specific sound
waves knock onto the surface, it stimulates drifting in the velocity and attenuation
of the sound through absorption and scattering mechanisms. Though comprehensive
results from different ultrasound frequencies are conclusively associated with the
evoking cavitation in the treated medium, viz. build-up of vapor bubble, which
violently outbursts in low-frequency applications, which causes liberation of high
pressures (>500 bar) and temperatures (up to 5000  C) provoking high shear forces.
Cavitation also is steadier (less vigorous wreckage of smaller bubbles) when engag-
ing higher frequency ultrasound, translating into more micro-streaming (Rastogi
2011; de São José et al. 2014; Zhu et al. 2020). These cavitation events drive
chemical, thermal, and mechanical effects. Chemically, it induces free radical
generation as H+ and OH imputable to annihilate the water molecule in aqueous
solutions. Secondly, accounts for single electron shift amidst the cooling phase and
recoalescence of hydrogen atoms and hydroxyl radicals to lay hydrogen peroxide
(H2O2) which possesses bactericidal action. While thermal effect (converted heat)
induced by cavitation can be used fruitfully in thawing, drying, and pasteurization
aids and mechanical effect due to produced mechanical shocks instigating
6 Non-thermal Food Preservation Technologies 183

disfigurement and ruination of cell structure fostering lysis, causing inactivation of

enzymes on account of depolymerization effect (Ashokkumar 2015; Dolas et al.
2019).

6.7.2 System and Processing

Ultrasound waves can be classified on the basis of low- and high-energy owing to the
frequencies, i.e., low energy ultrasound covers frequencies above 100 kHz at
intensities below 1 W/cm2 contrary to high energy which constitutes intensity loftier
than 1 W/cm2 with lying frequencies in between 20 and 500 kHz. However, the
commonly adopted frequency range for ultrasonic-technology applications stands
between 20 and 500 MHz. The different ultrasound-systems, frequency limits, and
conditions can be put into service for a broad array of food-applications, viz. high
frequency ultrasound is more often used to insight particulars on food constitution
(acidity, firmness, sugar-content, ripeness, etc.) while ultrasound hosting lower
frequency ranges are employed to induce cavitation bubble that bring about chemical
and mechanical effects through energy generation, intending for microbial-
inactivation in food stuffs by triggering compression, strain and temperature-
difference in the propagating system itself (Bhargava et al. 2021).
Basic lab-scale ultrasound equipment consists of an ultrasonic generator, oscil-
loscope, and sample room. However, most US treatments have not reached the
industrial level, though US applications in food research have shown promising
effects. This is mainly because ultrasonic equipment must be specifically designed
for every application, which leads to a lack of appreciation by the food industry.
Therefore, a collaboration between laboratory research and industrial scale is nec-
essary for the future (Awad et al. 2012; Misra et al. 2017).

6.7.3 Applications in Food Industry

Ultrasound is a comprehensive technology with extensive applications in the food

processing sector. It regulates production processes, analyses food properties, deter-
mines defects, and improves extraction, microbial inactivation. This multifarious
non-thermal technology has been exercised to enhance conventional food-
processing executions that only benefit with reduced energy and chemical require-
ments and moderate harmful emissions, hence catering a greener alternative (Tao
and Sun 2015).
Food is prone to microbial spoilage, which induces both qualitative and quanti-
tative losses. Ultrasound has effectively been used as a technology for microbial
inactivation for different food systems not only to scale down spoilage causing
pathogens but without inducing any detrimental effect to nutritional and sensorial
attributes of the food at ideal processing specifications, as preservation of quality
184 R. Kaur et al.

aspect is one the pivotal component while adopting any technology. Ultrasound
efficacy on the inactivation of Enterobacteriaceae bacteria in raw milk was tested
and the results reported notable inactivation of Enterobacteriaceae count
(1.06151 log cfu/mL) when treated with 120 μm amplitude at 60  C temperature
for 12 min (Juraga et al. 2011). On exposure of apple juice to ultrasound, obtained
results displayed highest enzyme (polyphenolase, peroxidase and pectin methyl
esterase) inactivation with 20 kHz frequency at 60  C for 10 min.; and microflora
(Abid et al. 2014). In a different investigation, a significant reduction in residual
enzyme (POD, PME, and PPO) activity as 4.3%, 3.25%, and 1.91%, respectively,
was reported when pear juice was subjected to sonication (65  C for 10 min)
(Saeeduddin et al. 2015). Microbial inactivation is attributable to the acoustic
cavitation phenomenon that induces damage to spoilage cell walls, causing patho-
gens. Though, certain key factors determine the inactivation efficiency of treatment,
such as amplitude, exposure time, temperature, food composition and volume under
treatment, target micro-organism (Gallo et al. 2018). Ultrasound technology also
finds its utilization as technology to enhance the quality and shelf-life of different
food products. In a study, Johansson et al. (2016) reported lipid oxidation derived
volatiles below the human sensory detection level in all cases with no oxidation
observed in milk treated with ultrasound (frequency: 1 MHz, 348 W and 2 MHz,
280 W) technology. Other studies also reported an increase in total phenols, antiox-
idant activity, flavonoids in fruit and vegetable-based juices treated with ultrasound
(Abid et al. 2013, 2014).
Ultrasound, owing to its principle, confers homogenous heat transfer, which
ultimately benefits various food processing operations such as thawing, drying,
freezing, and crystallization. It has been promoted for advanced results compared
to traditional methods (Gallo et al. 2018). The effect of ultrasound (40 kHz)
pre-treatment for 20 and 30 min on drying pineapple slices was analyzed. The
obtained results displayed an improved drying rate and accordingly reduced drying
time compared to the untreated sample. Pre-treatment for 30 min emerged to induce
the least total color change and lower browning in the sample during drying (Rani
and Tripathy 2019). In a different study, the efficiency of ultrasound (40 kHz;
30 min) pre-treatment for the vacuum freeze-drying of okra was analyzed. The
obtained results reported for improved drying rate of the sample after ultrasound
pre-treatment is considerably better than that of the un-pre-treated sample. Improved
drying rate is attributable to the ultrasound-induced micro-structure transformations,
which stimulated micro-pores formation among cells. Also, ultrasound pretreatment
obliterated the structure of fiber ducts, which slackened tissues and improved
porosity, which ultimately led to increased water diffusion from inside to outside
of okra tissues. Also, okra pre-treatment with ultrasound displayed lesser chlorophyll
degradation (5.05%) and higher total-phenolics, flavonoids, and pectin content
compared to other un-pre-treated and other methods (Xu et al. 2021).
Extraction is a basic process for the separation and recovery of bioactive com-
pounds from bio-matrices. Ultrasound is efficiently used as extraction technology
from different plant sources. Ultrasound-based extraction approach culminates in
ultrasonic energy (>20 kHz) engaging either ultrasonic bath and/or ultrasonic probe.
6 Non-thermal Food Preservation Technologies 185

Ultrasound-aided extraction enacts on the cavitation bubble formation principle,

which collapses and contrives higher shear, resulting in extraction enhancement. It
has been widely explored for its application on natural matrices. In a study, antho-
cyanin extraction from purple yam using an ultrasonic homogenizer at 750 W
(30  C; 10 min) resulted in improved anthocyanin content yield compared to the
conventional approach (Ochoa et al. 2020). In a different study, ultrasound (40 kHz)
aided polyphenols extraction from the whole mung bean, hull, and cotyledon using
other solvents. The reported outcomes displayed higher yield in hull, and the
obtained outcomes yield was higher than the conventional method (Singh et al.
2017). Furthermore, ultrasound technology in recent decades has been exploited in
one way or another efficiently for numerous engagements in food industries. The
physical and chemical effect on solid, liquid, and gaseous media has yielded better
and advanced results compared to conventional methods (Bhargava et al. 2021) in
areas such as cutting, meat tenderization, emulsification, filtration, de-foaming, and
degassing (Tao and Sun 2015; Firouz 2021).

6.7.4 Future Prospectus

Ultrasound technology is highly investigated due to consumer demand for minimally

processed food products without any chemical additives but of high overall quality
and safety. The potential of ultrasound as non-thermal technology for food
processing and preservation has led to its utilization in the diversity of food systems
with numerous application areas. Over the decades, multitudinous research studies
have confirmed the productiveness of ultrasound as a substitute and/or refinement of
various conventional processing approaches in the agro-food domain. Ultrasound
also confirms its utilization in combination with other technologies, which generates
better results. Further research should focus on the effect of technological alliance
with ultrasound on the overall quality of foods. In general, sound waves are reputed
to be safe, harmless, and eco-friendly, and edge over other techniques. Though some
limiting elements exist for example, high-intensity ultrasound equipment induces
nutritional and organoleptic quality loss. In addition, high-energy and expensive
requirement criteria limits the commercialization of techniques. Further, industrial
level enactment requires parameter optimization of ultrasound technology solely or
in combination with other methods for different food products and ground-level
research to scrutinize acoustic treatment effect on the commercial-scale food
processing. Finally, commercial-scale investigations should also focus on the con-
sumer view towards ultrasound-treated foods, which will purposely evaluate the
technology’s commercial success.
186 R. Kaur et al.

6.8 Summary

Non-thermal techniques find potential applications in food processing industries to

meet the demand for safe and high-quality food products. Some of the methods like
high-pressure processing are already being commercially used in the food industry.
In contrast, other applications need further studies for commercialization. Besides
being used as preservation techniques, these techniques also find applications in
pre-processing of various products, such as drying, freezing, emulsifying, and
extracting several bioactive components. These techniques can also be used in
combination with thermal processing to increase efficacy due to the synergistic
effect of the hurdle technology concept.

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Chapter 7
3D Printing: Technologies, Fundamentals,
and Applications in Food Industries

Mohammed A. Bareen, Jatindra K. Sahu, Sangeeta Prakash,

and Bhesh Bhandari

Abstract The current work aims to consolidate and analyze the technological pro-
gressions in three-dimensional (3D) food printing (3DFP) that holds the potential of
personalization and customization of food. Important reasons for the success of 3D
printing (3DP) in food applications are the driving factors like maintaining compet-
itiveness through superior technology, dynamic growth, and the role played by
researchers in strategically expanding the appropriate raw materials. 3DFP claims
to accomplish a sustainable food production process by removing the eminent flaws
in the conventional production practices. Another striking feature of 3DFP has been
its role to include novel food material into specific diets for people with special
dietary needs. By adopting this emerging 3DFP technology, an alternative nutri-
tional control arrangement stipulating an economically healthier choice could be
established. With 3DFP, it is possible to create products with great value, making it a
novel technology of the future for the industrial customization of many food
products. In this work, information on the operation of the existing 3DFP techniques
including their most recent advancements is congregated and analyzed. Further,
various reports on process/product variables that impact feature generation of edible
materials are briefly explained. Further, it discusses the potential implications of the
3DFP technology in ameliorating different domains of food sectors and future scope
in mass fabrication.

M. A. Bareen
Food Customization Research Lab, Center for Rural Development and Technology, Indian
Institute of Technology Delhi, New Delhi, India
School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD,
Australia
J. K. Sahu (*)
Food Customization Research Lab, Center for Rural Development and Technology, Indian
Institute of Technology Delhi, New Delhi, India
e-mail: jksahu@iitd.ac.in
S. Prakash · B. Bhandari
School of Agriculture and Food Sciences, The University of Queensland, Brisbane, QLD,
Australia

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 197
S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies,
https://doi.org/10.1007/978-981-19-1746-2_7
198 M. A. Bareen et al.

Keywords 3D printing · Food texture · Food structure · Food customization

7.1 Introduction

3D printing (3DP) is a prevalent name of the technology that is a branch of the broader
group of additive manufacturing (AM) techniques. It is also known as rapid
prototyping (RP) or layer manufacturing technology. The fabrication method in 3DP
involves data transfer from a computer-aided design (CAD) file, to replicate an object
into a 3D model by building up successive layers of the printable material. The
material loss during the fabrication is minimal with a rapid production rate, presenting
a phenomenal advantage over subtractive manufacturing methods (Jyothish Kumar
et al. 2018). Advantages of this digital mass manufacturing technique include high
precision and repeatability, favoring a wide range of materials and large production
capacity. Considering these competencies the manufacturing method is being utilized
in diverse fields including mechanical engineering, aeronautics to design sciences,
biomedical engineering, pharmaceutical industry, and biotechnology (Krujatz et al.
2017). Recently, the technology has been well acknowledged in food industries for the
design of novel food products with improved textural, structural, functional, and
sensorial quality attributes (Sartal et al. 2019).
The way food is manufactured has evolved remarkably over the last few years.
3DFP technology is one of those upcoming manufacturing techniques that is quickly
taking over the conventional processing techniques, owing to its ability to fabricate
food of intricate patterns with fidelity, achieve unconventional texture, and structure
along with personalized nutrition content (Rayna and Striukova 2016). It, further,
eliminates the labor-intensive step-by-step extensive unit operations needed in the
energy-inefficient conventional food production methods. The introduction of 3DP in
food, to fabricate food via design made on a computer, can be associated with the use
of 3DP as a “free form fabrication” method developed in 2001 that claimed food to be
a substantial material for AM (Wu 2001). The investigation was aimed at extrusion
printing of a mixture made from starch, yeast, corn syrup, and frosting. Eventually, a
commercial 3D food printer termed as Fab@home™ constructed by Cornell Univer-
sity ventured into the market in 2010 (Lipton et al. 2010). The developed printer was a
single-nozzle extrusion-based system employing a large open access, online model
directory for easy design, and development of complex food geometries. Following
this, deliberate research rendered in this field inspired the development of numerous
commercial 3D printers around the globe. Hitherto, approximately 50 companies are
involved in the production of printers for food printing.
During 3DP, the properties of materials employed play a substantial part in
defining the quality and acceptability of the end product. Food material is a hetero-
geneous system of many components with a wide deviation in the physicochemical
characteristics, making it implausible to print all categories of food with just one
standard printing process. Addressing this issue, several works have developed
edible printing material with peculiar texture and design either by enhancing the
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 199

material characteristics through additives and/or optimizing printing variables. Var-

ious food like chocolates, cereal products, dairy, meat gels, turkey meat puree and
scallop, orange concentrate, and processed cheese (Lipton et al. 2015; Lanaro et al.
2017; Azam et al. 2018; Le Tohic et al. 2018; Severini et al. 2018; Dick et al. 2019;
Ross et al. 2019) have been reported in the literature. Recent developments in 3DFP
technology have made it possible to manufacture 3D food printers at a low cost,
making them affordable to residences, restaurants, and hotel industries. As seen
irrefutably, 3DFP has the ability to mass manufacture personalized food with unique
functionality that is incomparable to the conventional production methods even with
advanced processing technologies and process control. Although the convenience
and benefits of the 3DFP are unequivocal to the hackneyed food manufacturing
methods, some prevailing challenges such as consumer acceptability and awareness
among producers still need to be addressed before it is recognized as an ideal food
fabrication method.
Over the past few years, studies on innovative food manufacturing processes have
been considered to address productivity, personalization, and environmental issues.
3DFP technology with its ability to deliver personalized nutrition and the
eco-friendly process could aid in the active replacement of in-use processing
methods. 3DFP has evolved significantly over the decade as “globalization” widens
food availability and technology integration. While several research works have
been published in the literature, the majority of which emphasize on investigating the
different characteristics of food materials to improve their printability. Very few
attempts have been made to develop a universal food printer committed to large-
scale production of customized foods integrating the best available AM techniques.
The objective of the current chapter is to provide an overview of operation planning
for 3DP, the potential relevance of various AM techniques in printing food, and
analyze published articles related to the advancement in the state of science and
applications of 3DFP with the aim of gaining insight into future possibilities of
developing novel 3D printer to design functional foods.

7.2 Operation Planning of 3D Printing

The basic objective of employing 3DP is to access its capability attributed to the
effortless and economical manufacturing of any complicated build for food fabrica-
tion. A 3DFP process can be divided into well-defined sequential steps necessary to
build 3D printed constructs using raw material in the form of liquid, solid, or powder
(Fig. 7.1). These steps are imperative regardless of the AM process selected for
fabrication based on the material properties.
1. The primary task is to design the required 3D model with the help of design
compatible systems like AutoCAD, Blender, CATIA, or Solid Works. This is
performed by either generating the required 3D illustration in conceptual design
(CAD model) or analyzing a real-world object to extrapolate its surface pattern
200 M. A. Bareen et al.

3D printed product

3D Prinng

Generate G code

Convert to STL

Prepare a CAD 3D

Fig. 7.1 Schematic diagram of a typical 3D food printing process

and appearance through a 3D scanner and recreate a complete 3D model, an

approach recognized as reverse engineering (Chua et al. 2010). A good model
should be compatible with the printing parameters of the process and used
material(s). This CAD file is saved and exported to an STL (Stereolithography
or Standard Tessellation Language) format. STL is a standard file type utilized by
most 3D printers. It is a triangular representation of the object modeled in CAD.
2. Following modeling, slicing operation is practiced by adopting any slicing
software to slice the CAD model into the uniform stack of parallel planes.
Subsequently, a G-code is generated, which is a computer numerical control
(CNC) programming language. It encompasses instructions about the print loca-
tion, a path to be followed, and the movement speed of the motors in the printer
during printing. Based on the literature review, Cura and Slic3R software pack-
ages have been extensively used for food printing. However, numerous options
are available online on open-source platforms which perform the same function.
The G-code has a huge impact on the smoothness as well as the mechanical
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 201

robustness of the printed product. The software used requires alteration of

printing and non-printing movements such as slice thickness, nozzle temperature,
infill percentage, retraction time, and printing speed suitable to the consumer
needs. A food material being an association of multiple biological components
would require a robust slicing software that can optimize the number of layers and
layer size required for an accurately printed object from the equipped CAD
model. An optimized slicing software specifically for food printing is yet to be
developed. Only one review related to optimizing the 3D model building and
slicing process is found in the literature for food 3DP (Guo et al. 2019). After
slicing, printing commands in the form of G-code are then loaded into the printer
to fabricate the desired model. The editor software packages such as Arduino
IDE, Reptier-Host, and Marlin are present in many printers, which basically read
the G-code (input) and convert the command into movements (output).
Selection of a printer suitable to material inks is a tedious task, as the commer-
cially available printing platforms only allow printing of a specific type of material
with characteristic properties. Therefore, it is important to fathom the currently
available technology explicitly in terms of applicable material and functioning
mechanisms. The next section provides an insight into the different methods
employed, their working mechanism, and recent advancements in terms of 3DFP.

7.3 Potential Relevance of Various AM Techniques

in Printing Food

The 3DP technology, since its inception, has undergone ample advancements
establishing it as the potential technology of the future. This has led to a mark
where 3DP has gained prominence as a competent approach for digital modeling and
fabrication of novice material. One illustration is the pertinence of 3DP in the food
industry; the technology presented an excellent opportunity for digital food fabrica-
tion, revolutionizing the food production process. It has been utilized in unprece-
dented ways by augmenting the process variables as well as the material properties to
address a diverse number of challenges in food processing. The major influential
parameters established by the research community for obtaining a high-quality 3D
printed product are material characteristics, specifics of printing technology, and
subsequent posttreatment. Out of these parameters, selecting the right technology
that is categorized in many classes conditional to the energy specification, spatial
configuration, operating mechanism, etc. is of prime importance. Based on the
American Society for Testing and Materials (ASTM) standards, AM processes can
be categorized into seven broad headings such as VAT photopolymerization, mate-
rial jetting, binder jetting, material extrusion, powder bed fusion, sheet lamination,
and directed energy deposition. The individual process has its corresponding strong
and weak points depending on the material employed and process parameters. Food
application has specific requirements of material that should be considered;
202 M. A. Bareen et al.

Fig. 7.2 A schematic classification of 3D printing technologies applied in food

therefore, only four 3DP processes have been effectively applied for developing
edible 3D constructs. These include material extrusion, material jetting, binder
jetting, and powder bed fusion through selective laser sintering and melting
(CandyFab 2017). Figure 7.2 illustrates the schematic classification of 3DP technol-
ogies applied in food as per the nature of selected food materials and type of driven
mechanism.

7.3.1 Material Jetting

7.3.1.1 Fabrication Technique Description

Material jetting (MJ) (method is commonly referred to as inkjet printing) printer

originated as an office accessory and has now perfected as a mechanism in the
industrial mass fabrication process to 3D print products. It was one of the first AM
processes adapted for 3DP and has been used commercially since 1994 (Gibson et al.
2015). The material/ink used in this process is deposited over the surface of a
substrate using a printhead. The added advantage with this deposition is it happens
over a large, desired area at once and not at just a particular point. The employed
printhead has hundred to thousand tiny nozzles, each one command assisted for
precision material deposition. The schematic of an MJ printing process is illustrated
in Fig. 7.3. MJ printing technique allows synchronized deposition of diverse mate-
rial. Typically, in a printhead of MJ printing, nozzle sizes are in the range of
20–30 μm. Liquid droplets produced are in the range of 10–20 pL. Minute nozzles
allow smaller droplet deposition which delivers a better resolution final product. The
printhead can be operated in two methods: continuous (Co-MJ) application and
drop-on-demand (DD-MJ) application.
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 203

Fig. 7.3 A schematic diagram of material jetting (MJ) process

In a Co-MJ deposition head, the printing material is pressurized through an orifice

using a high-pressure pump (Fig. 7.4), creating a continuous flow of droplets causing
the Rayleigh instability phenomenon in liquid jets assisting the flow of material
(Stow and Hadfield 1981; Shastry et al. 2004). Prior to the deposition, the material is
mixed with additional components that provide conductivity to achieve the desired
flowability. When the material in the form of droplets flows out of the nozzle, they
pass through the deflector plates (Fig. 7.4). Individual droplets are steered in the
desired direction by applying a potential to the electrostatic plates which avert the
charge of conductive material droplets to either the printing platform or the reservoir
for recycling. A material surface tension of 25–70 N/m is conferred for Co-MJ
printing (Lloyd and Taub 1988). The drop formation speed is in the range of
80–100 kHz, which is faster than DD-MJ dispenser. Application of charge condu-
cive agents to the material is requisite in Co-MJ process which makes it a little less
preferable in 3DFP.
The DD-MJ dispenser system uses a thermal or piezoelectric mechanism to
generate material droplets on-demand as shown in Fig. 7.5. An electric pulse is
passed through a semiconductor in the dispenser, causing inner temperature escala-
tion and consequent bubble formation. Maintaining consecutive pulses will induce
evaporation, nucleation, and enlargement of the bubble, which provides the required
energy for a drop ejection. Each pulse survives a few split-seconds and increases the
plate temperature to approximately 300
C. The ejected drop is positioned on the
substrate with precise control. Inside the dispenser, the piezoelectric chamber
accommodates a piezoelectric quartz crystal which controls the droplet ejection
mechanism. The applied external voltage causes an abrupt quasi-adiabatic decrease
in volume of the chamber via piezoelectric action, contributing to the pressure
required for ejection. The volume of the droplets range from 1 pL to 1 nL with
204 M. A. Bareen et al.

Fig. 7.4 A schematic diagram of binary deflection process in continuous material jetting (Co-MJ)
dispensing system

equivalent diameters in the range of 10–100 μm. The first commercial printer to
apply the MJ process for food fabrication was developed by De Grood, it employs a
DD-MJ mechanism for liquid food deposition on different food substrates
(De Grood and De Grood 2013).

7.3.1.2 Layer Consolidation Principle

For any material to be compatible for use in 3DP, it should have appropriate
characteristics like, it should follow a particular flow regime, adhere to the substrate
and previously deposited layer, and finally should solidify quickly after deposition
without spreading (Lanaro et al. 2017). The flow dynamics of material in the MJ
process can be characterized with the standard equations used for any incompress-
ible Newtonian fluids. Material droplets when ejected through the nozzle are anal-
ogous to the fluid dislodged from a small opening, the flow of which can be defined
by employing the Navier–Stokes and continuity equations. In literature, using these
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 205

Fig. 7.5 A schematic of drop-on-demand (DD) material jetting dispensing system

equations, certain parameters are formulated that have a significant effect on the
droplet jetting mechanism, those are:
• Ohnesorge number (Oh): A dimensionless number described as the ratio between
viscous forces to surface tension and inertial forces, a significant parameter that
illustrates the competence of the material for the MJ process.
pffiffiffiffiffiffiffi
η We
Oh ¼ 1 ¼ ð7:1Þ
ðγρaÞ2 Re

where ρ, η, and γ are the density, dynamic viscosity, and surface tension of the
fluid, respectively, a is a length—usually the diameter of a nozzle, Re is the
Reynolds number, and We is the Weber characteristic number (Bhola and
Chandra 1999).
206 M. A. Bareen et al.

• Minimum droplet discharge velocity (vmin): Duineveld recommended the mini-

mum velocity required by a material droplet to overcome the surface tension
produced at uncovered nozzle tip is generated by inertia of liquid droplets
(Duineveld et al. 2002). This typical minimum velocity of drop generation is
given by Eq. (7.2).

 12
γ
vmin ¼ 2 ð7:2Þ
ρa

• Maximum droplet discharge velocity (R): Upon jetting, the material drop has an
impact on the substrate causing to either settle or splash. To avoid splashing of the
droplet after the impact, Stow and Hadfield (1981) proposed a parameter (R),
above which splashing occurs. It is important to consider that splashing also
differs with surface roughness R (Eq. 7.3).
pffiffiffiffiffiffiffipffiffiffiffiffiffiffi
R¼ We Re ð7:3Þ

7.3.1.3 Impact of Process Variables on Feature Generation

The MJ process is endorsed to create a superior quality output with excellent

accuracy, which would require the material to be easily ejected from tiny orifices.
The final quality of the fabricated product in any AM technology is evaluated by its
accuracy to the designed model and stability of the printed constructs. The viscosity
of the material functions as a key determinant defining its flowability, regulating the
deposition rate, reducing unnecessary spreading, and providing uniformity and
smoothness of the printed object. In food applications, the MJ printing process
was initially used for creating 2D structures as the edible products that inherently
possess the necessary rheology for MJ were limited; typical materials used for MJ
have viscosity in the range of 2–6.8 cP (Shastry et al. 2004). Therefore, the
technology appeal was restricted to be used as a surface filling and image designing
process. The decorations and fillings were done on edible substrates such as cookie,
cake, and pizza. Several patents report the development of new edible formulations
that are suitable for the MJ process, emphasizing the role of ink flowability in the
final resolution of the printed image (Shastry et al. 2006; Edwards and Hills 2012;
Cavin et al. 2016). The temperature of the ink is a key determinant in the MJ process,
as it is having a direct effect on the viscosity of the ink. At low temperatures,
spreading of ink is reduced as a result of lower surface energy, establishing unifor-
mity and impressive finish to the print (Willcocks et al. 2011). The optimum
temperature that would administer a high precision final image varies with the ink
formulation. The compatibility of the edible substrate with ink after deposition
influences the interaction performance that has a considerable impact on the resolu-
tion of the final image. Largely edible ink preparations include a liquid solvent like
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 207

Table 7.1 Summary of selected works involving material jetting (MJ) technique in 3DFP
Ink formulation Device Study observation
Linseed oil core with carra- TNO’s encapsu- Their invention generates highly
geenan shell, Mint syrup lation printer monodispersed microcapsules with very
core with a wax shell well-defined shells and a capacity of 100 L/
h. The printhead has 500 nozzles and allows
the use of a variety of materials, including
waxes and fats, polymers, aqueous solutions,
emulsions, and dispersions Newman and
Newman (2015)
Sugar and starch powder Numerous The inks are designed to be printed on
mixtures along with different printers, one porous food surfaces. Printing can be done
flavor binders (water or example is before or after baking, thus making inks
alcohol based) and an edible Fujifilm suitable also for printing, deposition and
colorant Dimatix’s Merlin decorative food and non-food applications
Pallottino et al. (2016)
Liquid chocolate (Hershey’s, Electrostatic The authors have used the basic technology
shell topping) inkjet printer of electrostatic inkjet 3D chocolate printers
to create complex patterns with high preci-
sion in chocolate by controlling the printing
conditions Takagishi et al. (2018)

water, alcohol, and an edible pigment (Ryan 2009). The compatibility can be
enhanced by glazing the substrate exterior with a suitable binder or a layer prior to
jetting the ink onto the substrate, this entails a proper comprehension of the dynamic
interaction between ink and substrate surface (Willcocks et al. 2011). Surface
roughness aids in the strong adhesion of the ink onto the substrate. In previous
work, a layer of gums and other surface simulants like polyglycerol oleates and
polysorbates were credited to alter the chocolate surface effectively to support the
production of high-quality precise imageries (Mandery 2010). Besides the ink
viscosity, software packages employed for model development and machine control
are also key factors in the MJ process. Considering the multicomponent nature of the
food material as a potential for blockages and probable negative effects caused by
pressure and shear on droplet formation of the material, nozzle diameters reported in
the literature are usually larger than 1 mm and seldom below 0.5 mm.
As the technology progressed, many image processing programs were written or
modified befitting for food printing. In literature, liquid chocolate with or without
functional ingredients is the most frequently used ink in the food MJ process.
Recently, an electrostatic MJ printer assembly was constructed to print chocolate
on edible film with high-precision (Takagishi et al. 2018). Selected examples from
the literature highlighting research on food printing accomplished successfully using
the MJ process and their findings are summarized in Table 7.1.
208 M. A. Bareen et al.

7.3.2 Material Extrusion

7.3.2.1 Fabrication Technique Description

The material extrusion (ME) technique has gathered a great demand in the market
due to its ability to create intricate structures with a simple and effortless operating
procedure. The technique involves material deposition through a single- or multiple-
nozzle system guided by a numerical command to produce 3D constructs by
stacking layers of material (Fig. 7.6). This mechanism allows for the material to be
forced out under a continuous pressure coupled with persistent nozzle movement,
making it a competent method to build complex geometries with great accuracy.
Along with easy process control, it offers great flexibility with the material formats
involved for fabrication. Also, contrary to the inkjet printing process where the
material is ejected through tiny nozzles inside the printhead, ME functions on the
extrusion of the material under constant pressure allowing easy flow of colloidal
material (with a total solid in the range of 5–50%) to produce 3D structures. The ME
printers’ configurations that are frequently used for food fabrication include Carte-
sian, Polar, Delta, and SCARA (Selective Compliant Assembly Robot Arm) (Sun
et al. 2018). Delta and Polar configurations are the most commonly used owing to

Fig. 7.6 A schematic of 3D

printing: material extrusion
(ME) process

Extruder assembly

Piston

Material supply

Printing nozzle

Deposition of self -supporting layers

7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 209

their high material deposition rate, economic affordability, and small build. Exam-
ples of commercially accessible ME food printers and their products are described in
Table 7.2.
Depending on the format of food material, paste-like or powder, different dis-
pensing systems are engaged in ME printing. For liquid/semisolid food materials,
pneumatic and mechanical (screw or piston) dispensing systems are engaged. A
schematic diagram of the various dispensing mechanisms adapted for 3DFP is
depicted in Fig. 7.7. Screw-driven arrangement is a continuous extrusion system
where the material is loaded through a hopper and an auger screw is used to force out
the material forming a layered structure. Additionally, the auger mechanism aids
blend the material as it is being deposited to ensure homogeneity and avoid phase
separation. Intricate designs can be easily accomplished using a screw-based system
as it facilitates more spatial administration while printing (Sun et al. 2018). The
auger-based system is preferred to print material with high viscosity. In a positive
displacement-based system, a syringe/barrel is filled with printing material, and a
piston controlled by a stepper motor is employed to drive the extrusion process. The
reported deposition rate ranges from 10 to 104 μL/h (Zhao et al. 2015). Better
resolution can be obtained using this dispensing mechanism as there is a constant
positive displacement of the material in the syringe. A pneumatic-based system uses
air pressure to extrude materials kept in the enclosed cartridge. It is relatively faster
than the other dispensing systems as it can instantly be pressurized and
unpressurized on command. The low-viscosity fluids/semisolid material is fitting
to be used for this ME dispensing system. Furthermore, these three systems are also
used in combination with print novel materials created for specific purposes.
Broad categories of food materials ranging from high-viscosity pastes to liquids
have been reported in the literature that are articulated to obtain a good quality
printed construct using mechanical extrusion system. A recent study related to the
design and development of 3D printed food for people with swallowing difficulties
reported the use of a dispensing system involving a piston top and a fine nozzle
operated with pressure from an external pneumatic pump (Kouzani et al. 2017). An
investigation on print fidelity of soy protein isolate mixed with different concentra-
tions of gelatin and sodium alginate was reported to have good mechanical stability
and that it could be used to print more exquisite products (Chen et al. 2019). Both
rotation and positive displacement material dispensing systems have persistently
been explored to expand the variety of food material suitable for 3DP due to their
capacity to perform at high pressure, 100–600 kPa (Hamilton et al. 2018; Schutyser
et al. 2018). An innovative mixture of pneumatic and screw-auger-based material-
dispensing systems was established by Ghazanfari et al. (2017). Figure 7.8 illustrates
the design of the auger valve, which uses pneumatic pressure to transport material to
the printhead where an auger is used to deposit the ink (Ghazanfari et al. 2017). An
analogous novel arrangement combining two dispensing systems was employed in
the modularized printer—xPrint, assembled by MIT media lab for dispensing a
variety of materials (Wang et al. 2016). These printers can be used in food printing
by slight amendments to create functional foods with striking designs.
Table 7.2 List of 3D printing techniques employed in food
210

Printing Fabricated Commercially

process Method Description materials available printers Advantages Disadvantages References
Material Melting Binding of melted Chocolate Choc Creator, Supports large Low precision Lipton et al. (2010),
extrusion layers by solidifica- Focus, XYZ 3D array of foods level Natural Machines:
(ME) tion after deposition food printer, (The Makers of
Qiaoke, Porimy, Foodini, a 3D Food
Fouche chocolate Printer) (2020),
printer Porter et al. (2015),
Soft material Binding is based on Cheese, dough, Foodini, BeeHex Multiple com- Appearance Sun et al. (2015),
extrusion rheological proper- sauce, meat purees, Robot pizza printer, binations and of seam line (Yang et al. 2017),
ties (non-phase marzipan Barilla 3D pasta degree of between Periard et al. (2007),
change extrusion) printer, Procusini, freedom for layers (BEEHEX, PASTA
PancakeBot food OF THE FUTURE?
Gel-forming Binding through Sodium solution, Nufood 3D Food Low cost, Long fabrica- IT’S PRINTED IN
calcium chloride, Printer easy method tion time 3D BARILLA PRE-
• Thermal-gelation
xanthan, and gela- VIEWS ITS THE
• Chemical/ PROTOTYPE AT
ionotropic/enzy- tin with starch and
protein bases CIBUS 2016 |
matic cross-linking Barilla Group).
• Complex coacer-
vate formation
Powder Selective laser Melting of powder Icing sugar, TNO’s Food Jet- Support not Applicable to CandyFab (2017),
bed sintering (SLS) layer using a laser Nesquick powder ting printer required restricted food Godoi et al. (2016),
fusion source to fuses items with low Kietzmann et al.
(PBF) desired regions of melting point (2015), Sun et al.
the powder together (2018), Malone and
Selective hot Hot air is utilized as CandyFab More freedom Rough surface Lipson (2007), Diaz
air sintering a sintering source to to build com- finish et al. (2015)
and melting fuse powder particles plex food
(SHASAM) and form a solid items
layer
M. A. Bareen et al.
 Good accu- Expensive
racy and machines
resolution
Short time Complicated
Powder can process
be recycled
Material Drop-on- Deposition of a Sugars, corn flour, ChefJet, FoodJet High resolu- Limited appli- Murphy and Atala
jetting demand stream of droplets flavors, puree, or tion and cation in deco- (2014), Sun et al.
(MJ) onto a substrate from pastes accuracy ration and (2015)
a syringe-type surface fill on
printhead substrate
Continuous A high-pressure – No wastage
deposition pump directs the liq- of material
uid ink through an Easy method
orifice, creating con- Versatile
tinuous ink flow shape fabri-
printing cation ability
Multiple
colors
Binder Agglomeration A liquid binder Sugar-based sweets ChefJet, Able to create Rough or Diaz et al. (2015), 3D
jetting of powder sprays two consecu- complex grainy Systems (n.d.), Por-
(BJ) particles tive evenly spread structures appearance ter et al. (2015),
powder layers to a Precise Fragile end Wegrzyn et al.
predefined shape products (2012)
Support struc- Post-
tures are processing
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries

included auto- required to

matically in remove mois-
layer ture and
fabrication improve
strength
211
212 M. A. Bareen et al.

Fig. 7.7 Types of dispensing systems used in ME 3DP

b
Pressurized
air inlet

a
Heat radiation
Tank Extruder Material
source
and barrel Servo
nozzle motor

Liquid Liquid
Part Part

Substrate Substrate
Auger
Material
Outlet

Fig. 7.8 A schematic of an (a) on-demand extrusion process and (b) auger valve. (Adopted from
Ghazanfari et al. 2017)

7.3.2.2 Layer Consolidation Principle

For a successful extrusion-based process, it is imperative to comprehend the rheo-

logical characteristics like viscosity (η), yield stress (τ0), shear recoverability, storage
modulus (G0 ), and loss modulus (G00 ). Viscosity determines the flowability of the
material, which is an important parameter in extrusion-based printing. The relation
between the viscosity of the material and its flowability characteristics can be
described by the Hershel–Bulkley model (Herschel and Bulkley 1926) as per
Eq. (7.4).
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 213

τy ¼ τ0 þ Kγ n ð7:4Þ
yΔP
τy ¼ ð7:5Þ
2l

where n is the shear-thinning exponent, K is the consistency index, τ0 is yield stress,

τy is the shear stress, and γ is the shear rate. Numerous studies have reported the
crucial role of n and K in determining printability.
The yield stress determines the capability of printing material to adhere to the
previously printed layer which enables self-supporting 3D structure fabrication
(Lille et al. 2018; Joshi et al. 2021). Considering the pressure gradient due to external
pressure applied is ΔP along the barrel length (l), then the developed yield stress can
be calculated using Eq. (7.5), where y is the radial position within the nozzle (i.e.,
y ¼ 0 at the center axis and y ¼ R at the nozzle wall). The storage and loss modulus
are the measure of viscoelastic behavior of materials. These values determine the
post-processing stability of the printed construct, i.e., resistance to deformation after
deposition. Use of any material for the ME process should possess two essential
characteristics: demonstrate good flowability (shear thinning behavior) during extru-
sion and withstand distortion after layer deposition. The former can be altered by
enhancing rheological properties of the material through temperature and pressure,
and the latter can be controlled by inclusion of additives into the printing material.
The material inside the syringe during the extrusion through a nozzle is characterized
by shear rates of order 100 s1 (Seppala et al. 2017). This causes a significant change
in the orientation of components, derangement of loose bonds, etc. Food materials
are usually a heterogeneous mix of many components which exhibit highly variable
rheological properties, and these high shear rate and temperature fluctuations during
printing make it strenuous to establish one common optimum condition for print-
ability of all food. The printed layer combining phenomena for the construction of
intricate models in food material printing is governed by rheological behavior
(in case of gelling-assisted extrusion) and thermal performance (in case of fused
deposition extrusion).

Gelling-Assisted Deposition

Edible gels are viscoelastic substances, and numerous gelled products are
manufactured commercially. For 3DP, the gelation is induced in the material prior
to or during deposition to cause the adhesion in between the deposited layers. The
consistency and form of gel formulations are tailored to desired requirements by
altering their rheological characteristics. On the application of specified yield stress
(gel yield point), the formulation exhibits a shear-thinning behavior, making it
suitable for ME printing. Knowledge of the gelling process in the printing material
is important to modify its rheological properties such as elasticity and shear recov-
erability for better 3DP applicability. The gelling process can be categorized into five
sections: heat-induced gelling, covalent cross-linking, enzymatic cross-linking,
214 M. A. Bareen et al.

composite coacervate formation, and inotropic cross-linking. Several food gels such
as cheese, dough, meat paste, and jelly which have different gel formation mecha-
nisms were reported compatible for use as ink in the ME extrusion process to create
explicit patterns with good stability (Kern and Weiss 2018). A recent study
attempted to correlate the rheological behavior of different food-grade hydrocolloid
gels and their printing veracity results established two parameters: the phase angle
(δ) and the relaxation exponent (m), which can be used to determine the final product
quality (Gholamipour-Shirazi et al. 2019).

Fused Deposition Extrusion

In a fused deposition extrusion (FDE) process, semisolid material is deposited in the

form of layers and welded together due to the thermally induced bonding. The
softened and melted material is deposited in a X-Y plane through a nozzle with a
typical diameter of 400 μm which solidifies immediately after extrusion to form the
desired feature generation. The manifestation of changes due to the heat in edible
material happens either at the glass transition/crystallization or melting temperature
of the material. This happens in materials that are rich in fat or amorphous sugar
content. Therefore, in food applications, the FDE method has primarily been used for
printing chocolates (Malone and Lipson 2007; INFINITUS (CHINA) COMPANY
LTD. 2019; Mantihal et al. 2019). Temperature control is vital in this type of ME
process as the essential changes that facilitate contact between layers only happen
when the material attains its phase change temperature. If the temperature is lower
than the required phase change temperature, breaking and cracking arise in the final
product, and if the temperature is above the required threshold, distortion occurs due
to inadequate adhesion among deposited layers.

7.3.3 Impact of Process Variables on Feature Generation

The application of the ME process for food fabrication is a relatively new challenge.
However, substantial research investigating the critical variables that have an impact
on the printing accuracy is established, these include material attributes, choice of
printer, and printing variables settings (Yang et al. 2017; Feng et al. 2019). The
importance of the rheological performance of printing material to obtain uniform
printed objects has been described above (in layer consolidation principle). Some of
the crucial printing variables that have an impact on the printability of the material
have been established by the researchers; these include nozzle diameter, layer height,
extrusion temperature, and print speed (Liu et al. 2017). The importance of these
variables and their effect on 3D printability is detailed below.
The nozzle diameter of the printer is established to have a direct effect on the
surface finish and smoothness of a printed product. The best possible resolution and
a smooth finish of printed constructs are obtained by using the minimum nozzle
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 215

diameter that permits a uniform material extrusion. Liu et al. demonstrated that
printing of complex egg white protein with a larger nozzle diameter (1.5 and
2.5 mm) yielded a poor-resolution product due to over extrusion of material (Liu
et al. 2019). For the same material, a lower nozzle diameter (1.0 mm) produced a
decent surface quality product and good shape retention post extrusion. The nozzle
diameter also determines the amount of pressure intended for printing, for a given
material with specific viscosity, the pressure required for extrusion increases with
decreasing nozzle diameter. Yang et al. established that reduced nozzle diameter
results in an increased elevated pressure region in a nozzle which would cause
instant swelling after extrusion (Yang et al. 2019). They established that with the
increase in nozzle diameter from 0.2 to 0.5 mm, the pressure on the material in the
flow channel increased from 2.5  106 Pa to 4.95  107 Pa.
The material height or the layer height is the material layer width between the
nozzle aperture and the printing platform. This was considered as a key printing
parameter to determine the feature generation until recently, when Yang et al. (2018)
have performed comprehensive tests to confirm that it has the same impact as that of
the nozzle diameter. The authors interpreted that in an optimal printing condition
without over or under extrusion, the extruded layer diameter is the same as the nozzle
diameter and the inadequacies in layer adhesion due to a delayed deposition can be
avoided by restricting the nozzle height to as low as possible. Moreover, the ink
temperature during extrusion has a strong influence on the capacity of the extruded
layers to adhere after deposition. The validation is the direct association between
temperature and viscosity of the food. Predominantly in gelling-assisted extrusion,
the printing temperature is a crucial factor to maintain constant flow during the
printing. The print speed and extrusion rate have a significant consequence on the
rate of 3D construct fabrication with appropriate veracity. Derossi et al. (2020)
examined the effect of high print speed, extrusion rate, and some nonprinting vari-
ables on the printing fidelity of the designed model using wheat dough. The results
showed that in the case of the edible material extrusion process, at a high print speed,
the corresponding screw speed must be adequately increased to ensure sufficient
material deposition for accurate printing. The study found that increasing the
extrusion rate to three times or by reducing the value of the “diameter of filament”
command in slicing software to 1.0 mm against 1.75 mm permits acceleration in the
screw rotation speed that would obtain printed product with exact dimensions at a
print speed of 200 mm/s. In literature, most often for 3DP applications, a straight-
forward linear equation is employed to correlate the print speed and material
extrusion rate as represented by Eq. (7.6) (Khalil and Sun 2007). The equation
demonstrates that print speed, extrusion speed, and nozzle diameter are interrelated,
needed in complete regulation to construct perfect 3D objects. A summary of
selected works that explain the influence of process variables on feature generation
using different edible materials is presented in Table 7.3.
216 M. A. Bareen et al.

Table 7.3 Summary of selected works explaining the importance of process variables in material
extrusion (ME) technique when used in 3DFP
Raw Printing
material method Process variables Major findings References
Milk Fused depo- Nozzle diameter, layer Process variables such as Hao et al.
chocolate sition height, and extrusion extrusion rate, nozzle (2010)
extrusion speed velocity, and nozzle
height are critical for
geometry accuracy of
printed chocolate
Wheat Room tem- Layer height and infill The layer height has an Severini
dough perature percentage inverse effect on the layer et al.
extrusion diameter. Moreover, (2018)
mechanical strength of
the printed food is
affected by the infill
percentage
Potato Room tem- Printing temperature and The food formulations Martínez-
puree and perature composition of puree with higher consistency Monzó
milk extrusion index (K ) and lower flow et al.
index (n) at a temperature (2019)
of 30
C were the most
stable
Beef, salt, Gel forming Percent infill and fat Infill density (50%, 75%, Dick et al.
and guar extrusion content 100%) contributed (2019)
gum directly to moisture
retention, hardness, and
chewiness, and inversely
to shrinkage and cohe-
siveness, with no effect
on fat retention in the
lean meat-lard composite
layer 3D printed meat
products cooked sous-
vide
Sesame Infrared (1) 3D-printed without Applying heat to the meat Hertafeld
paste, lamp cooking, (2) 3D-printed and sesame pastes signif- et al.
chicken heating inte- with in situ IR heating, icantly affects the rigidity (2019)
paste, and grated and (3) handmade with and printing resolution of
shrimp extrusion oven heating the fabricated structure.
paste Printing without heating
leads to the extruded
layers to droop and sag

4
ps ¼ QD 2 ð7:6Þ
π r N

where ps is the print speed (mm/s), Qr is the material deposition rate (mm/s), and DN
is the nozzle diameter (mm).
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 217

7.3.4 Powder Bed Fusion

7.3.4.1 Fabrication Method Description

As the name signifies, this technique applies a laser or a heat source to fuse powder
material in a layer format layer by layer and finally into a required 3D model. If the
heat source is a laser, then it is called selective laser sintering (SLS) (Fig. 7.9) and
selective hot air sintering and melting (SHASAM) (Fig. 7.10) when the heat source
is hot air. The method of operation of SLS and SHASAM is similar to only one
variable being the heat source. Solidifying the powder particles with a laser beam
using a computing-controlled command unlocks countless opportunities for the
production of personalized objects with a great degree of freedom. In SLS, relying
on slicing of the 3D model, a continuous-wave laser beam scans layer course and
fuses selective powder material automatically. Once the cross-section is fused, the
printing bed is lowered, and a new thin powder layer is deposited on the top using a
roller. This procedure is iterated as long as the 3D design is finished. Subsequently,
the unfused powder material is collected and used for the next object (Kolan et al.
2012). SLS has been an attractive and adaptable method for printing varied powder
materials like metal, ceramic, and polymer (An et al. 2015; Wu et al. 2016). The
added advantage of using this method is that the powder bed holds together the
subsequent layers, providing a good degree of freedom to create complex shapes.
The SLS procedure permits the fast, flexible, and cost-efficient 3D structure
fabrication with good perfection, but is restricted to powder formulation, such as
carbohydrates oligosaccharides or polysaccharides, lipids, and sugar. The

Fig. 7.9 A schematic of selective laser sintering process

218 M. A. Bareen et al.

Fig. 7.10 A schematic of selective hot air sintering and melting process

multi-technology powder bed printer developed by TNO combines different

powder-based AM techniques to produce food with a high degree of resolution. In
one integrated system, a powder platform is provided where several liquids can be
deposited to bind the particles, and the hydrated area is subsequently heated by IR
beam to form a hardened layer (Diaz et al. 2015).

7.3.4.2 Layer Consolidation Principle

The layer consolidation phenomena in SLS are due to three main phenomena: solid
form sintering, liquid-assisted sintering, and powder melting. A solid form sintering
mechanism occurs when the laser temperature is between Tm (transition temperature)
and Tm/2 of the powder particles. For liquid-assisted sintering, an additive is
included in the powder formulation which will liquefy earlier than the matrix
phase and gradually dissolve to join the powder particles and form the layer. This
procedure is generally utilized for designing 3D structures for materials that require a
high sintering temperature (Shuai et al. 2014). In the melting laser sintering process,
complete melting of the powder particles achieving full density occurs in one step.
Typically, metals and ceramic powders use this method of 3D fabrication. In all the
methods when powder elements bind together at raised temperature, the overall
surface area declines, subsequently reducing the surface energy, thus slowing down
the rate of sintering (Schmid et al. 2013). For food applications, the solid form
sintering method is utilized as higher layer temperature causes degradation of the
temperature volatile components (Diaz et al. 2015).
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 219

7.3.4.3 Impact of Process Variables on Feature Generation

Several studies elucidated SLS as a very complicated process and proposed that
several processing factors impact powder densification mechanism and printing
veracity. The main process variables include laser energy density, spot diameter,
scan speed, powder particle shape, size, and distribution. It is established that most of
these variables are interdependent (Olakanmi et al. 2015). The powder densification
mechanism is reliant on the laser energy density, which has a direct impact on
strength of the manufactured object. To adjust the laser energy density, the laser
power and scanning speed combination is changed. Lower scan speeds produce
denser parts, while high scan rates produce porous and fragile structures (Gibson
et al. 2015). Reduced layer thickness follows in sturdier products with good
mechanical performance and lowered porosity (Amorim et al. 2014). Layer thick-
ness has a significant effect on the average pore size, as thicker layers facilitate less
fusion among particles causing less densification (Savalani et al. 2012). In an attempt
to mitigate the issue of low printing resolution in printed constructs using hot air
sintering and melting of sugar, it was established that changing the laser diameter
from 5 to 1.6 mm enhanced the final object resolution (CandyFab 2017). It is
pertinent to acknowledge that the powder properties bid a major impact on the
microstructure and mechanical characteristics of the printed construct (Ziegelmeier
et al. 2013). For SLS, the required powder material must be free-flowing and lump-
free. In an SLS-based printer, Aregawi et al. (2015) successfully printed many
cookies using various flour mixtures such as semolina, soft wheat flour, and a
flour and starch mix. Schmid et al. (2013) developed a method to produce edible
objects using a binder made of a mixture of palm oil powder and maltodextrin. They
concluded that the binder comprising at least two compounds that differ in their Tg or
Tm demonstrated excellent performance with a high degree of resolution and
precision.

7.3.5 Binder Jetting

7.3.5.1 Fabrication Method Description

Binder jetting (BJ) is a combination of MJ and powder bed fusion (PBF) techniques.
The schematic diagram with all the parts employed in the BJ process is illustrated in
Fig. 7.11. The basic setup comprises a build platform, a powder material supply bed,
a leveling roller, and a liquid binder supply head. The powder material of which the
model is to be made is fused by using a liquid binder. The designed 3D model is
fabricated by sequentially depositing 2D layers of liquid binder on the selective areas
of the print bed where the powder is spread using the roller. This deposition of binder
fluid on the print bed is achieved using an MJ printhead to form the required
geometry without melting the powder. Once the layer is hardened, the build platform
is dropped by a one-layer width and powder from the feed bed is dispersed on the
220 M. A. Bareen et al.

Fig. 7.11 A schematic of binder jetting process

print platform using a roller or a doctor blade. The subsequent layer is hardened by
the binder fitting to the 3D model and the procedure is iterated until the 3D object is
formed. This bound structure is denoted as the “green part.” Further post-process
like curing or cooking is required to strengthen the physical bonding between the
particles and across layers and provide enhanced mechanical stability. Unlike the
PBF process where a laser or a heat source is used to combine powder particles to
form a layer, in the BJ technique, a liquid binder accomplishes the same task. This
makes the process appealing for the fabrication of complex 3D structures using heat-
sensitive edible powder formulations. The formed products are gushed with pres-
surized air to eliminate any unbound powder from the fabricated object.
The most extensively exploited edible powder material in BJ fabrication of 3D
products is sugar. Reason being powdered sugar is hygroscopic in nature ideally, and
when immersed in a liquid binder, it sticks together with good adhesion and allows
for easy spreading. Utilizing this technique, a commercial 3D printer called
Chefjet™ is co-created by 3D Systems and The SugarLab which is capable of
fabricating complicated multidimensional confections with sugar. The machine
uses different edible flavors and colors as a binder to create these complex structures
(3D Systems 2013). Another endeavor by 3D Systems and Brill, Inc to create an
innovative professional-grade printing system for the fabrication of edible 3D figures
using the BJ technology is in progress (3D Systems n.d.). In addition, various
attempts are organized by the hobbyist to formulate edible structures using innova-
tive food products that are tried and tested for BJ printing, the recipes of which are
available on open-source platforms. Examples include using sake rice wine along
with alcohol, and a mixture of glycerol and distilled water as a binder liquid
formulations and a mixture of sugar and meringue powder, finely powdered salt,
and maltodextrin as powder ink formulations. Coffee and cocoa powders have also
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 221

shown promising results to be used as a powder to engineer 3D models using BJ

printing (Vadodaria and Mills 2020).

7.3.5.2 Layer Consolidation Principle

In a BJ process when the binder liquid is ejected on the powder particles, they
agglomerate to form a big porous secondary lump that is leveled using a roller to
form a layer with precise width. The process of agglomeration is described as the
development of large secondary particles as a result of an accumulation of primary
particles over time (Palzer 2005). The powder binding mechanism depends upon the
adhesion phenomenon between the particles and the liquid binder. Before binder
deposition, the powder material is evenly spread by the roller, which is ensured by
the altering flowability of the formulation. The flowability can be administered by
controlling the powder particle size. It is suggested that a blend of particle size
delivers good spread ability and post-processing strength of the printed construct
rather than using just fine or coarse particles (Shirazi et al. 2015). This can be
attributed to the formation of higher junction points, due to the substitution of
smaller size particles in the pores created by the large particles in the printed objects.
For edible powder formulations, a general tendency is to form lumps upon the
incorporation of water because of the chemical reaction between the particle sur-
faces. External influences like temperature and pressure that can facilitate physical or
chemical bonding of powder particles can be employed to alter the rate of agglom-
eration as well as the size of secondary particles (Dhanalakshmi et al. 2011). The
binder on deposition migrates into a porous powder bed through capillary phenom-
enon and induces many changes to initiate agglomeration. First, hydrogen bonds
among the polar components of the binder ink and powder are formed, enabling the
sintering of particles to one another. Over time, the nucleation phase is initiated,
dissolving the soluble components and increasing stickiness to strengthen these
sinter bonds. Powder composition has a substantial impact on the final product
quality, the presence of polar components like carbohydrates and proteins will
enhance the hydrogen bonding between particles and a liquid binder. The moistness
of the edible powder used in BJ must remain less than 6% based on the composition
of the food material used.

7.3.5.3 Impact of Process Variables on Feature Generation

Process variables that will largely affect the end product properties in BJ are powder
spreading rate, level of binder saturation, and layer thickness (Miyanaji et al. 2016).
The powder spreading speed or powder feed rate is controlled by means of a
spinning roller which travels forward and backward on the printing bed to deposit
a powder layer. The deposited powder layer must be uniform without causing any
distortion to the previous layer. An irregular and rough layer will contribute
undesired porosity leading to the poor structural integrity of the finished construct.
222 M. A. Bareen et al.

The spreading speed depends on powder particle morphology and flowability.

Spherical particles exert a lower friction value when compared to faceted particles
that adhere together generating more friction, therefore affecting the spreading
speed. Decreased spread speed is advocated to reduce the inconsistencies; however,
this would significantly raise the printing time. Due to the large Van der Waals
forces, finer particles (<5 μm) require low spread speed (Ramakrishnan et al. 2005).
Additionally, the surface on which the powder is dragged across, whether it is a
powder material layer or the empty bed layer also affects powder feed rate (Bhandari
et al. 2013). The powder feed rate determines the layer porosity or packing density of
the powder bed, which has a consequence on the binder saturation level. The level of
binder saturation can be defined as the ratio of binder volume to the void space in the
powder bed. The amount of binder deposited on the powder has a direct consequence
on the particle binding phenomenon which of course affects the build quality
(Gaytan et al. 2015). The powder absorptive ability and subsequent consistency
must be checked by combining the powder and ink in different combinations prior to
printing (Utela et al. 2008), as it has a significant effect on the bed packing density.
The results of the work establish the use of Eqs. (7.7) and (7.8) to evaluate the level
of binder saturation (Vaezi and Chua 2011).

ðV binder Þ ðV binder Þ
Desired saturation level ¼ ¼ ð7:7Þ
ðV air Þ ð1  Pr Þ  ðV solid Þ
 
V powder
Pr ¼   ð7:8Þ
V powder þ V air

where Vair and Vbinder are the volume of void space and the binder deposited volume,
respectively. Pr is the packing rate of the powder bed, and Vsolid and Vpowder are the
volume of a cross-section layer hardened after binder deposition and the volume of
powder that is spread on the platform.
Lower saturation levels result in loosely bound wetted particles while higher
levels of saturation may dissolve excessive powder particles beyond the described
layer dimensions, both of which will lead to a soft wobbly printed geometry (Asadi-
Eydivand et al. 2016). The smoothness of the printed feature is directly related to the
powder particle size in the BJ process. The particle size will determine the layer
thickness which will consequently define the printed product geometry.
Farzadi et al. (2014) demonstrated that printing veracity (dimensional accuracy
and mechanical strength) can be correlated with layer height and part orientation.
Their work reports the experimental investigation on the final product quality by
changing the printing orientation of an identical porous scaffold in X, Y, and Z axes.
It has been stated that the movement of the printhead, i.e., binder deposition direction
should match the model printing orientation.
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 223

7.4 Computational Fluid Dynamics (CFD) in 3DFP

3DP facilitates the creation of complex-shaped and functionally optimized products

within an appropriate time window. To leverage this capability, a systematic analysis
of the process is very vital. Literature review established that the rheological
components of the material are critical parameters associated with the prediction
of print fidelity. Analysis associated with just the rheological characteristics of the
material is nonetheless insufficient to predict its printability. As it cannot replicate
the complex flow behavior of materials during printing completely, distinct intricate
flow behavior during the process needs to be investigated in detail if an ideal layout
of a printing system and associated printing parameters are to be built. Modeling the
variation of material flow properties in the 3DP systems is not straightforward since
the preceding phenomena usually advance as a dynamic changing process. Compu-
tational fluid dynamics (CFD) modeling is established as an effective method to
simulate the actual flow in many numerical simulation processes such as spray
drying (Langrish and Fletcher 2001), microwave drying (Dinčov et al. 2004),
extrusion cooking (Singh and Muthukumarappan 2017) therefore, it is used as a
tool to simulate the material flow properties in the 3DFP process. CFD modeling
utilizes computers to compute the numerical solution of the partial differential
equations regulating momentum, mass, and energy transfer during material flow.

7.4.1 Numerical Simulation for Material Extrusion

Analyzing

In 3DFP, CFD simulation has been widely applied to characterize the material flow
in ME-based printing for process optimization by administering printing parameters
(e.g., applied pressure, shear rate, and nozzle dimensions for the particular material).
The computation of minimum flow stress, including velocity and pressure profile for
various edible materials, is done to assess and predict its 3D printability. High-
resolution CFD tools in 3DFP can resolve several challenges such as optimize the
printing process, stress distribution analysis of the printed construct, and avoid costly
and time-consuming errors and problems. Additionally, the use of CFD modeling
tools to predict the behavior of the material characteristics inside the printer nozzle
will lead to the highest print fidelity solution in a short span that allows printing
performance to be predicted. Since the printing process can be analyzed within a
digital platform, models may be accomplished without coming across health and
safety concerns or even running complications connected to actual prototyping. CFD
evaluation entails subsequent three steps. Step one is pre-processing, which com-
prises all the functions that take place leading to mathematical solution development.
This includes problem description, configuration, meshing, and generation of a
computational model. The next step is processing which relies on a computer system
to solve partial differential equations associated with the flow conditions. The last
step is post-processing, evaluating and visualizing the created simulation data both
224 M. A. Bareen et al.

Table 7.4 Examples of mesh construction software packages

Software package Open source Reference
PointWise N https://www.pointwise.com/
Zeus Numerix N https://www.zeusnumerix.com/
3D Slicer Y https://www.slicer.org/
Avizo N https://www.fei.com/software/amira-avizo/
Salome Y https://www.salome-platform.org/
Gmsh Y https://gmsh.info/
MeshLab Y https://www.meshlab.net/

numerically and graphically. The mathematical value of variables in the extrusion

flow field can be associated with the material characteristics and extrusion
conditions.
For simulating flow within the printer nozzle, printer-specific structural informa-
tion like nozzle design and material parameters such as rheology should be
established. The structural data should be such that it can handle adequate shape
particulars for segmentation and geometry extraction during model creation. A
segmentation program is employed to transform design information into a triangu-
lated fine mesh in line with the required geometry. Table 7.4 presents examples of
preferred segmentation programs intended to develop meshes that characteristically
function as input to the sophisticated numerical codes/solvers utilized in 3D CFD
simulations. The mesh influences the accuracy, convergence, and speed of the
solution. After the mesh is finalized, the boundaries of the problem domain are
provided, and also the required border problems identified within the preliminary
steps must be employed. Certain boundary conditions and initial flow parameters are
widely assumed (Yang et al. 2019; Liu et al. 2020) to solve the governing conser-
vation equations and determine the fluid field conditions during the extrusion
process.
These boundary conditions combined with flow variables and printer geometry
establish the flow problem to be solved. Sophisticated CFD software programs
possess the plan to execute the subsequent functions: determining the grid associated
with factors, also volumes or elements, characterizing the boundaries of the geom-
etry, implementing the boundary conditions, stipulating the initial conditions,
establishing the flow variables, and also setting the numerical control properties
(Dutta et al. 2020).
When meshing is accomplished, the model input parameters are stipulated in the
CFD code, solving the governing equations associated with every element until
satisfactory convergence is attained. This can be a rigorous procedure and typically
takes a sophisticated computer software package to solve a few thousand iterations.
Throughout every single element, the equations are integrated, along with boundary
settings attached to it, this is referred to as equation discretization. Numerous
numerical techniques to discretize the governing equations are used; some signifi-
cant methods include finite difference, finite elements, and finite volume. The model
can be used to validate the simulation outcomes with experimental data.
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 225

Post-processing tools equipped with potent CFD programs can produce visualization
including basic 2D charts to 3D representations. In such illustrations, colors are
utilized in order to distinguish between the volume of the requisite flow parameter
under evaluation. With this technique, a strong iterative feedback cycle could be set
up in order to optimize the fluid simulations and assure consistent, robust, and
reliable solutions.
Foods produced using 3DP technology usually demand a certain extent of post-
printing treatment. In brief, post-processing in 3DFP describes any kind of procedure
that must be accomplished after printing operation to improve its overall acceptabil-
ity. The specifications intended for post-processing of 3D printed non-food parts are
comparatively developed which involve removing support or excess material, sur-
face painting, washing, and curing, sanding, or polishing a model or coloring, etc.
However, post-processing operations in 3DFP are seemingly distinct, most printed
foods necessitate to endure conditions such as baking, drying, deep-frying, or
boiling.

7.5 3DFP: Solution-Oriented Paradigm to Ameliorate

Various Domains

7.5.1 Accomplishing a Change in Diet Pattern

The recent change in the lifestyle and diet from slow and healthy to fast and
unhealthy around the globe is an issue of growing concern. More and more people
are being identified as obese with wrong dietic habits, leading to the rampant
increase in diabatic and cardiovascular diseases. This issue can be addressed by
increasing consumer awareness regarding healthy food habits and delivering an
economical method for producing food with improved nutritional balance. The
3DFP on a global scale could be the solution. Here is how making the 3D printer
a common household appliance connected with the world through the internet will
help the consumers print food “on-demand” using just a cartridge. Further giving
them the choice to add ingredients in ratios conferring to their preferences and
personal nutritional necessities. Attractive food presentations would benefit the
kids to eat nutritionally balanced food (Hamilton et al. 2018). The cherished food
products with aesthetics can be fabricated using an online available model, which
earlier was not possible due to the requirement of expertise. Accompanied with the
advancements in the Information and Communications Technology (ICT) platforms
that would facilitate sharing through the internet irrespective of the geographical
locations, the consumers can help fabricate products at the comfort of their homes.
Thus, resulting in food offered at the price/performance of the latest technology,
reduced risk of product safety, producing food “freshly” on-demand providing the
opportunity to eliminate the use of chemical preservatives and polymeric packaging
material, multiple unit operations reduced to a single step (Nachal et al. 2019).
226 M. A. Bareen et al.

7.5.2 Prosumer-Based Food Market

Current mass food manufacturing companies emphasize on bulk creation using

limited ingredients and essences to the large variety of consumers with different
taste perceptions. As the taste of food is subjective, therefore there is a need for
customization in the food sector to produce products with individual fondness. The
comparatively novel arena of 3DFP possesses the immense capability for customi-
zation and fulfills the food structure according to the need and demand. It not only
can create customized edible shapes with advanced features using conventional
ingredients and flavors but also make use of novel ingredients to provide innovative
flavors. Additionally, the presence of a 3D food printer at home will ensure that the
specific consumer innovations and requirements are also met in the production
process which will provide higher market potential, which is essential in a specific
area like fine dining. The 3DP technology makes it possible to manipulate the
internal structure by regulating the order of layers (infill pattern) and infill percentage
(Liu et al. 2018). This is because the infill percentages alter the physical character-
istics of a printed configuration consequently providing a novel mouthfeel and
texture to the food.

7.5.3 Cater Food for People with Special Needs

The demand for food to tackle specific health ailments is in demand in the market.
3DFP can provide a solution to tailor a specialized healthcare arrangement intended
to better the lives of individuals with special meal necessities. The collaboration
between engineering and healthcare, to formulate food with necessary nutrients and
functional ingredients to sustain good health, comes under the preview of food
personalization (Sanwal et al. 2022). One specific category of individuals that can
benefit from personalizing food is those with dysphagia. Visually appetizing foods
with improved consistency and high nutrients can be designed using 3DFP for those
dysphagic patients. Additionally, the customization feature aids in creating sensory
and nutritional profiles for various categories of people with special needs (pregnant
women, athletics, toddlers, and children) (Ricci et al. 2019). With such techniques,
we can process a wide range of alternative base materials such as proteins from
algae, insect powder, and fungi to prepare nutrient-dense meals for people suffering
from undernutrition (Vieira et al. 2020).

7.5.4 Enable Food Supply Chain Digitization

The rapid urbanization of the expanding global population is accompanied by

increasing demand for accelerated production and tastier food products. The Food
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 227

Fig. 7.12 Soft cheese 3D printed constructs. (Adopted from Bareen et al. 2021)

and Agriculture Organization (FAO) of the United Nations lists one of the main
challenges of 2050 as “feeding ten billion people” (Production et al. 2014). Supply
chain digitization would be of substantial influence of convenience with cost and
packaging of food and can aid in usher new business models which can provide an
unprecedented solution to world hunger (German et al. 2004). In addition, the “print
and eat” approach affords foods at relatively low prices and great expediency as it
eliminates inventory risk and redundant businesses. The supply chain digitization in
food provides promising opportunities like (1) removing the need for chefs at small-
size production units like restaurants and fine cuisines as 3D printers can effortlessly
create customer-specific novel textured products with great accuracy. (2) Increasing
the consumer acceptance of an industry-fabricated product. When 3D printers are
utilized as a tool to shorten production time by combining multiple steps required
during manufacturing into a single step, it makes it easy to test multiple models of
different sizes and styles before going for large scale fabrication. This will eliminate
the production, transportation, and storage of huge quantities of unwanted food
products. Many food mass manufacturing companies have realized the potential of
3DP and are engaged in enhancing their product supply chain (Simon 2015; Murray
2021). Several research articles are available which have illustrated the potential role
of 3DFP in the digitization of the food supply chain using novel material formula-
tions and techniques (Vancauwenberghe et al. 2018; Cernencu et al. 2019). One such
example is presented in our recently published research article relating to the printing
of heat acid coagulated milk (HACM) semi-solids “soft cheese”-based dairy sweet-
meats with personalization of texture, structure, and functionality in an attempt to
digitize the supply chain of commercially available dairy structures (Bareen et al.
2021). Figure 7.12 illustrates the various numbers of soft cheese formulated using
3D printing technology.

7.6 Reducing the Detrimental Impact on the Environment

Better utilization of food that would otherwise be lost can improve diets, reduce
stress on waste treatment systems through less garbage disposal volume, and lower
the burden on the environment due to agricultural waste. Halving global wasted food
by 2050 could reduce emissions by an estimated 4.5 Gt (Bajželj et al. 2009). This
paradigm swing is possible by the application of food printing on a global scale. A
high production capacity per square inch makes the 3D printers a green technology
228 M. A. Bareen et al.

with a small ecological footprint. Another advantage in shifting from prosaic to

on-demand printing processes includes a drastic reduction of ingredients required to
make food. Moreover, the livestock industry contributes to a significant amount of
global warming triggered by agricultural activities (Green and Studies 2012). “If
current production and consumption practices of meat continue, these emissions can
increase to the point where agriculture will nearly exhaust the 2
C greenhouse gas
emissions budget by 2050” (2018, 1385). Adoption of 3DP technology, alternative
ingredients such as algae, fungi, seaweed, lupine, cultured meat, and insects can be
utilized as raw materials, production of which even in bulk quantities does not have
an adverse effect on the environment. An interesting example is a work done on ME
of natural plant-based ingredients to produce meat substitutions emulating the
appearance, texture, and flavor of steaks, roasts, and stews which is now commer-
cially available (Murray 2021).

7.7 Conclusion and Future Directions

To sum up, while several improvements have been proficient in the 3DFP process, it
is often applied in mass production. To fabricate products on a large scale, the
technology must be significantly reliable and universally accepted. All the new
printers or processes for printing have not gone beyond the five AM categories
and most work with a single material, which limits their industrial applicability.
There are several improvements in the process/product that need consideration for
betterment to develop the next-generation process for industrial mass food
fabrication.
• Improved printing times can be achieved by the use of multiple-nozzle systems
and the deposit of multiple components on the same layer. The raw material
feeding mechanism must be streamlined by minimising the number of steps and
making it a continuous process.
• Increase the variety of raw materials available to meet the diverse needs of
consumers while also making them responsive to hybrid printing technologies.
• For systems intended for mass food production, safety is imperative. The machine
parts and food contact surfaces engaged during processing need to be food grade
and many printed foods need to be refrigerated during transportation and then
heated before eating. Therefore, the fabricated structure should possess the
mechanical strength to handle all the post-printing stresses.
• Ease in operation and maintenance of the printer is crucial to preserve the high
quality over a long period. Easy cleaning/self-cleaning printers with proper
protocol to ensure no residues are left after printing is complete. This also helps
ensure a safe and good quality food product.
• Low energy consumption printers capable of creating high resolution of scale
products are better suitable for the industrial manufacturing process.
7 3D Printing: Technologies, Fundamentals, and Applications in Food Industries 229

Printing precision and accuracy are critical in improving production efficiency

which in turn will reduce production costs. In the future, 3D printers are likely to
become a fundamental part of every household kitchen and restaurant where con-
sumers can print and customize every meal. And many industries will benefit from a
multi-process hybrid system (i.e., additive technologies + conventional food
manufacturing processes) to match the consumer demand. It is apparent from the
above review that current 3D food printing/printers still have a long way to go before
they can achieve their full potential to fulfill the envisioned tasks.

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Chapter 8
Smart Food Packaging Systems

Aastha Bhardwaj, Nitya Sharma, Vasudha Sharma, Tanweer Alam,

and Syed Shafia

Abstract Modern consumer needs have brought about a colossal revolution to the
global food packaging industry. Smart food packaging is one such innovative
packaging solution that detects/senses/records changes in the food product quality,
both internally and externally, with respect to the product environment and commu-
nicates it efficiently to the consumer. This interactive packaging uses an integrated
approach with mechanical, chemical, and electrical-driven functions that help the
manufacturers monitor shipping duration, product freshness, reinforce safe handling
and product integrity, and allow traceability. Active and intelligent packaging is a
classic example of smart “interactive” food packaging that involves the incorpora-
tion of active components into the package that helps in product shelf life extension
as well as maintaining product quality while intelligent systems convey information
about product freshness during transit using indicators, sensors, and data carriers.
Smart packaging finds its application in dairy products, the meat and seafood
industry, and bakery and confectionery products. This chapter provides an insight
on the technology and elaborates on smart packaging systems for various food
sectors, recent trends, advantages, and challenges related to their commercialization.

Keywords Smart packaging · Active packaging · Intelligent packaging · Sensors ·

Interactive · Food systems

A. Bhardwaj (*) · V. Sharma · S. Shafia

Department of Food Technology, Jamia Hamdard, New Delhi, India
e-mail: aastha.bhardwaj@jamiahamdard.ac.in
N. Sharma
Centre for Rural Development and Technology, IIT Delhi, New Delhi, India
T. Alam
Indian Institute of Packaging, Mumbai, India

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 235
S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies,
https://doi.org/10.1007/978-981-19-1746-2_8
236 A. Bhardwaj et al.

8.1 Introduction

The prime function of food packaging involves restricting the migration of sub-
stances from outside into the food (i.e., containment and protection) and retention of
its sensory attributes. However, these functionalities of conventional food packag-
ing, though utmost essential, are no longer sufficient to ensure food safety and
delivery of wholesome foods to consumers. According to the Food and Agricultural
Organization (UN 2017), approximately 1.3 billion tons of food is wasted every year
which accounts for one-third of the total food produced globally, and these losses are
predominant at the post-harvest stage, during processing, distribution, as well as
consumption stage. Real-time information about the condition of the food product
during transit and distribution has become quintessential to ensure safer delivery.
This has urged the development of systems that can interact with food constituents or
the food environment and report on the food quality in real-time. Smart food
packaging is one such innovative packaging solution that detects/senses/records
alterations in the food product quality, both internally and externally, with respect
to the product environment and communicates it efficiently to the consumer. Active
and intelligent packaging (IP) is a classical example of smart “interactive” food
packaging that involves the incorporation of active components into the package that
helps in product shelf life extension as well as maintaining product quality while
intelligent systems convey information about product freshness during transit with
the help of indicators, sensors, and data carriers. Active packaging systems involve
the inclusion of oxygen scavengers, moisture absorbers, carbon dioxide (CO2)
emitters and absorbers, ethylene absorbers, flavor-releasing/absorbing systems, anti-
microbial agents, antioxidants, preservatives, etc., as pads or sachets or incorporated
directly into the package polymer film, to minimize deleterious consequences of
external variables on food quality (Bhardwaj et al. 2019). Indicators, sensors, and
data carriers are the chief technologies that an IP system encompasses. Indicators
such as time–temperature indicators (TTIs), freshness indicators and gas indicators
may be placed internally or externally to the food package and convey information
regarding product quality by monitoring changes in the interaction of its components
and outer environment (Drago et al. 2020). This is usually related to sensing a
qualitative change in the concentration of a particular constituent attributed to a
chemical reaction when the package is exposed to variable conditions during
distribution and storage. In most cases, a perceptible visual (colorimetric change)
alteration indicates the change in product quality or freshness. The terms “indica-
tors” and “sensors” may be used interchangeably in general; however, a technical
difference between the two is that an indicator demonstrates qualitative or semi-
quantitative visible response while a sensor is essentially connected to a device for
signal transduction of the receptor and provides quantifiable information. Sensors
involve receptors, transducers, and processors to detect the chemical, biological, or
physical changes in product quality and pertain to factors such as temperature,
humidity, pH, and light exposure (Ghaani et al. 2016; Vanderroost et al. 2014).
Unlike the two, data carrier devices provide information on product traceability, theft
8 Smart Food Packaging Systems 237

cases, and provide protection against counterfeit (Müller and Schmid 2019). The
most conventional form of data carriers is one-dimensional barcode which facilitates
inventory control, stock reordering, and product checkout. The latest intervention is
a Quick Response (QR) 2-D barcode that reads vertically as well as horizontally and
provides impactful data on the product’s source history, origin, manufacturing and
distribution dates, nutritional composition, allergen(s), instructions of preparation,
storage, and much more. Meanwhile, these have been further developed and inte-
grated into TTIs. The most advanced type of data carrier is radio frequency identi-
fication systems (RFIDs), often present in the form of tags and have the ability to
transfer and communicate information even over long distances, thus improving
automatic product identification and traceability operations (Plessky 2009).
In general, smart packaging technology has a wide variety of potential application
fields from monitoring food safety and drug use to tracking postal delivery of items
via embedded security tags (Arvanitoyannis and Stratakos 2012). Thus, keeping in
view the utility and applicability of these interactive systems, the present chapter
provides an insight on the technology and elaborates on active and intelligent
packaging systems for various food sectors, recent trends, advantages, and chal-
lenges related to their commercialization.

8.2 Active Packaging Applications

Active packaging (AP) employs technology that aims at intentionally releasing

active compounds to the food or absorbing compounds from the food headspace,
in order to stall degradative food reactions such as lipid oxidation, microbial and
enzymatic growth, and moisture loss or gain. Both scavenging and releasing systems
facilitate optimal shelf life of the food product with retention of nutritional and
sensory attributes throughout the supply chain. As stated previously, active packag-
ing systems involve the inclusion of oxygen scavengers, moisture absorbers, CO2
emitters and absorbers, ethylene absorbers, flavor releasing/absorbing systems,
antimicrobials, antioxidants, etc., as pads or sachets or incorporated directly into
the package polymer film, to minimize deleterious consequences of external vari-
ables on food quality. AP is given due credit as a smart packaging system because it
interacts with the system and brings about positive changes with respect to product
sensory and nutritional quality and shelf life elongation. The technology bolsters the
protective function of the package, thereby enhancing product integrity, by the
inclusion of scavenging and releasing agents (active layer). Figure 8.1 shows a
schematic illustration of an active packaging system, and various variants with
their applications have been discussed henceforth:
(a) Moisture Scavengers
The presence of moisture in packed and stored foods with high water activity
such as fresh fruits and vegetables, meat, fish, poultry, etc. is a major reason for
microbial spoilage. Moisture absorbers, in the form of sachets, sheets, or pads,
238 A. Bhardwaj et al.

Fig. 8.1 Schematic representation of an active packaging system/setup

are incorporated into food packages to control the accumulation or presence of

additional moisture. This may be done by absorbing water in the form of a vapor
or liquid, thus getting them prevented from being spoiled. Examples of sub-
stances that may be used to control humidity in the package headspace are silica,
calcium oxide, calcium alumina silicate, and activated clay (also known as
desiccants). In moisture-permeable sachets, desiccants absorb water vapor
from the contained product and the package headspace and absorb any water
vapor that enters by permeation or transmission through the package structure.
Films having a moisture absorbing function present a way forward for desiccant-
free product, thus reducing their accidental consumption risk. Additionally,
nanomaterials provide a range of wide spectrum antimicrobial properties as
well as improved mechanical performance. For instance, nanoclay improves
the gas and moisture barrier properties of polymer films. Formulation of
nanobiocomposites combining nanosilver with nanoclay or other nanomaterials
(e.g., titanium dioxide) to enhance both barrier and antimicrobial properties has
been studied for potential future applications in food packaging (Cozmuta et al.
2014). Several examples of commercially utilized moisture scavenger pads and
sachets include Dri-Loc by Novipax, absorbent pads by Thermasorb, and
MeatPad by McAirlaid’s Aptar. Table 8.1 provides a few examples of moisture
absorbent films utilized for various foods.
(b) Oxygen Scavengers
Oxygen is one of the major causes of chemical and microbial food spoilage,
and thus, exclusion of oxygen is crucial in determining the quality of food
products that reach the final consumer. The presence of oxygen may be
8 Smart Food Packaging Systems 239

Table 8.1 Selective examples of active food packaging systems with their respective applications
from recent literature
Active Application
property Film composition/polymer matrix intended Reference
Moisture Nanocellulose and nanowood starch-based bio- All perish- Ebadi et al.
absorbers degradable film able foods (2021)
Poly(vinyl alcohol) incorporated with green tea Dried eel Chen et al.
extract (2018)
Polyvinyl chloride films—silica gel Guava Murmu and
Mishra (2018)
Oxygen Polyisoprene-based UV-activated film Beef jerky Gaikwad et al.
scavengers (2020b)
Chitosan–gallic acid/sodium carbonate films Foods Singh et al.
(2021)
LDPE–sodium ascorbate nanoparticle Peanuts Modaresi and
Niazmand
(2021)
PET/SiOx with loaded palladium-based film Linseed oil Faas et al.
(2020)
Ultra-high-barrier flexible multilayer film Peach puree Skrypec et al.
(2021)
Carbon Agar-based label incorporated with Na2CO3 Shiitake Wang et al.
dioxide mushrooms (2015a, b)
scavengers
Ethylene Polyvinyl acetate/rice husk–egg shell powder Tomato Haider et al.
scavengers film (2020)
Potassium permanganate-impregnated Fresh fruits Joung et al.
halloysite nanotubes loaded into low-density and (2021)
polyethylene film vegetables
Sepiolite-loaded potassium permanganate eth- Apricots Álvarez-
ylene scavenger sachet Hernández
et al. (2020)
Antioxidant Starch films based on red cabbage extract and Ground beef Sanches et al.
emitters sweet whey (2021)
Chitosan films modified with mango leaf extract Cashew nuts Rambabu et al.
(2019)
Edible cassava starch films—rosemary extracts Fatty foods Piñeros-
Hernandez
et al. (2017)
Polyethylene—fish protein hydrolysates (radi- Seafoods Romani et al.
cal scavenging activity) (2020)
Poly(ε-Caprolactone) and almond skin extract Fried Valdés García
film almonds et al. (2020)

attributed to insufficient flushing during packaging or permeation through the

package (Yildirim et al. 2018). Oxygen scavengers or absorbers are added to a
food package to stall oxidative reactions and inhibit food spoilage. These are
mostly based on the oxidation of iron, ascorbic acid, or by using enzymes (e.g.,
240 A. Bhardwaj et al.

glucose oxidase) to scavenge oxygen. Iron-based scavengers are based on the

oxidation of ferrous salts (in the presence of water) to form a stable ferric oxide
trihydrate complex. Similarly, ascorbic acid-based scavengers oxidize ascorbate
to dehydroascorbic acid. Exclusion of oxygen may also be achieved by
employing gas flushing or modified atmospheric packaging (MAP) technology.
Moreover, a recent and emerging trend is the use of photochromic dyes, color-
ants, or bioactive agents immobilized onto packaging film surfaces and inclusion
into polymer matrices of packaging materials to be used for oxygen-sensitive
foods. This also caters to the fact that consumers do not like the idea of having
anything foreign, such as labels or sachets, within the package, close to the
edible material. Nanoactive oxygen-absorbing films bolster up the functionality
of films by providing an enhanced barrier and mechanical properties due to the
presence of nanomaterials. For instance, Cherpinski et al. (2019) developed
novel oxygen scavenging nanocomposite film, comprising cellulose
nanocrystals and palladium nanoparticles incorporated into ethylene vinyl alco-
hol copolymer (EVOH). Palladium nanoparticles, followed by cellulose
nanocrystals, are the active agents that demonstrated high oxygen scavenging
activity. This makes these nanocomposites very promising candidates as active
packaging materials for oxygen-sensitive foods. Table 8.1 provides a few exam-
ples of potential oxygen scavenging technologies and their intended food pack-
aging applications. Some commercially available oxygen-scavenging solutions
are SHELFPLUSR O2 (OS-masterbatch, Albis Plastic GmbH, Del., Sugar Land,
TX, USA), AMOSORB™ ColorMatrixTM (different OS-solutions, PolyOne™,
Europe Ltd., Liverpool, UK), Cryovac® (OS-film, Sealed Air Corporation,
Charlotte, NC, USA), and AGELESS OMAC® (OS-film, Mitsubishi Gas Chem-
ical Inc., New York, NY, USA).
(c) CO2 Absorbers/Emitters: Carbon dioxide finds potential applications in mod-
ified atmospheric foods; however, its presence may be detrimental to food
products (e.g., fresh horticultural produce), when present above a threshold
level. This makes essential the use of CO2 scavengers inside the packages as
sachets or labels. Common mechanisms that are exploited for CO2 scavenging in
food packages are chemical reactions and physical adsorption. CO2 may react
with alkaline solutions and salts to remove the gas. Calcium hydroxide is the
most commonly used scavenger for carbon dioxide as it reacts with CO2 to form
CaCO3, a compound safe for food contact. This reaction, however, is moisture
dependent. Calcium hydroxide-based carbon dioxide scavengers are used as
absorbents in coffee packaging to delay oxidative flavors and to absorb the
occluded carbon dioxide produced in the roasting process and, which if not
removed would cause the packages to burst (Smith et al. 1995). The other
mechanism involves physical adsorption that includes physical adsorbents
such as zeolite and activated carbon, upon which CO2 can be adsorbed (Lee
2016).
Unlike scavengers, utilization of carbon dioxide emitters is more relevant to
modified atmosphere packaged foods. Optimal concentrations of CO2 inside the
package prevent microbial growth and decay. Also, the release of CO2 inside the
8 Smart Food Packaging Systems 241

Fig. 8.2 Classification of ethylene scavengers and examples

package headspace may help in compensating for CO2 absorption into the food
product in the initial stages of storage, by way of which it counteracts the
formation of negative pressure in MA packages that increase the drip loss of
the product, giving packages an unattractive appearance (Yildirim et al. 2018).
Commercially used CO2 emitters are Verifrais™ (SARL Codimer, Paris,
France). Dual-action oxygen scavenger and carbon dioxide emitter sachets and
labels by the name of Ageless® GE (Mitsubishi Gas Chemical Company, Inc.,
Tokyo, Japan) and FreshPax® M (Multisorb Technologies Inc., Buffalo, NY,
USA) are more common. These systems are generally based on either ferrous
carbonate or a mixture of ascorbic acid and sodium bicarbonate (Vilela et al.
2018; Coma 2008). The main food applications for these dual-action oxygen
scavenger/carbon dioxide emitter sachets and labels have been with snack food
products, e.g., nuts and sponge cakes.
(d) Ethylene Scavengers: Ethylene is considered an essential evil owing to the fact
that the horticultural produce requires ethylene for ripening purposes while an
excess of ethylene generation becomes the cause of early senescence of the plant.
Thus, ethylene absorbers are employed to remove ethylene gas from the package
headspace. These act by either chemical reactions (e.g., oxidation) or physical
adsorption/absorption (Gaikwad et al. 2020a). Figure 8.2 shows the classifica-
tion with examples.
Packing ethylene scavengers, such as zeolite, titanium dioxide, etc. in a small
sachet or as a label, has been practically used. However, incorporating ethylene
scavengers into food packaging materials or films along with the in situ oxida-
tion of ethylene is gaining prevalence. The development of novel
nanocomposites with ethylene scavenging potential and superior mechanical
and barrier properties is a potential breakthrough in the field of active packaging.
For example, the incorporation of inorganic nanofillers (nanosilica and
nanoclay) with potassium permanganate to polyolefin elastomers demonstrated
enhanced ethylene scavenging potential of the nanocomposite film along with
appreciable barrier properties (Ebrahimi et al. 2021). Table 8.1 shows examples
of ethylene scavenging films and respective food applications. Commercially
242 A. Bhardwaj et al.

used ethylene scavenger sachets are Evert-Fresh (Evert-Fresh Co, USA),

Peakfresh (Peak Fresh Products, Australia), and Ethylene Control (Ethylene
Control Incorporated, USA).
(e) Antioxidants: Fat oxidation is a chief cause of loss of sensory, nutritional, and
textural quality of fatty foods. Antioxidant active packaging is a novel technique
that attempts to interrupt the formation of free radicals that are formed as
intermediate products of fat oxidation. It is one of the releasing types of active
packaging in which an antioxidant is added via packaging material into the
packed food in small amounts during storage or else added in the packaging
headspace to remove unwanted components and thus aims at maintaining
desired shelf life throughout the storage. It is also termed as “interactive pack-
aging” because it provides sustained release of antioxidants during storage and
stays in active interaction with the food. Conventionally, butylated
hydroxytoluene and butylated hydroxyanisole were used; however, Tocopherol,
Caffeic acid, Carvacrol, Quercetin, essential oils, plant extracts, and polyphenols
are a few of the natural antioxidants that are now being given preference.
Antioxidants may also be incorporated into polymer matrices of the packaging
material to produce active packaging films for food packaging applications
(Nwakaudu et al. 2015). These active antioxidant films ensure improved oxida-
tive stability of foods and retention of quality attributes concerning lipids. Some
of the latest developments in this aspect have been summarized in Table 8.1.
(f) Antimicrobial Emitters: Antimicrobial packaging is currently one of the
emerging aspects of active packaging to reduce the risk of pathogens in foods
and extend their shelf life by making use of antimicrobial agents inside a food
package. Antimicrobial agents are used to prevent microbial growth by
extending the lag period and reducing their growth rate or decreasing the live
counts of microbes. This technology involves the inclusion of antimicrobial
agents into either the package headspace or incorporation directly into the
polymer film or by coating onto polymer surface (Malhotra et al. 2015). Active
agents of natural origin such as plant extracts, bacteriocins, phenolic com-
pounds, organic acids, essential oils, waxes, etc. have a wide spectrum of
antimicrobial activity with low toxicity and are being currently used in antimi-
crobial packaging for minimally processed foods, meat, and seafoods, cheese
and dairy-based products, baked products, frozen fruits, etc. (Sharma and
Bhardwaj 2018). Table 8.2 summarizes a few examples of novel antimicrobial
packaging systems for various foods.
Antimicrobial packaging concepts have been further strengthened through
nanotechnological intervention. Nanomaterials are used as nano fillers (through
nanoreinforcements or encapsulation) into the biopolymer matrix not only to
improve mechanically but to enhance antibacterial properties against foodborne
pathogens as well. The nanomaterials in food packaging are categorized into
nanoparticles, nanofibers, and nanolayers. Metal nanoparticles (silver, gold,
titanium, copper) and metal oxide nanoparticles have demonstrated exceptional
antimicrobial activities when incorporated in polymer formulations (Jafarzadeh
et al. 2020). The mechanism of antimicrobial action of nanomaterials can be
8 Smart Food Packaging Systems 243

Table 8.2 Several examples of types of antimicrobial packaging and antimicrobials utilized
Antimicrobial
Polymer matrix agent Application Target microorganism Reference
Bacterial cellulose Chitosan Perishable Staphylococcus aureus Abral et al.
nanofibers/tapioca foods and Bacillus subtilis, (2021)
starch/chitosan based E. coli, and Pseudomo-
edible film nas aeruginosa
PVA (polyvinyl ace- Litsea cubeba Strawberries E. coli, Saccharomyces Thielmann
tate) film fruit essential cerevisiae, and Aspergil- et al.
oil (citral) lus niger (2021)
Polyvinyl alcohol- Grapefruit Perishable E. coli and Roy and
based films seed extract foods L. monocytogenes Rhim
and curcumin (2021)
Low-density polyeth- Yucca baccata Ground beef Pseudomonas Gutiérrez-
ylene (LDPE) butanolic aeruginosa García
extract (YBE) et al.
(2021)
Sugarcane bagasse Nisin Perishable L. monocytogenes Yang et al.
nanocellulose/nisin foods (2020)
hybrid films
Chicken bone gelatin Cinnamon Mozzarella L. monocytogenes Kim et al.
films bark oil cheese (2018)

mediated through several pathways, i.e., disruption of the bacterial membrane,

formation of holes and pits on the cell wall, generation of ROS, binding to
sulfhydryl groups of metabolic enzymes of the bacterial electron transport chain
to inhibit respiratory activity, and integration with DNA (Agnihotri and Dhiman
2017). However, the migration of nanoparticles from packaging films to foods
and ultimately to the consumers and their end-disposal remains a big concern
owing to their potential toxicity.
(g) Preservative Emitters: Ethanol has been used as a preservative in many food
products for preventing microbial growth inside the food package. Bakery
products may be sprayed with 95% alcohol at a concentration of 0.5–1.5% for
appreciable shelf life (Labuza and Breene 1989). However, ethanol-emitting
sachets or films seem to be a more practical and safer approach. For instance,
these comprise ethanol (55%) and water (10%), which are absorbed onto silicone
powder (35%) and are filled in turn with a paper-ethylene vinyl acetate copol-
ymer sachet (Mexis and Kontominas 2014). They may have trace amounts of
flavorings, such as vanilla, to mask the odor of alcohol in package headspace.
Examples of potential applications of ethanol emitters are bakery products and
dried fish. In fact, antimicrobial agents (discussed in the previous section) are a
form of preservatives that help elongate the shelf life of foods. Commercially
available ethanol emitters include Anti Mold, Oitech (Nippon Kayaku Co. Ltd.,
Japan), Negamold (Freund Industrial Co. Ltd., Japan), and Ageless type SE
(Mitsubishi Gas Chem. Co.).
244 A. Bhardwaj et al.

(h) Flavor/Odor Scavengers and Flavor/Odor Emitters: Operations during plas-

tic processing such as molding, sheet blowing, and extrusion may lead to the
breakdown of polyolefins to short-chain hydrocarbons, resulting in the develop-
ment of certain off-odors inside a food package. Also, they may be produced as a
result of a food protein or lipid breakdown resulting in odorous volatiles such as
amines, hydrogen sulfide, ketones, aldehydes, etc., all of which are responsible
for an off taint inside the package. Moreover, the permeability and migration
characteristics of the packaging material also govern the aromas inside the
package. Thus, removal of unpleasant aromas or odors from the package head-
space becomes essential, and this is facilitated by the use of odor scavenging
materials. These materials include ferrous salts, ascorbates, activated carbon/
clay, zeolites, etc. often in the form of sachets or pads. Also, the inclusion of
fragrances and aromas into the packaging film suggests a good avenue for flavor-
emitting packaging. Packaging of ready-to-eat meals, fried snack foods, cereals,
meat, fish, poultry, and beverages uses flavor releasers and odor absorbers.
Gradual release of odors can compensate for the natural loss of taste or smell
of products with appreciable shelf life and greater consumer acceptance
(Bhardwaj et al. 2019). A double-function active packaging for nuts constituting
polybutadiene as an oxygen scavenger with incorporated peanut aroma to the
packaging film was found to be effective against peanut oxidation in nuts and
also enhanced customer’s sensorial experience (Juan-Polo et al. 2021).

8.3 Intelligent Packaging Applications

Intelligent packaging may be rightly defined as a science and technology that utilizes
the communication function of a food package to facilitate shelf life extension,
safety, providing real-time quality data, and alert regarding any deviations in the
internal and external environment of the package, from the optimal conditions of
storage and transportation. These systems generally attached as labels, incorporated
into, or printed onto a food packaging material, are primarily employed to track and
monitor the quality of packaged foods, to capture and provide data of the product’s
condition throughout the supply chain (Chen et al. 2020). Indicators, sensors, and
data carriers are the chief technologies that an IP system encompasses. Time–
temperature indicators (TTIs), freshness indicators, and gas indicators are the most
common types of food product quality and safety indicators witnessed in the
intelligent food packaging sector. The following section also covers the relevance
and applications of sensors and RFID tags in smart food packaging.
(a) Freshness Indicators: Freshness indicators often employ information on metab-
olites that are produced during microbial decay or deterioration of a food
product. In most cases, freshness indicators are based on the use of dyes sensitive
to pH variations caused by the deterioration of the product, which leads to the
visible change in the color of the indicator. These may be present internally or
externally and work on colorimetric detection upon a change in pH values due to
8 Smart Food Packaging Systems 245

Fig. 8.3 Schematic representation of a freshness indicator based on pH change

spoilage, and the rate of color change is correlated with temperature and time
variation. A typical pH-based freshness indicator involves pH-sensitive dyes and
solid supports that act as an entrapment matrix and can immobilize these dye
molecules through physical adsorption, physical entrapment, or covalent binding
(Mohebi and Marquez 2015). These indicators alert the consumers of food
spoilage or senescence during transit or storage, despite the stated best before
or expiry dates. They facilitate real-time monitoring of food products such as in
case of interruption of the temperature-controlled supply chain (e.g., cold chain)
or imperceptible loss of package integrity (e.g., aseptic packaging). A range of
different freshness sensors such as ripeness indicators, leak indicators, and
microbial indicators, that employ a similar principle, have been projected in
the literature. For instance, microorganisms with proteolytic activity may act on
long-chain proteins and convert them into free amino acids that further undergo
oxidative deamination, decarboxylation, and desulfurization, resulting in the
production of metabolites (Rukchon et al. 2014). These metabolites include
carbon dioxide, ammonia, hydrogen sulfide, dimethylamine, and
trimethylamine, which are quantified based on a perceptible color change.
Similarly, electronic nose (e-nose) technology has been explored for assessment
of fruit ripening stage based on the volatiles it emits, during different stages of
maturity. The maturity of tomatoes was assessed using e-nose systems (Gómez
et al. 2006), and such systems can be further applied to food packaging to detect
changes in fruit and vegetable aromas.
Figure 8.3 shows a schematic representation of a freshness indicator based on
variation in food pH owing to the production of volatiles.
Table 8.3 summarizes a few examples of freshness indicators intended for
adjudging the quality of fresh fruits, meat, and seafood products. For example, a
high confidence freshness indicator involving a pH-responsive dye, bromocresol
green, exhibiting a color change from yellow to green, was utilized to detect
spoilage in the chicken breast as the time and temperature of storage of chicken
breast increased.
Synthetic dyes that are sensitive to pH such as bromothymol blue,
bromocresol green, bromocresol purple, methyl red, cresol red, etc. are usually
integrated within a freshness indicator (Roy and Rhim 2020). However, owing to
their toxic effects on humans, biopolymer-based pH-responsive color indicators
246 A. Bhardwaj et al.

Table 8.3 Freshness indicators intended for various food products

Color
change
indicating
Freshness indicator Food product spoilage/
composition Metabolite application decay Reference
Low-density polyethylene/ Total volatile Chicken Yellow to Lee et al. (2019)
bromocresol green + eth- basic nitrogen & breast green
ylene vinyl acetate/ carbon dioxide
Tyvek®) freshness
indicator
Methyl red—cellulose 2-Thiobarbituric Beef Red to Lee and Shin
acetate/dibutyl phthalate, acid-reactive sub- yellow (2019)
inserted between PET and stances & volatile
polytetrafluoroethylene basic nitrogen
Bromophenol blue/bacte- Ethanol, acetalde- Guava Blue to Kuswandi et al.
rial cellulose membrane hyde, and acetic green (for (2013)
acid overripe
indication)
Nylon/LLDPE film + Carbon dioxide Intermediate- Green to Nopwinyuwong
bromothymol blue and moisture orange et al. (2010)
methyl red egg-based
dessert

integrated with natural colorants such as anthocyanins, curcumin, shikonin,

alizarin, beetroot extract (betalains) are gaining prevalence for real-time moni-
toring of packaged food quality (Priyadarshi et al. 2021). The applicability of
indicators is dependent on a number of variables such as the chemical compo-
sition and moisture content of the indicator, the rate of reaction on target volatile
substance, and location in the packaging space. A major disadvantage of color-
imetric freshness indicators is that the color change can occur even in the
absence of contaminants and significant deterioration of the product.
Interestingly, the incorporation of nanomaterials in biodegradable polymer
films to form bio-nano composites provides enhanced mechanical strength and
serves as a barrier for moisture, gases, and microorganisms (Mustafa and
Andreescu 2020). A recent trend in the area of nanotechnology concerning
intelligent packaging aims toward developing bioactive and pH-responsive
films incorporated with nanoparticles for real-time monitoring of food quality.
For instance, a pH-responsive antioxidant soy protein isolate film, incorporated
with cellulose nanocrystals and curcumin nanocapsules, was developed to mon-
itor shrimp freshness, in response to pH change and the presence of ammonia
(Xiao et al. 2021).
(b) Time–Temperature Indicators (TTIs): One of the most critical environmental
parameters that influence the shelf life and textural quality of many food
products is temperature. Temperature abuse of chilled foods is a frequent
problem that permits spoilage by microbial enzymes when the temperatures of
storage or transit exceed the desired temperature Pavelková (2013). Hence, TTIs
8 Smart Food Packaging Systems 247

serve as an intelligent solution for safer food delivery. Time–temperature indi-

cator is a tag or a smart label that displays a visual summary of the time–
temperature history of a food package during distribution and transit, thus
making it easy for the producers, retailers, and consumers, to ascertain if the
product is in an acceptable condition in terms of its quality. Any physical,
chemical, enzymatic/microbial change in the food product is expressed by
mechanical deformation, color development, or diffusion and is directly related
to temperature change. Melting, acid–base reactions, polymerization, etc. signify
physical or chemical changes to the food product while time–temperature-
dependent pH change is usually attributed to microbial growth. The response
of a microbial TTI is directly linked to the microbial spoilage of the food, as the
bacterial growth that occurs in the food is correlated with the bacterial growth
and the metabolism within the TTI system (Mataragas et al. 2019).
TTIs may be classified as partial history indicators that act in response to an
exceeded threshold temperature and are intended to recognize abusive temper-
ature conditions, while the full history indicators respond consistently to all
temperature levels. The most important food sector for which TTIs hold great
relevance is the cold chain logistics owing to the fact that refrigerated or chilled
food products are highly sensitive to fluctuations in temperature. Thus, real-time
monitoring of the temperature is crucial in order to reduce food wastage and to
ensure food safety. There have been ample studies on the use of TTIs for quality
monitoring of chilled-stored fish and meat products. TTIs have also been applied
for the assessment of the quality of other food products such as frozen vegeta-
bles, poultry and dairy products, and fresh mushrooms (Chowdhury and Morey
2019; Kim et al. 2016; Vaikousi et al. 2008; Bobelyn et al. 2006). Table 8.4
shows various categories of TTIs employed for food applications and their
respective underlying principles.
In addition to these applications, a novel Maillard reaction-based TTI has
been recently developed that can monitor the concentration of fluorescent
advanced glycation end products that are related to chronic diseases, in various
re-heated foods (Hu et al. 2020). Many commercially available TTIs such as 3M
MonitorMark® (3M Co., St Paul, Minnesota), Timestrips® (Timestrip UK Lim-
ited, UK), Fresh-Check® (Temptime Corp., Morris Plains, NJ, USA), Check-
Point® (VITSAB A. B., Malmö, Sweden), etc. present reliable and reproducible
responses regarding efficient management of cold chain distribution system,
improvement in shelf life, monitoring, and thus reducing product wastage.
Figure 8.4a–d shows the schematic representation of these TTIs.
(c) Integrity Indicators: Also known as gas indicators, these visual leak indicators
are attached to the food package and are based on the detection of oxygen (O2)
and carbon dioxide (CO2) inside the package as leakage prevention is a crucial
aspect to be considered throughout the distribution chain of packaged food.
Modified atmosphere packaged (MAP) foods essentially require this element of
package integrity as they can monitor the levels of oxygen and carbon dioxide at
all times. The indicator may assume the shape of a label or be present as a printed
layer, or a tablet, or it may also be laminated in the polymer layer itself and is
248 A. Bhardwaj et al.

Table 8.4 TTIs employed for food packaging applications

Underlying Food Type of
Category principle Description application response Reference
Chemical Photochromic Based on a pho- Chilled Indicator discol- Brizio and
based tochromic com- chicken- oration with Prentice
pound that is based increasing time (2014)
activated by cer- products and temperature
tain wavelengths of storage
(light radiation
or heat) to
exhibit a color
change
Polymerization Solid-state poly- Perishable Color change is Wang
based merization reac- food measurable as a et al.
tion of a products decrease in (2015a, b)
monomer with reflectance;
acetylene group faster rate of
when subjected color change
to high tempera- with the increase
ture or radiation, in temperature
absorption spec-
trum shifts from
high band to the
low band; lead-
ing to color
change
Redox reaction Based on redox Fresh foods Indicator discol- Galagan
based reactions (may (fruits and oration with and Su
or may not be vegetables, increasing time (2008)
light-induced); meat) and temperature
e.g., anthraqui- of storage (beige
none com- to red color
pounds change)
decomposed to
crimson parti-
cles, in the pres-
ence of oxygen
Physical Diffusion based Makes use of Kiwifruit, Red-green-blue Yang and
temperature- strawberry, (RGB) values Xu (2021)
dependent diffu- and mango
sion of a colored (fruits)
chemical sub-
stance, such as a
fatty acid ester,
through a porous
matrix made of
high-quality
blotting paper
Enzymatic Acid–base Thermo-sensi- Spoilage of Lipase-based Casanova
reaction based tive reaction meat TTI acting on et al.
between enzyme (gondolas) tributyrin (2020)
(continued)
8 Smart Food Packaging Systems 249

Table 8.4 (continued)

Underlying Food Type of
Category principle Description application response Reference
and substrate; mimicking the
production of an spoilage in meat
acid/base by product leading
enzymatic to pH change:
hydrolysis to green to orange
increase or color change
decrease pH
value and then
to dynamically
display the
cumulative
effect of time
and temperature
through color
change
Glucose oxidase Fruits, veg- Quality loss due Li et al.
and horseradish etables, and to enzymatic (2019)
peroxidase fish lipid oxidation:
bi-enzyme TTI products blue-green to
orange
Based on Material Indicating Amylase-based Yang and
steaming time becomes trans- instant noo- enzymatic TTI Lee (2019)
parent at a dles pertaining to
threshold hot doneness starch-iodine
temperature complex degra-
when a specific dation: dark blue
amount of steam to colorless
has been
absorbed by the
material
Biological Microbial (bac- Makes use of Spoilage of Violet color col- Mataragas
terial) based metabolites gen- meat onies due to the et al. 2019)
erated by production of
microbes, per- violacein by
mitting a change bacteria: color
in pH; conse- development
quently leading Spoilage of Changes in lac- Kim et al.
to a color milk tic acid content (2016)
change to indi- (pH) lead to
cate the accu- color change
mulation effect
of time and
temperature

usually placed inside the package. Visual oxygen indicators are composed of
redox-sensitive dyes which change color with a change in oxygen concentration
in MAP foods. Similarly, carbon dioxide indicators are also used in MAP foods
to monitor their desired concentration inside the package through conventional
250 A. Bhardwaj et al.

Fresh Used soon Should not be used

b
3
6 months

1 6

Life in service

c
Index
10 °C (50°F), 1 week
TTI
9860 D 0 1 2 3 4 5

Do not use if the

circle is pink

Fig. 8.4 Schematic representation of some commercially available TTIs (from Fuertes et al. 2016).
(a) Fresh-Check; (b) Timestrip; (c) MonitorMark; (d) CheckPoint

electrochemical and optical methods. For instance, Moonstone Co. designed a

smart label containing a gas-sensitive dye, which was inserted into the food
package, and the dye could change colors based on the concentration of carbon
dioxide at several time points. When carbon dioxide leaked or diffused out of the
MAP, the dye changed color from dark blue to yellow (Pavelková 2012). Recent
developments to these include wet optical CO2 indicators (pH based), dry optical
8 Smart Food Packaging Systems 251

CO2 indicators, fluorescent CO2 indicators, sol-gel-based optical CO2 sensors,

photonic crystal sensors, and polymer hydrogel-based CO2 sensors (Puligundla
et al. 2012).
Besides numerous advantages, they have a few major limitations too. CO2
indicators are not able to sense the accurate CO2 content inside the package,
especially in the case of microbial decay which involves the production of CO2
as a metabolite or gradual use up of O2 in the package, thus leaving behind CO2
in the headspace. This means that even if the package undergoes carbon dioxide
leakage, its content would still be sensed as constant owing to the microbial
metabolism. Also, issues such as humidity interference, lifetime, cost, safety,
and proton generation during other oxidative reactions are to be considered
before realizing the complete potential of pH-based CO2 indicators for foods
(Puligundla et al. 2012).
(d) Humidity Indicators: These indicators help in an inexpensive and reliable
colorimetric detection in response to change in humidity around/inside the
food package, especially for fruits and vegetables. These usually involve a
moisture-sensitive chemical that changes its color when a desired value of
humidity is exceeded. However, in practice, these are usually coupled with
RFID tags for traceability and humidity monitoring of packaged food products.

8.3.1 Sensors and Nanosensors for Smart Food Packaging

Applications

By intelligent packaging, sensors utilize receptors and transducers to detect, record,

and transmit quantifiable data regarding product quality. Examples of sensors used
in intelligent packaging are physical (optical) sensors, chemical sensors, biosensors,
and gas sensors (Dalmoro et al. 2017). Optical biosensors are based on optical
properties of an analyte such as reflectance, bio- and chemiluminescence, UV–Vis
absorption, and fluorescence. Commonly used chemical sensors comprise a receptor
that can identify the concentration of a particular chemical analyte in the package/
product environment and is transmitted to an output signal through the transducer,
indicating food product quality. Similar to chemical sensors, biosensors are based on
detecting a target metabolite produced during chemical reactions associated with
food decay. These comprise biological elements used in the receptor such as pro-
teins, antibodies, hormones, DNA, etc. that recognize the metabolites and trans-
ducers convert the signal to an electronic response. The biological recognition
molecule interacts with the target compound, and the physical transducer converts
the biological response to a detectable signal, quantitates as redox changes, and
detects electrochemically, optically, acoustically, mechanically, calorimetrically, or
electronically, which can be correlated with the analyte concentration (Srivastava
et al. 2018). Likewise, gas sensors detect the presence of gaseous analytes (volatiles)
produced during food spoilage and package leakage, thus providing information on
package integrity.
252 A. Bhardwaj et al.

Fig. 8.5 Components and working of a typical nanobiosensor (Srivastava et al. 2018)

Nanotechnology has a technological overlap with active and intelligent packag-

ing and the contribution of nanosensors in monitoring food quality and ensuring
food safety is immense. Nanosensors are nanotechnology-enabled sensors charac-
terized by high sensitivity and specificity, to identify the presence of an analyte or a
metabolite generated inside the package (Caon et al. 2017). They may be placed
outside the package to monitor environmental conditions or may be placed inside the
package to monitor chemical changes and microbial growth. The recent decade has
seen an increased prevalence in the use of sensors using nanomaterials such as
graphene, graphite, nanofibers, and nanotubes (referred to as nanosensors). These
nanoscale materials demonstrate excellent detection sensitivity owing to their appre-
ciable mechanical and electrical properties and high surface area, thus making them a
good candidate for use in chemical sensors (Park et al. 2015). Metal nanoparticles
like gold nanoparticles, silver nanoparticles, magnetic nanoparticles, graphene
oxide, carbon nanotubes, electronic nose, and electronic tongue have been employed
for nanosensor development, functioning, and enhanced sensing. Nanobiosensors
are used for the detection of toxins (fungi and algae) and pathogenic microorgan-
isms. Detection of foodborne pathogens and toxins is usually achieved by exploiting
the optical (optical sensors) or electronic (electrochemical sensors) properties of the
nanomaterial (Pérez-López and Merkoçi 2011). Various nanostructures like thin
films, nanorods, nanotubes, and nanofibers have also been explored for possible
applications in biosensors, for their rapid detection and cost-effectiveness. Further-
more, reduced graphene oxide nanocomposite-based electrochemical biosensors for
monitoring foodborne pathogenic bacteria are also being currently exploited for
potential applications in intelligent packaging (Yu et al. 2021). Figure 8.5 demon-
strates the working of a typical bionanosensor for microbial and toxin detection.
Likewise, an optical gas sensor based on polyetherimide (PEI) and silver
nanoparticles was developed for packaging and food freshness indicator in salmon,
8 Smart Food Packaging Systems 253

chicken, and turkey products, in response to gases produced and released by them,
thereby producing a color change (Ryspayeva et al. 2018). Another hydrogen sulfide
sensor based on gellan gum-silver nanoparticles developed by Zhai et al. (2019)
produced a quantifiable colorimetric change in response to volatile components
generated from chicken breast and silver carp during spoilage. In a nutshell,
nanosensors and nanobiosensors have potential application in the food sector in
monitoring food processing, food quality assessment, food packaging, food storage,
monitoring of shelf life and viability, an indicator of food safety and microbial
contamination, toxin, and residual contamination in food (Srivastava et al. 2018).
Thus, an intelligent packaging integrated with a nanosensor ensures good quality of
products for end consumers by efficient monitoring of packaged food products and
environmental conditions through the supply chain.

8.3.2 Radio Frequency Identification Systems (RFIDs)

The emergence of RFID technology and its contribution to the agri-food sector has
modernized the food supply chain processes and facilitates reliable real-time infor-
mation that guarantees food safety as well as regulatory compliance. These are often
present as tags, labels, or microchips and can exchange information with a reader
using radiofrequency. Information that is transmitted may include details of the
product and the manufacturer as well as environmental factors such as temperature
and relative humidity (using sensors), under which the package was shipped and
distributed. An RFID system is composed of three main elements: a tag consisting of
a microchip connected to a small antenna; a reader that emits radio signals and
receives responses from the tag in return; and middleware (a local network, web
server, etc.) that connects RFID hardware and enterprise applications (Drago et al.
2020). Figure 8.6 gives a summarized illustration of an RFID setup. The reader emits
radio signals with varying frequency values for the operation as follows:

Fig. 8.6 Components and operation of an RFID system

254 A. Bhardwaj et al.

1. Low frequency (LF RFID Tag): 125 kHz or 134 kHz, range: up to 10 cm
2. High frequency (HF RFID Tag): 13.5 MHz, range: up to 10 m
3. Ultra-high frequency (UHF): 860–960 MHz, range: 10–15 m
Since LF and HF RFID have a low range of frequency, these tags work on the
principle of near field inductive coupling, i.e., the transfer of energy from one circuit
component to another through a shared magnetic field, while UHF RFID tag works
on far-field electromagnetic coupling. These tags are attached to the food package
that is intended to be tracked. As soon as the RFID tag receives the transmission
from the reader that emits radio signals, the energy runs through the internal antenna
to the tag’s chip. The energy activates the chip, which modulates the energy with the
desired information, and then transmits a feedback signal toward the antenna/reader.
Since it is based on radio signals, the information can be transmitted over long
distances, unlike a barcode system which works only when the object is in the sight
of a barcode reader.
Sensor RFID tags detect environmental changes and events and communicate the
data wirelessly to a reader. Depending on the sensor, the tags can sense variations in
motion, humidity, temperature, and pressure. For instance, temperature sensor-RFID
tags are gradually becoming an essential element in cold chain logistics management
(fresh fruits, vegetables, meat, and poultry products). The integrated temperature
sensor-RFID tag can monitor the real-time temperature of the food products and alert
about the deviations if any. The values are gathered by the temperature sensor into
the RFID tag chip repeatedly. This data in the chip, when uploaded to the RFID
reader gets transferred to the computer system, while the RFID tag reads the signal
from the antenna. It can also read all the point-to-point temperature data of the whole
circulation process at one time and generate a static temperature change figure to
monitor the temperature change of the whole cold chain logistics system conve-
niently (Zhou 2021). Thus, sensor-RFID technology not only provides greater speed
and efficiency in-stock operations, better inventory tracking throughout the supply
chain, but also facilitates enhanced forecasting about product quality until it reaches
the end consumer (Cuinas et al. 2014).
RFID tags may also be classified as active, passive, and semi-passive. Active tags
own a power supply, and in order to transmit the signal back to the reader, they rely
on their own power supply. Passive tags do not have their own power supply, rather
rely on radio waves coming from RFID readers for the source of energy. Semi-
passive tags have their own power supply but in order to transmit the feedback signal
back to the RFID reader, they rely on the signal coming from the RFID reader.
Conventionally, RFID tags are based on chips containing memory that could be read
wirelessly to uniquely identify products. However, chipless RFIDs have been
identified as a breakthrough technology because they remove the cost associated
with the chip, being at the same time printable, passive, low-power, and suitable for
harsh environments (Mulloni and Donelli 2020).
The data created by RFID is integrated during manufacturing, allowing auto-
mated data capture and traceability through the supply chain. When the shipment is
ready, the information on the RFID label is scanned into a distributed storage system
8 Smart Food Packaging Systems 255

such as blockchain, client-server. This enables transparency of the product. At the

food outlets, RFID-enabled packaging helps to improve accuracy and management.
For instance, to see what product is there and where it is with speed and visibility.
This helps the food outlets and supermarkets to put the right product in the right
place at the right time which increases consumer satisfaction, and this helps in
increasing sales and reducing wastage. However, contamination of food through
RFID stickers, cost ineffectiveness (in comparison to barcode readers), and difficult
implementation due to unreliability in the retail environment are some of its
limitations.

8.4 Major Challenges/Limitations That Halt Widespread

Adoption/Commercialization of Smart Packaging
Systems

Undoubtedly, smart packaging is one of the most sophisticated and versatile tech-
nologies that warrant efficient management of the supply chain, improves consumer
satisfaction through emphasizing product reliability, preventing theft and counterfeit
and managing product wastage. However, for several reasons, this technology is not
yet readily available for mainstream use. The packaging industry and consumers still
are apprehensive and hesitant for its usage for several reasons as discussed below:
(a) High cost: Unlike other conventional packaging systems, smart packaging
systems fetch a higher price in terms of manufacturing and production. These
systems have not yet achieved a mass-manufacturing stage due to various issues,
which would have reduced their costs otherwise.
(b) Lack of sustainability: Some of the smart packaging systems include compo-
nents such as batteries, circuits, and displays that are difficult to recycle. Thus, it
is evident that smart packaging is not a fully sustainable technology and does not
fit into the eco-friendly, green world concept, which the present world is in dire
need of. A probable intervention would be to include these elements that can
convert natural sources to energy sources such as solar energy to electric energy.
Application of “green” flexible electronics that are mounted on biodegradable
materials, such as paper and paperboards may also be sought after.
(c) Legislative complexities for end-disposal: Legislative frameworks worldwide
are not sufficiently flexible and updated to support and keep up with this highly
innovative technology. Device systems such as sensors, RFID tags, and other
electronic elements face a major challenge of end-disposal after their usage. The
disposal of these smart packaging elements is insufficiently regulated under the
packaging act, unlike other conventional packaging. Since these are classified as
electrical equipment, they are not covered under the packaging regulations.
(d) Privacy concerns: One of the major concerns regarding smart packaging is
related to the real-time monitoring and tracking features that they cover. These
systems might aggregate sensitive information and personal details of consumers
256 A. Bhardwaj et al.

such as identity and location. Thus, cryptography and blockchain technology are
required to be implemented efficiently to allow sharing of information only with
the intended recipient.
(e) Safety concerns: Smart systems involve the usage of synthetic dyes and toxic
organic or inorganic compounds that are responsive to changes in pH, temper-
ature, humidity, etc. This presents a major health hazard risk as these dyes may
leach into food products or the package. Thus, efforts are being made to utilize or
replace natural colorants in place of synthetic ones that can show a visual
response. However, another limitation of using these natural dyes on an indus-
trial scale is their low stability. Thus, making use of nanotechnology to increase
their stability has been sought after.

8.5 Conclusion

The last decade has witnessed enormous dominance and expansion in the smart
packaging food industry. Integration of active and intelligent systems to the food
packages offers numerous benefits, in addition to conventional functions, such as
convenience, communication, traceability, decision-making, real-time monitoring,
food safety, shelf life extension, and quality improvement. RFID tags and
nanosensors present a significant opportunity and benefits for the entire food and
beverage packaging supply chain in the near future. More extensive research is
required to cater to the limitations of smart systems, as mentioned before in terms of
cost, sustainability, and wastage.

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 Part III
Smart Food Safety in Food Supply Chain
Chapter 9
Smart Monitoring and Surveillance of Food
Contamination

Shalini Sehgal, Sunita Aggarwal, Ashok Saini, Manisha Thakur,

and Kartik Soni

Abstract Food safety and human health are closely interlinked. In recent years,
many developments have been made in food technology to detect changes in food
during spoilage and in methods to prevent it. Biosensors are highly efficient,
sensitive, and low-cost devices which can detect their targets even at very low
amounts within a short period of time with high accuracy. Thus, a variety of
biosensors such as immunosensors, optical biosensors, microfluidic surveillance
devices, and colorimetric biosensors are used in the food industry for detection of
contaminants. In addition, new technologies such as blockchain and IoT-based smart
technology are being used to enhance food traceability and food safety by eliminat-
ing counterfeit products. These technologies are also useful for improving inventory
management, reducing wastage of food, ensuring food supply chain integrity,
verifying labels’ claims, and improving food industry supplier selection. In this
chapter, we will be discussing the recent advances in technologies used in food
industries for the surveillance and monitoring of food contamination.

Keywords Biosensors · IoT · Block chain technology · Microfluidic systems ·

Contamination · Monitoring · Surveillance

9.1 Introduction

Food quality and safety is the foremost requirement nowadays in the food industry.
Safety and quality of food provided to consumers is directly related to their safety
and health as well as with the reputation of the company producing it. Thus, food
inspection and surveillance at the food manufacturing units is very important. Due to

S. Sehgal (*) · M. Thakur · K. Soni

Department of Food Technology, Bhaskaracharya College of Applied Sciences, University of
Delhi, New Delhi, India
S. Aggarwal · A. Saini
Department of Microbiology, Institute of Home Economics, University of Delhi, New Delhi,
India

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 263
S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies,
https://doi.org/10.1007/978-981-19-1746-2_9
264 S. Sehgal et al.

changing eating habits, evolution of online food providing services, cloud kitchens,
HoReCa (Hotels/restaurant/cafe) around the world have given an opportunity to
enhance the food supply chain management, to emphasize more on the surveillance
and monitoring practices, and innovations in this industry. These practices help in
controlling contamination and spoilage of food to ensure its quality and safety to
protect consumers. Thus, various worldwide regulatory bodies like the United States
Food and Drug Administration (USFDA), Food Safety and Standardization Associ-
ation of India (FSSAI), European Food Safety Authority (EFSA), etc. have been
established to ensure the maintenance of food safety standards in food manufactur-
ing units. They also lay down various science-based standards for articles in food,
and various regulations for the manufacture, storage, distribution, import, and sale
of food.
In this chapter, we will be discussing monitoring, which refers to a continuous,
dynamic process of collecting data about factors controlling safety in the food
industry, over a period of time and surveillance, which is a technically advanced
way and is a more intensive form of data recording than monitoring. The monitoring
and surveillance plays a very important role in food quality and safety assurance, and
various advanced devices used to implement the same.
Whenever an outbreak occurs as a result of food contaminants, it loses not only
consumers, but also the trust of people who consume the particular brand. To
maintain their customer base and reputation in the market, now the food industries
are more concerned about the risks associated with food safety and quality, as well as
their long-term consequences. Each operating unit uses a different perspective and
various monitoring methods, to maintain food safety and quality standards (Grunert
2005).
As a result, a control over the surveillance methods for food commodities is
critical in order to reduce the probabilities and possibilities of risks related to food
contamination and the resulting outbreaks, which may include a variety of methods
related to operations. The currently used methods for any type of analysis are time-
consuming and require highly skilled persons to handle such sophisticated equip-
ment. Also, the sample volumes that need to be analyzed are usually large in number,
due to bulk production in the industry and therefore, traditional procedures are
monotonous and time-consuming to perform a particular analysis. Therefore, rapid
analysis tools play a very important role to monitor the safety and security of
undesirable hazards, pathogens, and contaminants in the food which may cause
foodborne illnesses when consumed by people. Therefore, to overcome all of these
difficulties electrochemical biosensors have been developed to support time man-
agement and include quantitative detection and surveillance of food contamination
and spoilage.
Many examples of outbreak highlight the importance of food safety and its usage
for monitoring, to enable sustaining the food security status of our food production
and supply. For example, in the frozen warehouses, we have data loggers to monitor
the temperature and it alerts employees when the temperature of a freezer or
refrigerator drops outside the safe operating zone (Abdulrahman et al. 2018).
9 Smart Monitoring and Surveillance of Food Contamination 265

When we look for surveillance in food contamination, we consider the farm to

fork process, meaning the entire food chain. In agricultural fields, advanced drones
and GPS systems are placed to avoid spoilage and contamination by insects, birds, or
animals. However, when we talk about food processing in an organization, or a big
firm, there are critical control points or CCPs designed as per the hazard analysis
critical control point (HACCP) implementation, followed by industry personnel to
prevent food from physical, chemical, and biological hazards to establish food safety
norms and quality of the food product. For instance, in order to detect a physical
hazard, various sieves, metal detectors, and X-ray machines are installed at the CCP
area of the processing area (Demaurex and Laurent 2014).
High sensitivity industrial metal detectors are used to check standards of the food
product manufactured using a processing line in a food processing unit. These metal
detectors detect various physical contaminants which can cause serious injuries to
the oral cavity and internal organs of the consumer. There are past instances in
history, showing drastic incidents due to metal and glass contaminants. Other
machines like X-ray systems are also used these days in food industries to detect
the physical contaminants, scan the internal ingredients and physical properties of
the product, and only allow the product to pass standard pre-requisites. Products
having any suspicious micro material are eventually rejected by the X-ray machine
(Haff and Toyofuku 2008).
The basic principle of X-ray systems is to detect contaminants based on the
differences in the density of the product passed through the X-ray machine. Usually,
standard samples used are steel and stainless steel, also glass, sandstone, quartz,
shale, and many others. Sieves and sifters in food manufacturing companies are used
to prevent contamination, to ensure that their products are safe for consumption.
There are two primary types of sieving: safety screening and grading. It is further
divided into various categories considering the application of the sieve. It can be
used for grading only pertaining to prevent the physical contaminant from having a
greater size of the sieve (Liu 2009).
To ensure food safety and quality, modern food production involves many
complex processes that must be strictly controlled. Various aspects such as shelf
life, quality and origin of foods, instrumental methods, and human sensory analyses
need to be determined. Unfortunately, in addition to being time-consuming, these
methods are prone to erroneous interpretation. Over the last decade, new devices
known as intelligent surveillance systems and devices have been marketed as a
low-cost, quick substitute. With a growing demand for advanced monitoring systems
for food processes, the emphasis should be on online or near-real-time monitoring
during the manufacturing process. The food industry requires a quick, standardized,
objective, and profitable tool to control and improve quality (Popa et al. 2019).
266 S. Sehgal et al.

9.2 Recent Advancements in Monitoring and Surveillance

of Food Contamination

Recently, the development of advanced technologies in various electronic devices,

immunosensors, biosensors, drones, etc. is accelerating which are now used to detect
and manage a large amount of food and its intermediates.

9.2.1 Biosensors

Biosensors are analytical devices consisting of immobilized variety of biologically

active compounds such as nucleic acids, enzymes, antibodies, etc. which bind to
their specific targets like DNA/substrates/cofactors/antigens, etc. and transduce the
binding event into signal to the transducer of different types. Different kinds of
signals such as optical, electrochemical, thermal, etc. are generated, when the
immobilized sensing element (nucleic acid, cells, enzymes, antibodies, etc.) interacts
with its specific target molecules which are detected by transducers. The signal can
be measured qualitatively or quantitatively. Due to their properties such as fast speed
of detection, specificity, and low cost, these devices are becoming very popular in
the food industry for monitoring food supply chain surveillance and detection of
metabolic changes going on in the food. They detect the changes like production of
gas, electrons, etc. taking place in the food product and produce a signal in various
forms either qualitatively or quantitatively (Bhatt and Bhattacharya 2019). Thus,
biosensors play a very crucial role in on-site monitoring to ensure food safety and
quality in a real-time manner. Various types of biosensors have been developed
based on the transducing mechanisms such as electrochemical, thermometric, opti-
cal, piezoelectric, etc. Among all types of biosensors, electrochemical biosensors are
more popular and widely used in the food industry due to their easy usage, sensi-
tivity, and well-known transducing mechanism of detecting biomolecular interac-
tions (Aggarwal et al. 2021). The applications of electrochemical biosensors have
been well documented in the food and beverage industry and their capability in the
detection of genetically modified commodities used in the food, etc. (Thakur and
Ragavan 2012; Mishra et al. 2018).

9.2.1.1 Electrochemical Biosensor

Electrochemical biosensors measure the electrochemical signals such as electrons or

ions produced in food commodities due to the interaction of the analyte with the
biorecognition element. Food is a natural product, so it is quite obvious that it will
have metabolic changes and reactions in it like our body, and these changes are
sometimes detectable due to its nature and measurable. These natural changes are
then converted into electrical signals for measurement using an electronic device and
9 Smart Monitoring and Surveillance of Food Contamination 267

the signals are converted into impedance, current, or electric charge which can
further be measured by conductometer (Mishra et al. 2018).
Electrochemical biosensors can be of three types based on the electrochemical
parameters: amperometric, potentiometric, and conductometric. Amperometric bio-
sensors are based on the detection of current produced by movements of electrons,
produced or absorbed during enzyme catalysis or other redox reactions while a
constant voltage is applied. The change of the current is directly proportional to
the substrate concentration. One of the famous and widely used amperometric
biosensors is blood glucose sensing strips used to measure sugar levels for diabetic
patients (Yoo and Lee 2010).
Potentiometric biosensors measure the changes in the ionic concentration using
ion selective electrodes. Various ions are produced in the food due to the variety of
biochemical reactions which can be detected by potentiometric sensors. The change
in the substrate concentration will ultimately change the concentration of hydrogen
ions. The basic and potential difference between the potentiometric electrode and the
reference electrode can be measured. Ion Sensitive Field Effect Transistor (ISFET) is
one of the examples of a potentiometric biosensor (Lee et al. 2009).
Conductometric electrochemical biosensors mainly measure the change in elec-
trical conductivity due to metabolic changes occurring in food samples. One com-
mon example of these types of biosensors is urea biosensor which is used to check
the urea present in milk and other drinks using immobilized urease enzymes. These
biosensors are also used in dialysis and urinal surgeries (Ghourchian et al. 2004).
Thermal biosensors detect the change in temperature due to production of heat
during any biochemical reaction between substrate and enzyme. It can be used in
cholesterol measurement. It can be used in enzyme linked immunosorbent assay
(ELISA) which is a method used to detect antibodies and antigens in the body
(Mehrotra 2016; Narwal et al. 2019).
The basic principle is to sense the ions developed during the biochemical reaction
in the biological samples which bind with the analyte and develop an electrode-
based signal. One such testing sample exploring two electrode-based EIS setup was
reported by Bacher et al. in 2012 for the detection of aflatoxin M1, which is present
in milk samples. Impedance which is the effective resistance of an electric circuit or
alternating current, arising from the combined effects of resistance and reactance,
which changed due to electrode caused by antigen–antibody interaction reactions,
was measured at the electrode surface where the first electrode was configured as
working electrode and a second electrode was used as a reference electrode. So, it is
very important to select the suitable working electrode for successful electrochem-
ical quantification in the end.
Recently, several researchers and models are working with electrode materials
that have been developed where forms of carbon and others have been used, rather
than the classic metals such as, mercury, gold, copper, etc. Figure 9.1 shows the
basic principle used in electrochemical biosensors.
The main working principle of a biosensor is described in the picture above,
wherein the transducer is the key component which utilizes the physical change, i.e.,
heat output, which can be detected by thermal biosensors. Change in the electrical
268 S. Sehgal et al.

Fig. 9.1 Food safety analysis using electrochemical biosensors

output would fall under electrochemical biosensors, such as redox reactions which
occur during the metabolic reactions and can be measured by amperometric bio-
sensors, light output is measured by optical biosensors whereas for mass and
frequencies between reactants and products, piezo-electric biosensors are used
(Mehrotra 2016). Some of the applications of electrochemical biosensors in food
are discussed below.

Detection of Pathogenic Microorganisms

Foreign bodies, such as antibodies, antigens, or contaminants, can contaminate

biological systems. Other forms such as microorganisms, their toxins and
byproducts as well as non-toxic components like proteins, nucleic acids, and charged
molecules may also contaminate food commodities whose detection is essential for
food safety. Various biochemical sensors have been used for the detection of
pathogenic microorganisms attacking the food and resulting in food contamination,
eventually leading to food poisoning and human health issues. Many foodborne
pathogens like Escherichia coli, Salmonella typhimurium, Staphylococcus aureus,
Bacillus cereus, Streptococci, etc. are known food contaminants which have lethal
effects on the body if consumed with food resulting in different types of foodborne
illnesses. Conventional methods are used to detect these pathogens in the samples,
but these methods are time-consuming while molecular methods such as polymerase
chain reaction (PCR) and enzyme linked immunosorbent assay (ELISA) have been
developed to speed up the detection. However, these new techniques will have many
limitations such as complexity of process, insufficient blocking of immobilized
antigen resulting in false negative results and antibody instability, specific labeling,
and difficulty in distinguishing spores. Thus, electrochemical biosensors have been
developed which can assess viability of the pathogen in samples within 10 min
(Vidic et al. 2017; Cesewski and Johnson 2020; Nesakumar et al. 2021).
These impedance-based electro-biochemical sensors help to detect the signal in
these advanced technologies without using any labeling, instead label-free detection
is being carried out with high sensitivity in food (Daniels and Pourmand 2007;
Malvano et al. 2020).
9 Smart Monitoring and Surveillance of Food Contamination 269

Detection of Toxins

Toxins are lethal contaminants produced mainly by various microorganisms, mainly

fungi which contaminate the food product and are very injurious to health.
More than 400 different varieties of chemical toxicants have been identified and
documented with their chemical and toxicological properties (Marin et al. 2013).
The toxins secreted by fungi are known as mycotoxins, which may lead to various
effects including genetic disorders, carcinogenicity, allergic effects and can be lethal
if consumed. Many neurotoxic effects of these toxins have been reported on the
body. Various toxins such as ochratoxins, patulin, and fumonisins sometimes enter
into the food chain at various stages of food production and can cause serious ill
effects. Thus, strict regulations have been made by many nations for importing or
exporting the food products. Accordingly, plants imported or exported must be
evaluated on various criteria for ensuring food safety as per approved norms.
Many approaches have been used to detect ochratoxins including combination of
gold nanoparticles (AuNPs) with polymers and aptasensors (Yang et al. 2011).
The detection of magnetic and non-magnetic nanoparticle toxins in this case,
zearalenone (ZEN) is detected and their immunoassay was based on a competitive
direct immunoassay and anti-ZEN primary antibodies were immobilized on mag-
netic microspheres 3-aminopropyl-modified gold electrode.
The emerging field of biosensor has been proven their robustness, easy handling,
time saving, and cost-effectiveness in detection for various toxins such as marine
biotoxins and harmful algal blooms (HABs) (Estrela et al. 2016; McPartlin et al.
2017).

Detection of Bisphenol A and Other Toxic Chemicals

Shelf life of any food product can be increased by the addition of various antioxi-
dants such as Propyl Gallate (PG) which prevents the oxidation of macromolecules
such as fats, edible fats, oils, etc. Yue et al. (2018) developed a biosensor for PG
detection in food. Bisphenol A (BPA) is used in manufacturing of various plastic-
ware, epoxy resins, etc. used for storing and packaging of food products. BPA can
affect health by disrupting endocrine system functioning leading to failure of
thyroid, central nervous system, and immune system. BPA can leach into the food
products stored in the plasticware; thus, its detection is very critical before supplying
the food to consumers (Alkasir et al. 2016; Zhang et al. 2021). There are many
traditional ways to detect BPA such as Liquid Chromatography-Mass Spectrometry
(LCMS), Gas Chromatography-Mass Spectrometry (GCMS), electrophoresis, etc.
but these methods are time-consuming and costly. So, many biosensors have been
developed for on-site rapid detection of BPA in food products (Ragavan et al. 2013;
Wang et al. 2013).
Dioxins, dioxin-like polychlorinated biphenyls (DL-PCBs), polychlorinated
biphenyls (PCBs), and polycyclic aromatic hydrocarbons (PAHs) are also known
as xenobiotic compounds in food. The electro biosensors are used for the detection
270 S. Sehgal et al.

of these xenobiotic compounds (Laschi et al. 2003; Chobtang et al. 2011; da et al.
2013).

Detection of Heavy Metals

Heavy metals are one of the contaminants found in water and food. These heavy
metals get accumulated in the body if consumed and cause various ill effects and
diseases like Minamata disease, which is caused by the absorption of methyl
mercury usually found in seafood. Thus, its detection is very important to safeguard
the consumers. To detect the heavy metals in wastewater and other food like seafood,
and to ensure the safety of drinking water and cleaning of wastewater before
releasing it into the main source of water, these contaminants need to be controlled
and removed so as to ensure the quality and safety of water and food. Various
biosensors have been fabricated to monitor the presence of heavy metals using
enzymes, metal binding proteins, and antibodies as biorecognition element coupled
with different type of transducers such as amperometric, potentiometric, conducto-
metric, and optic (Bontidean et al. 2000; Blake et al. 2001; Chouteau et al. 2005).
These biosensors are designed in such a way that they can detect the heavy metals in
real-time even in very low concentrations. Zaib et al. (2015) have developed a novel
biosensor based on carbon paste electrode modified with Porphyridium cruentum
biomass and used for mercury detection in contaminated water.
The ability of the biosensor in the presence of different interfering metal (Na+, K+,
Ca , and Mg2+) ions was examined. The usability of P. cruentum-modified biosen-
2+

sor was tried by using Fe3+, Mn2+, Cd2+, Cu2+, Ni2+, Hg2+, and Pb2+ metals as
interferents (Li et al. 2013).

Detection of Pesticides

Many pesticides, chemical fertilizers, herbicides, etc. have been widely used these
days in fields to grow fruits, vegetables, grains, cereals, and other food commodities.
These chemicals are very hazardous for health, causing various diseases, if the food
contaminated by them is consumed. Thus, due to their excess usage, it is important to
detect them and prevent food from becoming hazardous. Variety of biosensors such
as optical, electrochemical, and piezoelectric have been designed in recent years to
detect these residues such as lindane (Gao and Lu 2015; Verma and Bhardwaj 2015).
Rodriguez et al. (2015) used Streptomyces strain M7 (SM7) to detect the presence
of OrganoChlorine Pesticides (OCPs) using a label-free impedimetric biosensor
without the need of pre-treatment of the sample. Fenvalerate is a pyrethroid insec-
ticide used to control several insect pests including Diptera larvae, Hemiptera, and
Lepidoptera. The biological component binds with the analyte having chemicals
related to pesticides and is detected by converting physical changes into electrical
changes to detect the change. Wang et al. (2013) developed an impedimetric
immunosensor for fenvalerate detection using chitosan as an electrode cross-linked
9 Smart Monitoring and Surveillance of Food Contamination 271

with anti-fenvalerate antibodies. Colorimetric biosensors are also used in the detec-
tion of pesticides and enzymatic reactions (Shah et al. 2021).

9.2.1.2 Colorimetric Biosensors

Microbes frequently spoil the food which may lead to food infection or food
poisoning. So, the rapid detection of specific microorganisms is required. Various
biosensors have been developed to detect these microbial contaminants based on
visual detection signals. These types of biosensors are known as colorimetric bio-
sensors, which produce colored signals due to the presence of gold nanoparticles,
which can be easily observed with the naked eye. Colorimetric biosensors based
lateral flow assay can detect pathogens like Salmonella, E. coli which are fecal
contaminants and can cause public health issues. Biosensors are less time-
consuming therefore this method will detect the pathogens in just 10 min with the
antibodies present in the solution. However, this detection can be made more
efficient by adding magnetic beads and higher number of samples can be analyzed
using micro- and nanoparticles; usually gold is used as the nanoparticle as it can be
detected by the color change in the solution at a very lower limit of detection (Shah
et al. 2021).
Another type of biosensors known as calorimetric biosensors are also used but
they mainly detect the change in the temperature during the reaction between
the target analyte and the biorecognition element such as enzymes. In most of the
enzyme-based reactions, heat is either absorbed or released which changes the
temperature at the biosensor surface and this change in temperature is then correlated
with the concentration of analyte. Calorimetric biosensors are also used in the
detection of pesticides and enzymatic reactions (Thakur and Ragavan 2012).

9.2.1.3 Optical Biosensors

Optical biosensors are the most sensitive biosensors as they work on the principle of
optical measurements, e.g., fluorescence, luminescence, chemiluminescence, absor-
bance. These biosensors are mostly chosen due to their very high performance in
terms of their low detection limits and detection speed for the detection of pathogens,
toxins, and pollutants present in the food (Narsaiah et al. 2012).

9.2.1.4 Other Sensors

In addition to the biosensors mentioned above, many other types of biosensors are
employed in the food industry due to their quick response , sensitivity and reliability.
One such example is that of proximity sensors or ultrasonic sensors which help in the
monitoring of food safety as well as the nutritional quality of the food. (Coupland
and Saggin 2003; Patel and Doddamani 2019).
272 S. Sehgal et al.

Another biosensor such as a humidity sensor measures the humidity at the

particular environmental temperature and moisture, then converts it into the electri-
cal signal. Relative humidity is also calculated in the same way taking the reference
of highest humidity at the same temperature which can be used to check the
freshness of packed fruits and vegetables. Chemosensors are used in smart packag-
ing to measure the real-time microbial breakdown of the packaged food. The
changes during the breakdown of macromolecules substantially present in food
will be detected by the change in reaction during this process of degradation by
microorganisms inside the packaged food. Similarly, other sensors such as gas
sensors, pH sensors, freshness sensors, etc. are used to detect the conditions of the
environment and food in order to prevent spoilage of food (Adley 2014; Patel and
Doddamani 2019).

9.2.2 IoT-Based Smart Technologies

Food waste has been increasing at an unprecedented rate in recent years, posing a
threat to economic growth factors. This, in turn, has a significant impact on the
agricultural processing industries. Food wastage is increasing these days due to a
lack of proper storage facilities and food processing knowledge. According to the
Food Waste Index Report 2021 published by the United Nations Environment
Program (2021), 50 kg of food is thrown away per person every year in Indian
homes. Food hygiene is also a major concern to prevent food wastage. To prevent
food spoilage and wastage, we need to control the decaying factors which influence
the food commodities, e.g., moisture, temperature, humidity, etc. and in order to
control them we need certain monitoring devices, which can control and detect the
limit, when the action needs to be taken to prevent the decaying of food material.
The food wastage measurement system in various areas is required which can
provide real-time input on food wastage to the stakeholders or consumers on a live
computer-based dashboard and can generate comparison reports to provide detailed
insight to higher management. This can be accomplished in two ways: manually
(or) automatically by utilizing the Internet of Things (IoT) as an underlying archi-
tecture (Manjunath and Shah 2019).
In the current scenario the world is moving towards innovation and technology,
mainly the internet of things (IoT) is becoming one of the most helpful technologies
for connecting anything, anywhere, and anytime. An IoT connected device can
detect its surroundings, collect appropriate readings, and communicate the informa-
tion via the internet to a server, which can store the information for later use, or to
another device, such as a smartphone, where the information can be viewed. This
enables for continuous monitoring of a system that is being evaluated. The user can
make decisions about what activities to take in such a monitored environment (Gupta
and Rakesh 2018).
Food supply chain plays a very important role in the food industry for connecting
material movement from farm to fork, and IoT has been helpful in tracking live with
9 Smart Monitoring and Surveillance of Food Contamination 273

the stake holders so as to assure the quality and safety of the food. Moreover,
growers may drastically reduce pesticide use by precisely recognizing crop pests
using IoT-based intelligent equipment such as wireless sensors, robots, and drones.
Modern IoT-based pest management delivers real-time monitoring, modeling, and
disease forecasting, making it more successful than traditional calendar or
prescription-based pest treatment approaches. Advanced disease and pest detection
methods rely on image processing, with raw images collected across the crop area
utilizing field sensors, unmanned aerial vehicles (UAVs), or remote sensing satel-
lites. Remote sensing imagery typically covers huge areas and hence provides more
efficiency at a reduced cost. Field sensors, on the other hand, can support more
functions in data collection, such as environmental samples, plant health, and insect
situations, in every corner of the crop cycle. IoT-based automated traps, for example,
may collect, count, and even describe bug kinds, then upload the data to the Cloud
for detailed analysis, which is impossible with remote sensing (Lin et al. 2018; Ayaz
et al. 2019; Suma 2021).
Considering the growing demand of controlling environmental conditions of food
processing and packaging, agricultural researchers have revolutionized it by incor-
porating Multimedia Internet of Things M-IoT K (clustering) which involves com-
bining metabolic changes in the agricultural produce, then converting them into
algorithms which eventually detects diseases, quality deterioration or infestation and
accordingly, these can be prevented. IoT is used in the food processing industry to
determine whether the environment used to process food is appropriate and the
quality of the food which is about to get packed should meet the requirements and
meet the quality and safety norms as shown in Table 9.1.
Thus, the IoT technologies have enormous potential for application in the field of
food and agriculture, especially given the sector’s societal and environmental chal-
lenges. From farm to fork, IoT technologies have the potential to transform the sector
by improving food safety and reducing agricultural inputs and food waste. The
implementation of IoT-based large-scale pilots (LSPs) across the entire supply
chain will be a significant step towards greater adoption of these technologies
(Brewster et al. 2017).
In 2019, Balaji et al. described an IoT system named Raspberry Pi to detect food
adulteration using various monitoring systems and sensors. For example, a sensor is
used to add oil in the baking unit as per the radiation change in the product, at a
particular time and temperature, then the oil will be automatically added and the
information will be shared as analogue signals between the sensors which will
further transform into electrical or digital signals, these signals can be amplified
with the help of a microcontroller. The Zigbee was acting as a transmitter to transfer
the signals in lower power consumption value, accordingly Raspberry Pi will display
the results. After completing the whole process, a notification will be sent to the user
along with the results using IoT. This will give the information to the end consumer
or user about the food product. It is very helpful in checking the freshness of animal
food and meat products, because the time calculated after slaughtering the animal
from where the rigor mortis starts can be used to evaluate the freshness of the animal
food. Apart from just the freshness, other steps during handling and processing of
274 S. Sehgal et al.

Table 9.1 IoT-based sensors used in food industry

Types of sensors Applications
Electronic sensing device (e-nose) Inspection of quality, spoilage, and post-
It is an electronic device replicating the human harvest changes in the flavor of the food prod-
sense (nose) uct
Cyranose 320 is a commonly used e-nose in the Used in medical diagnosis, environmental
food industry and related fields monitoring, etc. also
Oil sensors detect the electrical capacitance Determination of the quality of the edible oils
generated due to total polar compounds in fats Detection of oil spoilage at various sweet
and oils shops, restaurants, and food industries
FOM310 and TESTO 270 oil sensors are used
in edible oil industry
Humidity detectors use electrical resistance to Monitoring of moisture-sensitive food prod-
detect changes ucts
DHTII and DHT22 are reliable and precise Prevention of alteration in qualitative attributes
impact sensors towards humidity of dehydrated foods, frozen foods, fruits, and
vegetable stores
Photoelectric sensor emits light from a trans- Detection of color adulteration
mitter and then detects the reflected light from Post-harvest and processing changes in the
the object food
TCS3210 and TCS3200 are used to detect arti-
ficial color or change in color in food
Metal detectors work on the principle of con- Detection of metals as physical hazards in the
version of electromagnetic radiations into elec- food
trical output as signals Also used in fruit juices and mineral water for
APEX 300 and Sentinel 1000 are popular in the metal content detection
food industry
pH (acidity/alkalinity) sensors detect hydro- Used in various sectors of the food industry
gen ions concentration and convert to electrical such as meat and dairy to check quality and
output chemical changes
SEN0161 and pHT810 are used for factory
calibration certifications
Consistency/viscosity sensors are used to Used in sauces, concentrates, gels, beverages,
check the fluidity of food using electromagnetic fluids, etc. to check food consistency which is a
concepts major impacting attribute of the end product
Viscosity sensor 440 and 443 are used to mea-
sure temperature-compensated viscosity
Thermal sensors/temperature-based sensors It can be used at various food stores, slaugh-
detect thermal changes with an electrical output terhouses, and restaurants to prevent food
LM35, PT 1000, and NTC sensors are com- spoilage
monly used
Salinity meters are sensitive to the salinity ions Used to detect salt concentration in foods such
and convert the electrical resistance to electrical as meat, salads, etc.
output
SSX 210 is used for detecting the salt ions in the
food

meat and meat products can be controlled using IoT and the information regarding
each step is shared with the end user to trace the hygiene of the process as well as the
final end product (Balaji et al. 2019).
9 Smart Monitoring and Surveillance of Food Contamination 275

QR code generation and attaching Radio Frequency Identification (RFID) tags

helps again in tracking and tracing the real-time data about the food product at
different levels of packaging. These days, consumers are very cautious about the
safety and hygiene of food products and also aware about threats involved at every
step and methods of controlling them with surveillance. This will eventually portray
a smart gateway towards the smart technology which connects and controls physical
parameters and shares the real-time detailing locally or nationally, etc. Therefore, the
real-time data sharing would gather the trust and importance in customers’ minds
regarding the safety and quality of food.
Some portable pocket-sized IoT devices have been developed such as
immunosensors with Wi-Fi modules to detect the food contamination present even
at very minute level (Seo et al. 2016). Like biosensors, immune sensors are a type of
electrochemical biosensor that can detect the binding analyte to the antibody at a
particular site. The concentration of the analyte will be directly proportional to the
change in the reaction due to contaminants and the presence of biomolecules in the
food. Now, when IoT was combined with these sensors, as were used in Seo et al.
(2016) for food testing, the target bacterium Vibrio parahaemolyticus was
pre-cultivated (e.g., plastic culture bag and stomacher). The culture was monitored
and immunosensor was used as a control device and results were shared online on
the internet. Such a technique of combination of biosensing and IoT can be used as a
prior indication to prevent unintended repercussions especially in terms of food
safety and quality of food. This can be used for surveillance of infectious diseases to
control major outbreaks (Bouzembrak et al. 2019).

9.2.3 Portable Detection Devices

Sensors in portable detection equipment identify whether the food has gone bad or
not, as well as for how long the degradation has been going on. It monitors volatile
organic compounds (VOCs), chemical levels, temperature, and humidity levels,
among other things. Also, before sending the data to the user’s smartphone or tablet,
these devices examined and analyzed the data collected by the sensors. It aids in
on-site application as well as environmental and food composition surveillance and
monitoring (Jin et al. 2019; Müller-Maatsch and van Ruth 2021).

9.2.4 Portable Gas Detector or Gas Detection

These portable biosensors mainly detect the hazardous gases formed either during
production of food or spoilage. For example, in the beverage industry, carbon
dioxide is one of the key ingredients utilized to increase the shelf life and achieve
the fizz quality in the finished product. Depending on the situation and application at
various industrial locations, these gas detectors or sensors can be portable or
276 S. Sehgal et al.

stationary. They display the level of explosion, toxicity, and oxygen or carbon
dioxide gases levels (Matindoust et al. 2016). These small gas detectors are useful
for real-time monitoring and regular surveillance of the risks arisen due to gas leaks,
and ensure the safety of workers in the industries.

9.2.5 Portable Chemosensory and Biosensor Devices

Many types of portable biosensors are used in the food industry to analyze and
characterize the macromolecules (or chemicals) present in food, collectively known
as chemosensory devices. These sensors are used for real-time monitoring and share
the on-site data including environment and food composition screening data. Now,
they are broadly used to detect the changes happening in the food matrix due to any
contamination and analyze the environmental condition provided during the
processing. Such devices could be used, for example, to overcome the limitations
existing in the measures currently used in the fields of the environment and the food
industry. While these measurements are primarily focused on independent analyses
of various parameters such as the complexity of food and environmental matrices but
they require a new holistic approach (Dragone et al. 2017; Mustafa and Andreescu
2018).

9.2.6 Optical Biosensors Devices

Optical biosensors are one of the most sensitive biosensors which mainly detect
optical signals generated during the reaction by various optical phenomena such as
fluorescence, luminescence, chemiluminescence, absorbance, etc. These devices are
preferred due to their high performance, low detection limits, and high sensitivity to
detect pathogens, toxins, and pollutants present in the food (Narsaiah et al. 2012).
Nowadays, more advanced biosensors based on the surface plasmon resonance
(SPR) phenomenon have been developed to detect the biomolecular interactions in
real time (Fig. 9.2).
SPR based sensors can measure the signal quantitatively as well as qualitatively
and do not require any tagging of the molecule with light emitting groups as required
in optical biosensors. This is also important with respect to food safety as these tests
have a specificity of a highly narrow spectrum and the output can be obtained in real
short time. In the food industry for example in milk through SPR, the problem
related to the turbidity and fouling can be controlled by measuring the refractive
index, wherein the biomolecule responsible to develop the changes in the turbidity or
spoilage can be detected and prevented in future (Ravindran et al. 2021).
9 Smart Monitoring and Surveillance of Food Contamination 277

Fig. 9.2 Surface plasmon resonance (SPR) biosensor

9.2.7 Microfluidic Analytical Devices

It is a fast and economical analytical screening tool to test contamination in food

products. As compared with other monitoring methods these are very systematic,
eco-friendly, low cost, and less time-consuming which detects the molecules quan-
titatively as well as qualitatively without affecting the external environment. The
fabrication of a microfluidic chip is primarily composed of a microelectron-
mechanical system and its processing technique, as a result of which this chip
provides control at a very small scale, i.e., at the micro level (refer Fig. 9.3). It is
widely used in biochemical analysis due to its fabrication capability and other
economic advantages. Polydimethylsiloxane and cycloolefin are commonly used
macromolecular polymer materials for its fabrication. A multi-layered technique is
used for creating a microfluidic chip which has potential to detect chemical contam-
inants and toxins such as pesticides and insecticides, among other things.
In 2012, Jokerst et al. have developed a paper-based microfluidic chip for the
detection of E. coli O157:H7, L. monocytogenes, and Salmonella in seafood and
ready-to-eat meat products. The change in the color intensity is detected whenever
the food is contaminated due to secretion of enzymes by them, which react with
substrates present in the microchip.
A disposable paper sensor for the analysis of copper present in the natural water
and wastewater used for drinking and other applications was developed by
Jayawardane et al. (2013). This device can also be used to detect other metals in
alkaline or weak acid conditions.
278 S. Sehgal et al.

Fig. 9.3 Microfluidic systems

Many food additives such as food colorants are added to the foods, to enhance
their appearance and other properties. However, many of these additives and color-
ants may be carcinogens and cause teratogenic effects in our body if added more than
the prescribed amounts. So, their qualitative detection is very crucial for surveillance
in food safety. Thus, Gao et al. (2020) developed a polyelectrolyte-coated paper
which detects the presence as well as the amount of the colorants present in the food
product. This test was done in orange and grapefruit juices as sunset yellow and
tartrazine were being tested using this paper-based chip technology.
Other additives such as nitrites and nitrates used as the coloring, texturizing as
well as preserving agents in meat and meat products produce some cyclic pigments
known as nitrosamines. These nitrosamines if consumed above the adequate amount
can harm human health leading to the development of cancer. Jayawardane et al.
(2014) had fabricated a disposable microfluidic paper-based sensor used to detect the
number of nitrites or nitrates. This device saves time, is eco-friendly and convenient
to use.

9.3 Block Chain Technology

Around 2019, many food companies started using a new technology known as block
chain technology. According to a report this technology has been accepted by more
than 20% of the food manufacturing companies globally. Block chain technology
9 Smart Monitoring and Surveillance of Food Contamination 279

has not only evolved in technical and electrical industries but also in the food
industry (Mireille et al. 2020; Hang et al. 2020).
Block chain technology provides a system of monitoring and surveillance in the
food supply chain. It starts from the farm and can be maintained and monitored till
consumers. It is basically a digital connection between the farmer to the supplier and
further to the end consumer using computers or internet of things. As the fresh
produce passes from one person to another or one step to another, the respective data
is captured cryptographically, secured and unstoppable blocks are created. Internet
of things (IoT) also clusters with these blocks and further can evolve and revolu-
tionize the food supply chain industry to another level. IoT solutions are very helpful
in these aspects because they bring digital and physical systems together. Therefore,
block chain technology helps in providing a common platform to share the data of
each step of all the processes between all the stakeholders dealing in the food
processing and supply chain (Rejeb et al. 2020).
This technology helps to track the food product details at every step, which in
return ensures the safety and quality of the final product, and eventually gain into the
trust value of the end consumers and retailers in the food supply chain. However,
product traceability still needs to be handled with care in order to build trust and
quality of the product. There have been various food outbreaks and scandals around
the world like melamine in China, European horse meat scandal, BSE crisis, etc.,
therefore, the worldwide food regulations need data to ensure a safe and healthy food
supply all over the world. This is possible only after keeping an eye at each step and
ensuring the eradication of each health hazard during the same. Additionally, this
technology can provide supply chain traceability and information transparency. It
also enables rapid identification of the history, movement and current location of
consumer products reaching to where and when resolving various food concerns.
Attaining food traceability is a big relief to the regulatory body, retailers, producers,
and consumers (Olsen et al. 2019; Baiod et al. 2021).
Blockchain technology, when combined with IoT sensors, enables stakeholders
to provide recoverable data storage records on agricultural and other products,
providing a large amount of data and information transparency and traceability of
the farm produce.
It allows the data to be stored on a unified platform and the product can be tracked
and recalled if there is any doubt or risk associated with the product. Everything will
be on the same page, and the system will become clearer. Taking into account time
constraints, earlier it used to take months to trace a raw material that was processed
into a product, but now, using this technology, the data can be generated in a matter
of seconds.
If any product does not qualify the safety and quality norms, it can be easily
withdrawn from the markets without causing any imbalance in the system. Unlike
traditional food traceability systems, blockchain technology can ensure and collect
all traceability records covering critical information, and exchange information
between various stakeholders. Eventually, this technology can increase consumer
confidence in the quality, safety, authenticity, and provenance of food products
(Rejeb et al. 2020; Xu et al. 2020).
280 S. Sehgal et al.

The benefits of using this technology enhance food traceability, ensuring food
safety, eliminating counterfeit products, improving inventory management, reducing
food waste, providing food supply chain provenance, proving labels claims, and also
enhancing food industry supplier selection. Companies like Walmart, Nestle, Kraft,
Heinz, etc. are using this technology. Walmart did a pilot study with IBM to check
the complete end to end traceability with mangoes in US and pork in China stores
(Chapman 2020).

9.4 Smart Packaging

Due to the technological advances and availability of smart technologies, the world
is changing very fast. Food industry is also experiencing this change in technology in
a positive way. There are various critical parameters of these smart technologies
required in the food industry. First, these must retain the integrity for prevention of
food from the outer contaminants to avoid spoilage. Second, the packaging material
should improve with enhanced parameters such as appearance, texture, color, and so
on. Third, these must respond towards any changes brought about by the external
environment. Fourth, these should be self-explanatory by the packaging material
labeling about the product and the qualities required by the consumers, as well as
convenient to open and use, and last but not least, confirm product authenticity
(Schaefer and Cheung 2018).
In order to confirm the above parameters following devices are used in the smart
packing and processing. Time-temperature indicators help to indicate the change in
physical or chemical response against the time and temperature change, acid-base
reaction, melting, polymerization, etc. But, biological response is based on the
change in biological activity due to microorganisms or their enzymes with time
and temperature. Time strips are one such smart technology, used to check the
duration till when the package is opened and exposed to the environment. It is
helpful to indicate changes in organoleptic properties and other properties of food
due to temperature and other abuses (Kuswandi et al. 2011).
Another example is pH sensors which sense changes in pH due to temperature
change to determine fish freshness. When the meat and fish products get spoiled,
their pH increases due to the increased number of amines produced and this change
is detected by the sensors. Whenever there is any change in pH, the sensor produces
a visible change in the packaged product which indicates spoilage of fish products.
So, this type of colorimetric sensor has potential to detect the changes in color due to
metabolic reactions inside the product. The sensor’s responses are correlating with
the bacterial growth and based on real-time monitoring.
There are other types of sensors available which can be used to check the fruit by
determining O2 and CO2 levels, but the challenge to present fruit in a good condition
increases with distance from markets to fulfill more sophisticated consumer
demands. The yearly supply of fruits and vegetables is only possible by
9 Smart Monitoring and Surveillance of Food Contamination 281

implementing Modified Atmosphere Packaging/Controlled Atmosphere Packaging

(MAP/CAP) processes (Kuswandi et al. 2011; Fuertes et al. 2016).

9.5 Conclusion

The global food industry requires reliable, fast, and cost-effective technologies for
the surveillance and monitoring of quality of food to prevent wastage of large
amounts of food by spoilage. Majority of known techniques mainly recognize and
detect the changes occurring in the biomolecules present in the food in a real-time
manner which helps in delivering better-quality food to consumers. Also, many
trend setting innovations are developed with blending of IoT smart devices with
biosensors which detects spoilage very fast by detecting the physical changes
occurring in the food and transforming the signals into electrical sensations. Thus,
they play a vital role in food industries to detect various food contaminants such as
additives, pesticides, toxins, pathogens, etc. in a cost-effective manner with high
sensitivity and specificity. There is further need to develop various biosensors
commercially for surveillance of food supply which can also provide the information
to the various stakeholders to enhance the food quality and safety for the end
consumer. Internet of things (IoT), microfluidic paper-based surveillance devices,
block chain technology, biosensors, etc. therefore, have immense scope in the food
processing industry to provide better traceability, safety monitoring, and quality
control.

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Chapter 10
Neural Network Approach for Risk
Assessment Along the Food Supply Chain

Uma Tiwari

Abstract Neural networks are widely used as a mathematical tool for effective
monitoring of robustness in the input data based on the machine learning process.
Neural networks have been widely used for food safety, quality analysis, monitoring
bacterial growth, and applying controls along the chain. Additionally, a combination
of risk assessment processes would develop a smart approach and a model with fast
methodology for evaluating risk at all stages of the food chain, thus providing a
suitable prediction of risk at a given stage and increasing public health awareness for
food processors, food safety managers, and other stakeholders working towards a
sustainable food supply chain. This chapter discusses applying neural network
approaches for risk assessment along the food supply chain.

Keywords Neural network · Risk assessment · Food chain · Food safety

10.1 Introduction

Worldwide increasing consumer demand on food attributes such as quality, safety,

sustainability, and nutritious food is mainly associated with the information along
the supply chain (Nardi et al. 2020). Complexities surrounding climate change and
urbanization have posed challenges to the food supply chain to produce safe food,
which has led to the advancement in climate-neutral processing and analytical
technologies (Leat 2013). To protect consumers’ health, the food business operators
are enforced to align their food safety objectives and collaborate at each stage of the
chain supply (Chammem et al. 2018; Buncic et al. 2019). A food supply is linked by
one or more stages and collaboration along the chain will reduce any harm to the
consumer’s health. From the supply chain management point of view, the food
sector is a challenging domain and advanced control systems or approaches would
be required for stringent food safety and sustainability (Verdouw et al. 2016). As the

U. Tiwari (*)
School of Food Science & Environmental Health, College of Sciences & Health, Technological
University Dublin (TU Dublin—City Campus), Dublin, Ireland
e-mail: uma.tiwari@tudublin.ie

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 287
S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies,
https://doi.org/10.1007/978-981-19-1746-2_10
288 U. Tiwari

FAO/WHO (1995) indicated food safety is the “assurance that food will not cause
harm to the consumer when it is prepared and/or eaten according to its intended
use” and reduce any health effects for humans. Some influencing factors may affect
the food supply chain, i.e., quality, technology, logistics, the regulatory framework,
consumers (Bourlakis and Weightman 2004). Thus, assessing every possible risk
and its occurrence level along the series of links and interdependencies from farm to
fork would be important for safety in a food supply chain.
Any food safety issues along the chain are assessed and analyzed using smart
mathematical approaches such as algorithms; empirical models are applied along the
food chain to maintain sustainability (Zhong et al. 2017). In addition, the occurrence
of food wastage along the supply chain stages has raised concerns due to its impacts
on environmental, social, and economic problems (Papargyropoulou et al. 2014). It
is important that there is a vehicle routing algorithm and modeling at every stage of
the food system, i.e., production, processing, distribution, consumption, and disposal
(Osvald and Stirn 2008). Information technologies, the internet of things and in
combination with the application of dynamic models, sensors, simulations are
utilized to monitor, control, plan, optimize, and capture the real-time using software
systems along the food supply chains that facilitate early-stage tracing (Astill et al.
2019). Leithner and Fikar (2019) investigated the impact of organic fresh food
supply chains using real-time information of product quality and applying
simulation-based decisions to support, to monitor the sustainable production of
food from farm to the retail level. Badia-Melis et al. (2016) demonstrated that
monitoring surface and internal temperature over a pallet of apples will provide a
quality control across the supply chain. Moreover, application of neural network
modelling will also facilitate to capture any variation in temperature across the entire
pallet of fruits which is important for preserving perishable goods (Badia-Melis et al.
2016).
Moreover, an effective supply chain requires identifying, assessing, and evaluat-
ing the risk and probability of occurrence along the chain. Integrating a neural
network in detecting risk will create predictions and early warning diagnostic out-
comes for various stakeholders working along the chain (Geng et al. 2021).
Therefore, this chapter aims to discuss neural network approaches for risk assess-
ment along the food supply chain.

10.2 Neural Network

Neural network model approaches are linked with neurons in the brain and how the
neurons are connected in the form of electrical impulses that transmit information
between the brain and nervous system. Simply to state that brain structure and its
functional properties of interconnected neurons. A neuron consists of a cell body,
dendrites (extend cell body and receive a message from other neurons), and an axon
(extends to smaller branches) attached to synapse structure and neuron to neuron
communication occurs (i.e., connect a neuron to another neuron) (Gurney 1997).
10 Neural Network Approach for Risk Assessment Along the Food Supply Chain 289

Fig. 10.1 Components of brain neuron system and how they transmit the information from neuron
to neuron

Therefore, the signals are transmitted from one neuron to other neurons through a
branching fiber known as the axon. The strengths of synaptic connections often
change in response to external stimuli depending on how the learning takes place in
living organisms (Gurney 1997). Generally, learning refers to training a process
within a neural system so the ability to carry out certain tasks can be achieved by
altering its internal parameters. This means that axons transfer information through
the presynaptic neuron to the dendrites of the postsynaptic neuron (Gurney 1997).
Presynaptic neuron refers to the cells that send out information, while the cells that
receive the information are known as postsynaptic neurons. Figure 10.1 shows the
components of a neuron and indicates how the information is transferred from one
neuron and another neuron.
The neural network can be classified based on its structure and algorithm, such as
artificial neural network (ANN), convolution neural network (CNN), and recurrent
neural network (RNN). These neural network classifications are widely used in food,
beverages, and the prediction of biogas/bioethanol production. ANN models are
shown to predict biogas production rate from food, fruits, and vegetable wastes
(Neto et al. 2021) and also used for optimization of bioethanol production from
pumpkin peel wastes (Chouaibi et al. 2020). For example, Morelle et al. (2021)
developed artificial neural networks to detect and predict foam evolution during
bottling noncarbonated beverages. It is known that foam evolution in noncarbonated
beverages would cause a high contamination risk to product safety and process
efficiency. In order to solve the risk of contamination, Morelle et al. (2021) applied a
CNN tool for monitoring the visible regions in the foam evolution time and further
adapted a RNN tool to study the properties of the foaming behavior (e.g., foaming
height) during the filling of noncarbonated beverages.
290 U. Tiwari

10.3 Artificial Neural Network (ANN)

The ANNs are based on biological neural networks’ structure and function and work
similarly to the human brain processes information. Indeed, an artificial neural
network is a biologically adapted computational model formed from hundreds of
single units, namely artificial neurons (Agatonovic-Kustrin and Beresford 2000). To
start any neural network, a sample/variable of interest is loaded on the input layers
and forms the input nodes for the ANNs. The output will depend on the activation
function and the weight values are managed by a specified learning rule (i.e.,
mathematical algorithm) which improves the network’s performance. Neurons are
arranged in various layers such as single or multi-layer neural networks and as the
theorem states that the single-layered neural network is a set of inputs directly
proportional to an output in a linear function, known as the perceptron. Conversely,
when neurons are arranged in multiple layer-wise linkages, a group of hidden layers
is separated by input and output layers (Priddy and Keller 2005; Argatov 2019).
These layer-wise linkages are commonly known as feed-forward neural networks
and the information travels typically in one direction. According to Ripley (1994),
feed-forward neural networks consist of layers of non-processing units from subse-
quent layers connected by sets of synaptic weights (Fig. 10.2). This neural architec-
ture follows a trend that the output of each layer feeds the next layer of units, thus
forming one or more layers of hidden units between the input and the output layer.
Such a neural network format can influence output patterns by altering or
transforming a set of input patterns. Additionally, an existing set of input and output
utilized for running training sets is often required to develop feed-forward neural
networks.
Figure 10.2 shows an example of a feed-forward single-layer (a) and multi-layer
(b) neural network. A multilayered feed-forward structure may include one or more
hidden layers. The ANN uses a training algorithm to learn the datasets that modify
the neuron weights depending on the error rate between target and actual output
(Huang et al. 2007).

Fig. 10.2 Feed-forward (a) single-layer and (b) multi-layer neural network
10 Neural Network Approach for Risk Assessment Along the Food Supply Chain 291

10.3.1 ANN Processing Units

An artificial neural network consists of interconnected processing units including the

summing part that receives various input values (discrete or continuous data) with
weights (positive or negative). Bias node’s function “f” is parameterized by the set of
weights to understand the learning patterns, i.e., node value of 1 indicates every link
in the network is connected or node value of 0 indicates links are absent. Inputs for
creating a network can come either from the outputs of other processing units or
from external sources. In contrast, the output of each unit depends on the strength
and activation value of the weight associated with the link. This activation value
determines the actual output captured either deterministically or stochastically
(Yegnanarayana 2005).

10.3.2 Application of ANN in Food

There is a wide range of ANN applications in food processing including quantifica-

tion and quality estimation of meat products, process control of post-harvest stages
of fruits or product formulation, etc. Table 10.1 shows the various applications of
ANN in various food products. ANN has been demonstrated to classify diverse food
products and implement good quality inspection and grading along the chain. For
example, Ibarra et al. (2000) applied a combined IR imaging neural network to
determine the quality of chicken breast filets and recorded the external temperature
during the cooling process after cooking. They applied ~120 time series, i.e.,
60 series for in training set and test set, respectively, and showed that the combina-
tion method captured the internal temperature within an error of 1  C after 3 min of
cooking for evaluating the doneness of chicken meat.

10.4 Convolution Neural Network (CNN)

Convolutional neural networks follow the principle similar to the biologically neural
networks used in computer vision for image classification and object detection, etc.
(Huang and Le 2021). CNN is a type of feed-forward neural network used for visual
recognition by encoding certain layers as sequential training sets from the input to
the output layers (Nebauer 1998). The CNN includes different segments: convolu-
tion layer, nonlinearities layer, pooling layer, and fully connected layer. The convo-
lution layer systematically filters input images to detect the features in the image.
Nonlinearities are essential for neural network design that may have a large impact
on the training speed of a neural network and depends on data type (D’Souza et al.
2020). Nonlinearity is used for classification tasks to avoid uncertainty in the input
data. Pooling layers will help to perform features extraction, while the fully
292 U. Tiwari

Table 10.1 ANN application in various food products safety

Product Objectives Applications of ANN References
Beef Prediction of Escherichia coli Quantification of bacteria Gosukonda
O157:H7 inactivation on beef inactivation and enhancing the et al. (2015)
surfaces meat quality using ANN
model, increase the market-
ability of the meat product
Apples Application of thermal imag- Monitoring surface tempera- Badia-Melis
ing predicts surface tempera- ture across the entire pallet of et al. (2016)
ture over a pallet of apples apple packaging using ANN
either packed using plastic with a combination of thermal
boxes or cardboard boxes imaging technology
Ham Texture analysis and grading ANN models used for predic- Zhu and Wu
sausages of ham sausages tion of the hedonic score of (2019)
texture and to correlate sen-
sory test of the sausages
Plum Optimization of the functional ANN models used for opti- Bajić et al.
pomace plum spread mizing quality parameters (2020)
(texture, color, phenol
antioxidant)
Quail (meat Prediction and optimization of ANN model optimizes the Jahan et al.
type) quail’s slaughter weight process of carcass yield and (2020)
the bird’s weight at slaughter,
providing a powerful approach
to determine phenotypic
records (pedigree information)
Natural Texture properties of yogurt Texture profile, rheological Batista et al.
non-fat properties of yogurt formula- (2021)
yogurt tion, and process conditions
using ANN models
Cream Prediction of pH along the ANN model predicts the vari- Ebrahimpour
cheese cheese fermentation ation in pH of cheese fermen- et al. (2021)
tation process at lab and
industrial level
Flour (water- Drying process ANN captures the convective Fabani et al.
melon rind drying process of watermelon (2021)
pomace) rind pomace and optimizes the
parameters
Egg Evaluating egg quality ANN provides an accurate Malfatti et al.
indicator prediction of the yolk index to (2021)
evaluate egg
Dry-cured Evaluation of quality; protein ANN predicts mid-quality of Zhu et al.
ham degradation dry-cured ham using protein (2021)
degradation index as input
variables; predict the product’s
shelf-life
10 Neural Network Approach for Risk Assessment Along the Food Supply Chain 293

connected layer maps the extracted features into the final output. CNN architecture
and the training process are applied in various food applications.

10.4.1 Application of CNN in Food

CNN algorithms can be applied in various food chains to assess food safety and
adulteration. Several researchers have demonstrated the application of CNN in food.
Kozłowski et al. (2019) classified barley varieties based on color, texture, and
morphological attributes based on a convolution neural network with more than
93% accuracy. Therefore, the application of CNN can be used for quality assessment
in the malting industry either in small barley samples or large quantities of barley.
Similarly, Fan et al. (2020) demonstrated an online detection of defective apples
using computer vision and CNN-based classification method using accuracy
(96.5%), recall (100%), and specificity (92.9%) for the training testing set. They
used about 79,200 images (normal apple) and 39,600 images (defective apple) as
training sets and evaluated the CNN to detect defective apples along the online fruit
sorting machine (Fig. 10.3). The application of the CNN-based classification model
was shown to be cost-effective and have greater potential for identifying defective
fruits in commercial fruit packaging lines.
Moreover, implementing a convolution neural network also facilitates product
traceability along the chain and thus also aids in automating grading operations. For
example, Vo et al. (2020) used CNN in combination with image processing tech-
niques that automatically grade lobster around the supply chain. Lobsters are graded
based on size, weight, and color and using mask-refined CNN and thus improves
supply chain tracking from sea to table. Likewise, Zheng et al. (2021) demonstrated
a novel detection method for identifying adulteration of minced mutton with pork
using thermal imaging integrated with CNN. They used 245 thermal videos of
mutton samples, of which pure samples of 35 mutton and 35 pork samples and the
remaining 175 samples were adulterated mutton with a varying ratio of 10–50% of
pork samples.

Fig. 10.3 Online fruit sorting machine that sorts normal apples from rottenness and scar apples.
(Adapted and modified from Fan et al. 2020)
294 U. Tiwari

Fig. 10.4 Convolutional neural network outline

Additionally, they used thermal images as regions of interest for developing CNN
models. They observed a good prediction between the qualitative classification of
different samples and the quantitative prediction of adulterated proportion. Integrat-
ing the CNN model and thermal images are shown as potential applications in
detecting and monitoring adulterated food that too in economic and convenience
methods. Similarly, Dixit et al. (2021) demonstrated prediction of pH and fat
attributes in red meat using a non-invasive method (i.e., hyperspectral images in a
combination of deep CNN) as a quality assessment in a meat processing plant and
thus enables to reduce the cost of developing a new model for other meat and meat
products.
Figure 10.4 shows an example sample as an input (meat sample). The spectrum of
images is captured either by computer vision or hyperspectral image and pixels are
classified using CNN regression.
Gao et al. (2021) developed one-dimension CNN in the combination of a
hyperspectral imaging system (band range between 292 and 865 nm) to detect
aflatoxin in pixel level by classifying clean area and aflatoxin area. They observed
that classification of pixels produced a maximum accuracy of 96% (peanut), 92%
(maize), and 94% (mixed data) and thus produced an algorithm for the intelligent
classification of aflatoxin, thus showing a positive significance for grain processing.

10.5 Recurrent Neural Network (RNN)

Recurrent neural networks (Fig. 10.5) are known for their sequential data or time
series modeling and are often used to develop multiple layered models. RNN uses
the data from previous information stored in the hidden layers and then transferred to
the next stage as input data and continues as a recurrent loop of neural network
(Cossu et al. 2021). According to Yu et al. (2019), the RNN consists of hidden layers
influenced by past data and current input using a feedback network. Moreover, RNN
and its variants of long-short term memory have been successfully reported and
10 Neural Network Approach for Risk Assessment Along the Food Supply Chain 295

Fig. 10.5 Recurrent neural network

reviewed for its language modeling, speech recognition, and other sequence predic-
tion problems (Keren and Schuller 2016; Yu et al. 2019).
RNN consists of internal loops that cause a network, which may delay the
activation dependencies across the elements in the network (Marhon et al. 2013).
Kolen and Kremer (2001) discussed the dynamic recurrent network model using the
integration function of time series. For example, the RNN structure includes the
following layers, i.e., input layer “i” connects the hidden layer “h” with changing
time series as new data points arise and produce output value “y” at the sequence of a
network. The hidden layer at the given time acts as a function of the input vector at
the time “t” and the hidden vector at the time (t 1) and in a sequence of time series
produces varying inputs such as i(t) i(t 1). Network parameters such as “a, b, and
c” are hidden layers that improve the model’s output, which is continuously fetched
back to the network to improve the result or output.

10.5.1 Application of RNN in Food

Generally, RNN applications are widely used in time series data; however, it is
applied in predicting the bacteria growth and changes in the growth phase.
In a study, Cheroutre-Vialette and Lebert (2000) investigated the growth of the
Listeria monocytogenes in a tryptic meat broth with varying pH and concentration of
NaCl and measured using optical density at a wavelength of 600 nm. They devel-
oped a multilayered RNN structure to different model parameters such as growth rate
and time with varying pH and NaCl percentage conditions and predicted the output
response. After several training and testing data repetitions, the RNN captured the
growth variation with two specific parameter combinations such as pH 9.1 and 68%
296 U. Tiwari

NaCl. They also reported that application of RNN will facilitate to predict different
characteristics of pathogen response including initiation of a lag time and growth
recovery in fluctuating environmental conditions. Buchanan (1993) described that
any dynamic model techniques could capture microbial impact along the food chain
and support the food safety hygiene system. In a study, Kang et al. (2021) developed
an efficient tool for rapid identification of foodborne pathogens with hyperspectral
microscopic imaging and integrated the information using classification algorithms
RNN. They successfully differentiated five common food pathogens (C. jejuni,
E. coli, L. innocua, S. typhimurium, and S. aureus) and concluded that RNN
especially long short-term memory (LSTM) algorithms predicts direct processing
of spectra from a different region of interest. Lee et al. (2020) showed a multi-period
product recommender system that helps capture purchasing orders and repetitive
purchase patterns of fresh food using LSTM (a form of RNN) model. They devel-
oped a robust model to monitor and create a rapid decision to save customers’
shopping time and money, thus reducing food waste and subsequently inducing
planned consumption. Likewise, Fermo et al. (2021) classified oranges based on size
using RNN and predicted parameters of quality and shape of fruits. They used
low-cost digital image processing in combination with RNN to monitor the defects
in fruits to identify the presence of fungi, stains, rot, etc. and subsequently reduce the
farmers’ economic feasibility.

10.6 Importance of Risk Assessment Along the Food Chain

Risk assessment is a process of systematic auditing at different stages along the food
chain including identification of hazards, dose–response assessment, exposure
assessment, and characterization of risk (FAO 2020). It provides efficient and
effective auditing that requires eliminating risk and exposure to human health
(Sawyer and Dittenhofer 1996). Risk assessment is important to evaluate the various
levels of risk and predict outcomes that facilitate risk managers with suitable
information for decision-making. Risk assessment outcomes also provide guidance
to develop public health policy for food processors and consumers, thereby contrib-
uting to economic and regulatory inputs. Neural network technology is a powerful
tool that can assist in conducting systematic auditing with information overload and
bias and captures all possible interactions between independent variables. Integra-
tion of neural network approaches for risk assessment will provide a robust and
effective risk assessment and prevent risks. Therefore, predicting a risk along the
food chain with efficient management and evaluation of risks will increase the ability
to supply safe food.
10 Neural Network Approach for Risk Assessment Along the Food Supply Chain 297

10.6.1 Risk Assessment

Any food chain requires a suitable risk assessment to evaluate and assess possible
farm-to-fork risks. Moreover, risk assessment is an essential tool that contributes to
risk analysis in food safety for emerging and reoccurring bacteria causing risks and
hazards to human health. As it is important to understand the definition and differ-
ence between “hazard” compared to “risk” (FAO 2020), “risk is an estimate of
probability and severity of the adverse health effects, in exposed populations,
consequential to hazards in food,” while “a hazard is a biological, chemical or
physical agent in or condition of, food with the potential to cause harm.” Food
processors, researchers, handlers, and all stakeholders along the chain must under-
stand the association of risk and hazard and work together to reduce the risk to
consumers and thus apply suitable food safety controls. Implementation of food
safety has become a priority control globally and even today, stakeholders’ engage-
ment in the food supply chain continuously works to develop risk assessments to
reduce the coronavirus (FAO 2020).
Risk analyses are classified as qualitative and quantitative analyses, which reduce
the degree of risk to human health (WHO/FAO 2006) and discuss the application in
sustainable food processing by Tiwari and Cummins (2013). The qualitative risk
assessment determines the importance of risks and evaluates the likelihood of impact
of the hazard. Simply capture the risk impact and its degree, arrangement in a matrix
format based on the probability (e.g., possibilities range from 0.05 to 1) and rank the
risks as “very low, low, medium, and high” to protect human health. The Food
Regulatory Agency worldwide has developed official meat inspections to ensure the
safety of meat, especially for the risk of tuberculosis, maintaining food biosecurity
for international trade. Hill et al. (2014) conducted a qualitative risk assessment and
visually inspected post-mortem meat of cattle, sheep, goats, and farmed deer for risk
of Mycobacterium avium subsp. paratuberculosis (cattle), M. bovis (all species),
Fasciola hepatica (all species), Erysipelothrix rhusiopathiae (cattle, sheep, deer),
Cysticercus bovis (cattle), etc. They reported the following (1) a very low risk to
animal health to tuberculosis lesions although being missed by meat inspectors; (2) a
very low to low level of risk for Mycobacterium bovis and Cysticercus bovis,
respectively; (3) negligible to very low for Fasciola hepatica. Figure 10.6 outlines
the qualitative approaches for estimating the risk along the chain, indicating possible
entry of contamination and corresponding risk levels and food safety controls as per
compliances (e.g., European Food Safety Authority, U.S. Food and Drug
Administration).
Qualitative risk approaches are used to capture the risk (microorganism or
chemical or physical contamination) and its interactions at each stage, assess the
probability of occurrence, and characterize the associated risk based on the matrix.
Moreover, the exposure assessment and health impact models will also help to
evaluate the risk level of foodborne disease to human health. In a study, Coasta
et al. (2020) performed a qualitative risk assessment from farm to the table and
characterized the risk of Salmonella enterica hazard occurring in unprocessed pork
298 U. Tiwari

Fig. 10.6 Qualitative risk assessment outline along the chain (M microbial, C chemical, P physical)

and developed a monitoring process and control in the entire pork chain. Likewise,
Ali et al. (2021) conducted a qualitative risk assessment of liquid nitrogen in foods
and beverages as it is widely used in ice cream, snacks, cocktails, etc. They evaluated
the literature, conducted a qualitative risk assessment, and found that an excess level
10 Neural Network Approach for Risk Assessment Along the Food Supply Chain 299

Fig. 10.7 Quantitative risk assessment outline along the farm-to-fork chain

of liquid nitrogen consumption may lead to adverse health effects and assist in
developing appropriate public health guidelines.
Quantitative risk assessment also means quantifying risks along the chain, which
is often conducted to evaluate the uncertainties and provides various outcomes for
the stakeholders to identify hazards/threats in the chain. Probabilistic risk assessment
is a key tool and approach to capture the uncertainties and predict possible outcomes
to conduct a risk analysis. Three elements in risk analysis are risk assessment, risk
management, and risk communication. Risk assessment is based on numerical and
applying mathematical and statistical techniques in combination to evaluate the risk
systematically in a food chain (Fig. 10.7). Additionally, probability including vari-
ability and uncertainty are taken into account to assess the adverse health effect.
Quantification of risk is usually estimated by predictive modeling or simulation
approaches affected by randomness and Monte Carlo methods of simulation.

10.6.2 Monte Carlo Simulation Approaches

The use of Monte Carlo simulation has been ongoing since the 1940s as the
similarity of statistical simulation to a game of chance and the name was coined to
reflect the center of gambling of Monaco. This simulation method is generally used
as an alternative to analytical mathematics to capture the randomness in a model
300 U. Tiwari

evaluating its behavior in a random sample. Randomization can be achieved using

computerized or physical methods that produce numbers with no sequential pattern
and are arranged purely by chance. Additionally, Monte Carlo method is based on
running the model several times to generate random sampling and random yield
outcomes on each output variable. Over the years, several simulation software
packages (e.g., MS. Excel Solver, Risk Solver Engine) including a series of math-
ematical equations and algorithms are developed and widely applied in various
industry and academic research (Thomopoulos 2013). Monte Carlo simulation
techniques are often represented using probability distributions which capture the
uncertainties and variabilities in the large sample population and provide various
outcomes.
In a recent study, Tsaloumi et al. (2021) conducted a quantitative risk assessment
of Listeria monocytogenes in 87 ready-to-eat cooked meat products sliced at retail
stores in Greece. They developed a risk assessment model to evaluate the risk of
listeriosis which included input data such as prevalence, the concentration of path-
ogen, physicochemical characteristics (water activity, pH, nitrite, etc.), and lactic
acid bacteria. The authors found that the products are highly correlated with the
concentration of nitrites and risk per serving increased with a lower concentration.
They reported significant variability in the growth behavior among strains and the
product was rejected prior to consumption based on the predicted outcome of Monte
Carlo simulation. Wong et al. (2020) quantified the intake of 43 commercial food
samples and identified the presence of carcinogens (3-MCPD and 1,3-DCP) in soy
sauces obtained from the Malaysian market. They reported a high concentration of
3-MCPD in chicken seasoning cubes that exceeded Malaysia’s maximum tolerable
limit of 0.02 mg kg 1 with 99th percentiles that were lower than 4 μg kg 1 body
weight day 1 among the population.
Likewise, evaluating the nitrites (food additives) present in the food chain that are
added to diverse food patterns, storage conditions, and processing (Vlachou et al.
2020). The authors conducted a quantitative risk assessment to assess the nitrites
intake for the Austrian adult population. They evaluated nitrites in 3282 samples
from 20 categories of meat products, 1968 samples of water, and 910 samples of
mineral water and compared the levels of nitrites to the recommended regulations
and classified lower limit and upper limit of exposure. The risk assessment of nitrites
concluded that risk estimates indicate a low level of concern among the Austrian
adult population (Vlachou et al. 2020).

10.7 Integration of Neural Network and Risk Assessment

Approach Along the Food Chain

A combination of neural network and risk assessment approaches is recently applied

along the food chain to capture possible uncertainty around the model input data
parameters and other dependent variables associated with the model (Goel and
10 Neural Network Approach for Risk Assessment Along the Food Supply Chain 301

Bajpai 2020). Integrating the neural network with the risk assessment approach
along the food chain has been an effective approach. Jeyamkondan et al. (2001)
introduced a new technique to overcome the traditional time-consuming microbio-
logical enumeration methods, thus increasing the possibility of assessing the growth
of various microorganisms. They evaluated the growth of three bacteria using ANN
method which combined both general regression neural network (GRNN) and
published statistical models and determined a reliable prediction with the GRNN.
Using this combination, the authors concluded that ANN is a useful tool that
facilitates accurate predictive microbiology to evaluate risk in food product
development.
Oscar (2009) demonstrated accurate prediction of survival and growth of Salmo-
nella on raw chicken skin using an integrated model in combination with GRNN and
risk assessment. The study found that the GRNN model could predict over 70% for
acceptable model validation. The robustness of the model was reported to predict the
growth and survival of Salmonella from less than 1 log of contamination per chicken
skin portion. The author concluded that the GRNN model would overcome the
limitations of the regression model and predict the risk of microbial contamination
accurately, which will be utilized in hazard analysis and critical control point and
risk assessment.
In another study, Tiwari et al. (2015) developed a farm-to-fork model using
Bayesian methods for neural network approach combined with a quantitative risk
assessment to capture raw and pasteurized milk cheese contamination. They used
Bayesian inferences for the neural network model to capture the Listeria
monocytogenes contamination at farm level and to model the processing stages;
risk assessment modeling approach was utilized to capture the probability of each
parameter and quantified the model using mathematical equations. They observed
that the model predicted a high concentration of L. monocytogenes contamination in
raw milk cheese (mean: 2.19 log10 CFU/g) compared to pasteurized milk cheese
(mean: 1.73 log10 CFU/g). The authors concluded that the integrated model pro-
vides guidance for food processors and policymakers to evaluate the routes of
Listeria contamination and thus facilitated further setting of performance objectives
and food safety objectives along the cheese chain (Tiwari et al. 2015). Recently, Niu
et al. (2021) conducted a safety risk assessment for evaluating chemical contami-
nants (benzopyrene, aflatoxin B1, and heavy metals) in edible vegetable oil using an
analytic hierarchy process and backpropagation neural network. They demonstrated
an early warning system by applying a deep neural network to the safety risk
assessment model and concluded that the model improves the level of consumption
safety of edible vegetable oil. Likewise, Jianying et al. (2021) assessed the possible
risk levels in the fresh grape supply chain using optimized backpropagation neural
network combined with a risk assessment model. They showed that evaluating risk
optimization of the supply chain would induce a sustainable approach.
302 U. Tiwari

10.8 Conclusions

Smart or early prediction and assessing risk at every possible stage along the food
chain are important for human health. Identifying risk and applying controls and
eliminating hazards (microbial, chemical, and physical) in the food chain will ensure
food safety. A combination of neural network methods with risk assessment simu-
lation approaches would facilitate all stakeholders along the food chain to put in
place the necessary process controls and evaluate the outcome for achieving food
safety. Concerns around the food quality and to gain consumer confidence, nowa-
days several food industries are focused on reducing any incident occurring in food
process stages. Hence, the application of neural network and risk assessment models
is beneficial to monitor and forecast risk levels and develop a continuous food supply
chain evaluation method.

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 Part IV
Sustainable Food Waste Management
and Coproduct Recovery
Chapter 11
Waste Minimization and Management
in Food Industry

Rahul Kumar, Vasudha Sharma, and Maria Jose Oruna-Concha

Abstract Food waste nowadays has reached one-third of the entire food production.
Considering this fact, if food waste was a country, its impact could have been third
on global warming, after China and the USA. Several catalysts result in food waste,
such as household-generated waste, overproduction, lack of facilities to store and
preserve for a longer duration, lack of cold chain facilities, food processing industry
and food trade losses, post-harvest losses due to mechanical infrastructure, and
automation in handling and packaging. Waste minimization is a primary step for
waste management in the food industries and has the potential to save millions of
economic resources. Waste minimization practices such as increased machinery
performance, the better quality of the fresh produce, reuse of trimmed products,
specialized packaging for particular produce, appropriate product disposal, and well-
analysed market demand could significantly reduce waste. Moreover, waste man-
agement includes several basic steps such as reducing waste, reusing the discarded
resources while ensuring customer safety, recovery of the health-promoting bioac-
tive and food additives from produced waste, and desirable measures for disposal of
the waste to minimize waste any hazards towards life and the environment. Hence,
the primary implementation of waste minimization and management operations
could reduce waste production in food industries and protect the resources from
unwanted disposal and economic loss. Additionally, it will also protect the environ-
ment and life on our planet.

Keywords Waste · Minimization · Management · Hazard · Environment

R. Kumar (*) · M. J. Oruna-Concha

Department of Food and Nutritional Sciences, University of Reading, Reading, UK
e-mail: r.kumar@pgr.reading.ac.uk
V. Sharma
Department of Food Technology, Jamia Hamdard, New Delhi, India

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 309
S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies,
https://doi.org/10.1007/978-981-19-1746-2_11
310 R. Kumar et al.

11.1 Introduction and Definition of Food Waste in the Food

Industry

The food industry is one of the largest economic forums globally and significantly
contributes to the global gross domestic product (GDP). Every third person on this
planet is employed in the food or agriculture sector for their living (Jain et al. 2018).
The food industry is composed of Agriculture, Food Processing, Distribution,
Regulation, Financial Services, Research and Development, and Marketing (Sadiku
et al. 2019), out of these steps, many need to be controlled and optimized; otherwise,
they can cause waste in the food industry. As recently reported by (FAO 2020) FAO,
1/3rd loss of the food waste is alarming for global food security, considering the fact
that approximately 805 million people in the world suffer from food shortage and
undernutrition (Pap and Myllykoski 2014; Food and Agriculture Organization of the
United Nations (FAO) 2015). Furthermore, the food processing industries produce
40–50% of the final agricultural produce in the form of bagasse, peels, trimmings,
stems, shells, bran, and seeds, resulting in a decrease in the quantity or quality of
food as a result of decisions and actions by retailers, food service providers, and
consumers (Quirós-Sauceda et al. 2014). Conversely, the waste produced from the
food industries is related to the processing steps and damaged and discarded during
transportation, storage, and other pre-processing steps.
The waste produced by food industries is far less hazardous than the other
industries and is a minor contributor to environmental loads (Torres-León et al.
2018). On the contrary, the amount of water used for food production and waste-
water generation during processing is the highest among the manufacturing sector
(Pap and Myllykoski 2014). Not only the processing, the water requirements are
throughout the supply chain and cooking. The wastewater disposed of by food
industries consists of dissolved solids, organic and inorganic acids, and other soluble
matters that would increase the water’s biological and chemical oxygen demand
(Allison and Simmons 2017).
This chapter illustrates a comprehensive overview of the waste caused in the food
industry. As the food industry is a complex system to operate, which involves
producers, processors, supply chain, retailers, and end consumers, hence, every
consequent step inevitably produces some waste related to food and by other
means such as water discharge loaded with chemicals and soluble components of
food. This chapter looks explicitly into the types of waste generated in the food
industry and the major shortcomings of the same. In addition, case studies and
examples have been discussed on ways to minimize waste production and manage-
ment principles to minimize waste generation. It is also imperative to discuss
industry-specific wastes and their role and mode of valorization; most importantly,
the use of waste and products to their best potential has been the major focus of this
chapter.
In order to holistically address the problems of food waste in the food industry
from producers to end consumers; the need of the hour is to use and introduce some
smart and data-driven technology, which can provide real-time analysis and
11 Waste Minimization and Management in Food Industry 311

suggestions about the future steps to reduce the food waste and use the resources to
their utmost potential. Thus, the introduction of data and big data analysis, concepts
of the Internet of Things (IoT), and cloud manufacturing (CM) to reduce food waste
in the food industry have also been discussed. This chapter specifically focuses on
technologies like data analysis IoT in the food industry and CM to impact the
transformation of the food sector for better sustainability, efficiency, convenience,
and economy. The exhibition and execution of these developments will provide the
opportunity for production and trade of the products like never before; information
sharing between the stakeholders in the supply chain, collective information about
the product which is reaching from retailers to end consumers, and the infrastructure
needed and available in agri-food business will provide transparency in systems.
These developments have immense potential to change the face of the food sector
business model applicable nowadays.

11.2 Types of Waste Generated in the Food Industry

Waste generation in food industries is inevitable and a consequential result of food

processing. Several waste classes are discarded from food industries during produc-
tion, processing, distribution, and consumption. Moreover, the control on the quality
of waste before discharging into the environment is of utmost importance to facilitate
further utilization (Chen 2009). The waste generated could be of solid, liquid, and
gaseous nature that further could be classified as Biological, Chemical, and Physical

Table 11.1 Types of waste produced and their classification from various food industries
Sl. Food
no. industry Avoidable waste Unavoidable waste
1 Chicken Flesh Feather and bones
2 Fish Flesh Scales, tail, and bones
3 Fruits and Peel Seeds, trims, and leaves
vegetables
4 Oil and Oilseed meals Ploughing back in the field
oilseeds
5 Spices Hull and seeds Spent spices after use
6 Bread and Leftovers mix and non-quality Contaminated during processing and
bakery compliance handling
7 Tea and Over- or underprocessed Prepared too much for sensory panellists
coffee products
8 Milk Sample from platform test and Remaining in the process line or not
processing inadequate processed supply within shelf-life period
9 Cereal Bran, germ, and fibre Hull and stalks
processing
10 Distilleries Yeast sludge Spent wash
312 R. Kumar et al.

wastes, based on their constituents. Table 11.1 represents the production of different
waste from the same raw resource.

11.2.1 Biological Waste as Food

This can be categorized into the following types of waste.

• Unavoidable Waste: The segment of waste generated from food during
processing and preparation of food and drink that could not normally be con-
sumed, such as peels of vegetables, bones from meat, egg shells, and tea bags
(WRAP and IGD 2020).
• Possibly Avoidable Waste: Those food items which are suitable to one class of
people and possibly not liked by others, there is another possibility that when
food was prepared, it was 100% edible by volume, but after some time some part
is not suitable for consumption such as bread crusts, potato skins (WRAP and
IGD 2020).
• Avoidable Waste: Those portions of food that were edible at some point in time,
but due to delay in timely consumption or being prepared in larger quantities than
required, such as a slice of bread, apple, and meat (WRAP and IGD 2020).

11.2.2 Chemical Waste

The first kind of chemical waste discharged from the food industries is nitrogenous
compounds, i.e., proteins. As nitrogen exits in different forms in essential utilities in
industry, environmentally important nitrogen forms include ammonia, nitrate, nitro-
gen gas, and organic nitrogen, such as protein. Moreover, the digestion done by
bacteria converts the nitrogen into ammonia which has two forms such as NH3 and
NH4+, and the non-ionic form of ammonia (NH3) is toxic for aquatic life (USEPA
2013). Another element commonly rejected from the food industries is phosphorous
in the form of phosphate, and it is a growth-limiting factor for aquatic animals.
During the respiration phase at night, hosphorous can deplete the dissolved oxygen
in water bodies, causing a fish to kill. Sulphur is another commonly disposed
chemical from food industries. The use of sulphur dioxide in pre-treatment of fruits
or sodium bisulphide in processing may result in sulphur content in wastewater,
which is detrimental for the aquatic life and a major cause of acid rain (Chen 2009).
11 Waste Minimization and Management in Food Industry 313

Global Food Waste Mapping

Household Waste
Services Waste
70
Retail Waste
% of Global Food Waste

60
50
40
30
20
10
0
Food Waste Channels

Fig. 11.1 Catalysts of global food waste and their contribution

11.3 Catalysts of Producing Waste in the Food Industry

There are several contributors and catalysts of food waste generation, as shown in
Fig. 11.1.

11.3.1 Household-Generated Waste

In developing countries, it has been estimated that most of the losses occur during
production and storage, whereas, in developed countries, food waste is prominent on
the household scale (WRAP 2013). In household food waste, generally, the waste
gets created due to unplanned meals and untimely use of the food. Conversely,
shopping behaviour was also described as one of the major causes of food losses
among households (Canali et al. 2014).

11.3.2 Overproduction

Previously, it was thought that overproduction of food is an opportunity for meeting

food security for the current world and future population. However, the shortcom-
ings are the unavailability of storage and warehouses to process them and further put
them into the supply chain. In developing countries, overproduction is a major
culprit for food waste, and it accounts for nearly 7–8% of the fresh produce (Rezaei
and Liu 2017).
314 R. Kumar et al.

11.3.3 Lack of Cold Supply Chain Facilities

In practice, it is apparent that a working cold chain is essential for the protection and
transportation of perishable commodities, both fresh and frozen. Perishable food loss
can be kept to as low as 2% in places of the world where the cold chain has been
created and is functioning well. A fully functional cold chain may successfully
prevent and decrease food loss when used properly and regularly (Rezaei and Liu
2017).
Operator mistake, insufficient pre-cooling, poor loading methods, inadequate
insulation, badly operating refrigerated equipment, or even something as basic as
cargo- or walk-in cooler doors being left open too long are all examples of difficul-
ties that can cause breaches in the cold chain. Any of these fractures can cause
perishable goods to be damaged by exposing them to temperatures that are too hot or
cold, resulting in food being lost or squandered (Sahakian et al. 2020).

11.3.4 Food Processing Industry and Food Trade Losses

These kinds of losses are mostly visible in transition developing countries or

developed countries, which comes under post-harvest losses in the market and
processing industry. Concerning the total waste, 20% is made during food
processing, distribution, and retail selling of produce. A larger part of these wastes
is used as animal feed (WRAP 2013). The types of wastes are mostly the by-products
or unsold prepared food products. From the food processing industry waste, several
brands are the major stakeholders in the production of waste because in the appre-
hension of the shortage of the vegetables and to meet the requirement at short notice,
these brands keep a surplus amount in processing plants, and that is a leading cause
of waste, if not timely traded. Measures of waste created by food retailers differ
between source types. Little markets produce proportionately more waste than huge
stores, as customers will generally utilize the former for top-up shopping, making
demand unpredictable (Parfitt et al. 2010).

11.3.5 Post-harvest Losses Due to Mechanical Infrastructure

Post-harvest losses generally occur due to insufficient infrastructure, processing

equipment, packaging, and handling. To begin with, when harvesting, a good
quantity of fresh produce remains behind in the field or gets ploughed again into
the soil. Harvesting is a combination of experienced art and technology to determine
the maturity of products, and any errors may cause serious losses to the fresh
produce. These losses have been estimated up to 8.8% (Food and Agriculture
Organization of the United Nations (FAO) 2015) of the total produce. For
11 Waste Minimization and Management in Food Industry 315

harvesting, the used mechanical system should ensure that it would not damage the
product during harvesting and storage; otherwise, it may reduce the quality desirable
for the marketability of the fresh produce and it accounts for an average of 8.9%
(Food and Agriculture Organization of the United Nations (FAO) 2015) of the total
losses.

11.4 Principles of Waste Minimization in Food Industry

11.4.1 Increased Machinery Performance

The largest potential for the food and drink industry towards waste minimization is
by reducing raw material losses at source machinery, which handles raw material
with higher accuracy (Henningsson et al. 2004). One of the most effective methods
to boost a company’s profitability is eliminating waste. To decrease or eliminate
waste’s impact on productivity, overall performance, and quality, it is critical to
understand what waste is and where it resides. Manufacturers now have a great
chance to adopt new technologies that will help them to take their lean manufactur-
ing projects to the next level, thanks to the arrival of digital equipment transforma-
tion. Of course, lean manufacturing aims to continuously improve production
processes while reducing waste and expenses. Not only can they rectify existing
inefficiencies by monitoring machine performance, but they can also uncover new
difficulties, allowing them to successfully implement preventive or predictive main-
tenance methods. Production of defects is one of the most immediately visible losses
in manufacturing. Wastes such as scrapping manufactured goods and items that need
to be reworked are examples (Rudolph Raj and Seetharaman 2013; Villarejo et al.
2014).

11.4.2 Better Quality of the Fresh Produce

In many cases, the problems begin at the earliest stages, on the fields themselves.
One reason for this is that there is no accurate way to evaluate quality pre-harvest or
on the tree at scale. Another is that quality control is both manual and expensive and
out of scope for most growers with limited budgets. In addition, there is limited data
for the target quality standard at the point of harvest. Many areas also have the
additional challenge of poor weather, making it difficult to ascertain the root cause of
the waste (Pinotti et al. 2020). In Sub-Saharan Africa, for example, losses at the farm
level can be greater than 35% of all crops (Otles et al. 2015). The Clarifruit
technology is the first of its kind, an end-to-end automatic quality control solution
that can meaningfully reduce the losses and waste that stakeholders experience
across the value chain, from field to fork. By simply taking a photo of the produce
in question with a regular smartphone device, anyone can benefit from Artificial
316 R. Kumar et al.

Intelligence (AI)-derived insights into the quality of the fruit or vegetable (https://
www.clarifruit.com/).

11.4.3 Reuse of Trimmed Products

Waste cuttings and trimmings account for over 30% of the raw product stream in a
major multi-vegetable processing firm. Solid wastes were often swept dry or
mechanically transferred to a dump truck or storage location outside the plant. It
might also be swept down the drains, resulting in organic and solid loadings in the
wastewater (Sen 2012). Lettuce and cabbage leaves, carrot tops, celery leaf and
butts, yellow and decayed spinach leaves, broccoli and cauliflower stems and leaves,
corn husks and cobs, unusable turnips, parsnips, Brussels sprouts, radishes, onion
peels, and other green leaf product wastes are examples of solid waste from the
vegetable processing industry (Pinotti et al. 2020). Solid waste in the seafood sector
often consists of fish shells and heads from the processing step. Furthermore, only
25–50% of raw materials are used in primary production, while the rest 50–75% are
discarded. The trash is either used for low-value items or discarded. Similarly,
inedible fish and endoskeleton shell portions from the crustacean peeling process
might result in a considerable amount of trash and by-products. The creation of
waste is greatly reliant on the species and procedures that were used (Sen 2012).

11.4.4 Specialized Packaging for Transport of Fresh Produce

In Central and Southern Asia, losses in transport can be as much as 30% of the
overall yield, a huge amount of waste. As fruits and vegetables can be moved
multiple times, taken through different routes, and through numerous handlers, the
ability to track the produce across the supply chain is limited. Fresh fruits and
vegetables go through a negotiation process at each handover, where prices are
driven down, food rejected, and a certain percentage wasted (Rudolph Raj and
Seetharaman 2013). This not only causes unnecessary loss of fresh fruits and
vegetables, but also makes it challenging for stakeholders to forecast their revenues
or sales ahead of time. Hence, to prevent the further loss of fresh produce in
transport, special attention needs to be paid to specified commodity-based packaging
for increased longevity for transport without mechanical damage.

11.4.5 Well-Analysed Market Demand

Improve farmers’ access to market data and information so they can better match
supply with market demands and minimize over-supply and under-processing,
11 Waste Minimization and Management in Food Industry 317

which would result in waste from fresh crops. Moreover, the market analysis should
not be limited to the supply and demand, but the kind of standards needed for
marketing should also be prioritized. Further study on the relationship between
marketing standards and food waste is needed, taking into consideration the results
of the review of marketing standards. For economic and environmental reasons, it is
important to consider reducing resource waste by preventing the formation of food
waste (Jülicher 2019).

11.5 Principles of Waste Management in Food Industry

11.5.1 Reduction and Prevention of Waste

Money may be saved on garbage collection, treatment, and disposal by avoiding

waste before it occurs. It also minimizes the environmental effect and expenses of
mining, producing, and using additional raw resources. When the full costs of waste
are evaluated, this becomes much more essential. The global scale of reducing food
waste by 2020 was 50%; however, this figure was not met; however, some success
was significantly achieved, for example, the UK has already reduced the food waste
at the household level by 21% in the last decade, and more progress in the same
direction is expected in coming decades. The World Resources Institute has fore-
casted that if food waste on a global scale can be reduced by 22% by 2050, the total
produce will be able to secure food for every individual in 2050 (Baldwin 2014).

11.5.2 Reuse of the Discarded Resources While Ensuring

Customer Safety

The next desire is to reuse items and materials for the same (or similar) purpose.
Before a material may be reused, it should be evaluated for quality and consumer
safety since slight changes in composition or additions may be required before the
product meets the requisite standard. This might involve things like keeping unused
resources from one product to utilize on the next (Baldwin 2014).

11.5.3 Recovery of the Health-Promoting Bioactive

and Additives from Waste

If processed for the greater good, waste can take another potential form, i.e., health-
promoting ingredients, biologically and mechanically. Recovery of health-
promoting substances such as antioxidants, phenolics, flavonoids, anthocyanins,
318 R. Kumar et al.

and betalains could be proven a game-changer in waste processing (Baldwin 2014).

Conversely, wastage created by fruits and vegetables is nearly 44% of the total
waste. The natural colour contained by these commodities could be a preferred
alternative for artificial colouring in the food industry. The increasing awareness
among the consumers for minimum artificial additives in food processing is pressing
the food industry to emphasize bio-processing of waste and focus on the recovery of
valuable additives from food waste (Hang 2004).

11.5.4 Desirable Measures for Disposal of the Waste

The non-utilizable waste must be disposed of on land after the conclusion of all
sorting, biological, and thermal processes. To make garbage appropriate for
landfilling, material may need to be transformed by mechanical treatment, thermal
treatment, or other means prior to disposal (Girotto et al. 2015).
Landfilling organic waste releases gases like methane (a strong greenhouse gas)
and pollutes soil and water, not to mention stink and other social annoyances. Food
waste should only be disposed of in landfills as a last option, especially in light of
rising land scarcity for Earth people (February et al. 2012).

11.6 Types of Industry and Their Waste Minimization

and Management
11.6.1 Fruits and Vegetable Industry

Waste generated from the fruit and vegetable processing industries is classified as
solid municipal waste. The processing industries are the third frontier to produce
waste after production and handling and storage. Thus, the total amount of fruits and
vegetable waste accounts for 0.6 billion tons (44% of the global output), and it is
most by any consumable food group. The total cost of wasted fruits and vegetables
accounted for 435 billion U.S. dollars in 2015. The waste created from fresh produce
of fruits and vegetables in the industry is in different forms such as trimmings, peels,
seeds, stalks, redundant, oversized products, undersized products, damaged during
processing, damaged during unloading, and packaging. The wasted fresh produce is
diverted to different handling mechanisms such as 73% goes to animal feed, 20% in
landfill, equally 2% each goes to composting and donation, and 4% goes to various
small-scale mechanisms (Baldwin 2014; Jain et al. 2018).
11 Waste Minimization and Management in Food Industry 319

11.6.2 Bakery Industry

The bakery industry alone in the UK accounts for 3.38 billion pounds and is one of
the largest food industries in the world. Moreover, bread is the single most sold item
among the bakery products. However, the waste created from bakery industries is the
fourth-largest in the list of food wastage. Perhaps, bread is the most wasted bakery
product, and the major reasons for waste are untimely attention over the due date of
using bread. More than 75–80% of the bread and other bakery products are wasted
due to untimely usage and the remaining 15–20% waste is created due to eating and
cooking practices (WRAP 2011).
Bread waste reduction could be done by following practices such as creating
awareness and communicating about the yearly waste of money on bread that people
are not consuming post-purchase. Secondly, protect the freshly purchased bread by
keeping it in an air-tight container after opening the pack, as air degrades bread
quality. Next could be the idea of resealable packing for the bread bags as well, and
lastly, suggesting some recipes and other uses of bread for consumption other than
conventional uses (Strotmann et al. 2017).
Repurposing bread trash rather than sending it to the landfill might generate a new
revenue stream for the bakery business while also reducing the industry’s carbon
impact. Indeed, it is estimated that we waste several hundred tonnes of bread every
day around the world: bread is particularly vulnerable to wastage as a food product
due to its short shelf life, high manufacturing losses, and consumer preferences for
products like crustless bread—all of which send plenty of perfectly good bread to the
trash (Strotmann et al. 2017; Sowbhagya 2019).

11.6.3 Dairy Industry

As we have discussed previously as well, the demand for production is another

reasonable answer for the generation of waste from processing plants of food
industries. Similarly, the rise in global demand for milk and milk products has led
to creating waste on a larger scale. The major wastes which are discharged from
dairy industries are whey, dairy sludges, and wastewater from cleaning, processing,
and sanitary work. These discharges from industries are loaded with nutrients and
high on biological and chemical oxygen demand. Moreover, they also contain a
variety of acids and alkaline detergents, which are ultimately responsible for pollut-
ing natural resources such as soil, air, and water (Bernstad Saraiva Schott and
Andersson 2015; Ahmad et al. 2019).
Every year, around 4–11 million tonnes of dairy waste are dumped into the
environment, posing a major threat to biodiversity. The reduction of dissolved
oxygen is one of the most important issues produced by the direct release of raw
wastewater into the environment. Fat effluents, such as oil and grease, build a film on
320 R. Kumar et al.

the water surface, preventing oxygen transport and, as a result, putting aquatic
creatures and plants in perilous survival situations (Ahmad et al. 2019).

11.6.4 Meat and Fish Industry

Fish and meat waste mainly comprised heads, bones, skin, viscera, and legs. These
waste resources are a great source of minerals, protein (58% dry matter), and fat
(19% dry matter). In fish waste, the present fat is enriched with monounsaturated
acids, palmitic and oleic acids. Similarly, the high ash content from fish waste leads
to higher mineral content (22% dry matter) (Arvanitoyannis and Kassaveti 2008).
However, heavy toxic substances are also detected in fish. Marine wastes and their
disposal may pollute the soil and water with heavy metals, which are major reasons
for many organ failures (Jaishankar et al. 2014).
Crustacean exoskeletons contain 15–20% chitin by dry weight, making it a
structural component. The synthesis of chitin and chitosan from food waste (crusta-
cean canning) has proven to be both ecologically and economically viable, espe-
cially when carotenoids are recovered. The wastes include significant chitin levels,
which is sold as a fish food ingredient(Arvanitoyannis and Kassaveti 2008; Kumar
et al. 2017).

11.6.5 Spices Industry

The main goods derived from spices are spice oils and oleoresins. In the spice oil and
oleoresin sector, 80–90% of the bulk spice is left over as residue, which has no
commercial value or uses at the moment, posing a disposal difficulty. Finding a
technique to use industrial waste for food uses and so avoid pollution is extremely
desirable. With a growing interest in health foods and a focus on the health benefits
of dietary fibre, it is possible that the spent residue from chilli, cumin, coriander, and
pepper after primary processing could provide a new source of low-cost dietary fibre
in certain food products, particularly bakery products. Spice spents are high in
dietary fibre, protein, vitamins, polyphenols, and critical minerals including calcium,
iron, magnesium, and zinc, all of which are necessary for the body’s metabolic
activities. The dietary fibre content of the spice spents (44–62%) is much higher than
that of many fruits and vegetables (Jain et al. 2018; Sowbhagya 2019).
Henceforth, to describe and introduce a few spice spents, including as cumin,
chilli, celery, pepper, ginger, and turmeric, as a unique rich source of functional
nutrients in low-fibre, low-protein bakery goods (biscuits, bread), resulting in a
greater protein and dietary fibre-rich product. Spice spents have also been success-
fully employed in composites to increase tensile strength and thermal stability, as a
weedicide, and the manufacture of bioactive films, in addition to food uses. Spice
11 Waste Minimization and Management in Food Industry 321

spents might be used in various functional food compositions, which would be good
for both health and the environment by lowering pollution (Sowbhagya 2019).

11.6.6 Cereals and Pulses Processing Industry

Cereal production and processing is one of the most important sectors of the agri-
food industry, as cereal food products account for more than 20% of daily consump-
tion. Furthermore, cereal products serve as the foundation for all food pyramids that
have been created and suggested in various studies (Belc et al. 2019). The demand
for food due to the world’s growing population is directly linked to an increase in the
amount of food wasted. One of the most serious environmental, economic, and
social problems is food waste, which is addressed by the UN Sustainable Develop-
ment Goals (Principato et al. 2019).
Massive amounts of waste are produced during the harvesting, transportation,
refining, storage, and distribution of food. According to Baiano (2014), cereal
processing and manufacturing generate about 12.9% of all food waste. Grain loss
and waste account for around 35% of total output in North America, Europe, and
industrialized Asia. In North America and Europe, around 10–12% of total output is
lost along the cereal food chain, but in industrialized Asia, total loss and waste
amount to up to 18% (Blakeney 2019). According to the circular economy, cereal
production and processing provide by-products and leftovers that can be turned into
new raw materials. Various techniques such as extraction, fermentation, and microbe
cultivation can be used to repurpose straws, husks, brans, flours, bread trash,
confectionery waste, and other materials for value-added commodities (Belc et al.
2019).

11.6.7 Oil Industry

Oilseed production is currently increasing due to increased demand from the food
industry and the fact that oilseed crops are being evaluated for potential biodiesel
production to replace fossil fuel supplies. Oil extraction from oilseeds leaves a
carbon and nitrogen-rich residue, causing soil depletion when dumped on the ground
without any treatment before dumping. Because of inhibitory effects on
non-pathogenic soil microorganisms, which affect bacterial and eukaryotic commu-
nity structure, this has a negative impact on soil microbial ecology, resulting in
environmental degradation. Soil microbial communities may have a direct effect on
carbon mineralization and soil quality (Lau et al. 2019). Characterization and
measurement of moisture, fat, heat of combustion, calorific value, and ash content
of the most prevalent oilseeds and residual materials in the form of cakes. It was clear
that oilseed plants and trash created from them may be a valuable source of energy
(Perez et al. 2011).
322 R. Kumar et al.

11.6.8 Tea Industry

Tea industries are one of the oldest settings across the world and a major portion of
the industries are still running on the same principles. However, in developed
countries, industrialization has significantly changed the face of tea processing and
reduced waste generation and energy loss. However, the poor nations and
non-industrially revolutionized world are still struggling with waste management
and energy loss during tea processing (Oirere et al. 2004). Oirere et al. (2004)
reported that tea industries do a total solid waste of 0.01% of production amount
on the day, which cannot be recovered and further utilized as a mainstream product.
Dust, bits and pieces of twigs, broken leaf fragments, floor sweepings, stalks, and
residual detritus that does not fit the criteria or processes that result in it being part of
the packed final product are examples of additional waste created by the same
business.
Tea waste has a variety of properties that can be used to generate revenue. Waste
is not always useless. The inventions in recovery, reuse, and waste management can
take advantage of the tea bush’s and leaf’s chemical and molecular diversity such as
polyphenols, antioxidants, catechins, flavanols, cellulose, amino acids, insoluble
proteins, caffeine, fibre, sugars, lignin, zinc, and tannic acid, which give tea its
rich flavours and textures, are retained in waste for economic gain (Oirere et al. 2004;
Waste and Processing 2020). Caffeine, a nutrient contained in waste, has a large
demand and significant export potential. It is extracted for use in cosmetics, fertil-
izers, instant teas, medical and dietary supplements, among other things. After the
tannic acid that interferes with protein digestion is removed, tea waste makes a high
protein cattle feed (Morikawa and Saigusa 2011; Bernstad Saraiva Schott and
Andersson 2015).

11.7 Smart Technologies for Waste Minimization

in the Food Industry

11.7.1 Industrial Internet of Things

The advent of the Internet of Things (IoT) has changed the face of manufacturing
industries, including food industries. Some of the most awaited developments have
made the processes more efficient and manageable from remote locations. Previ-
ously, technological development shaped the future of following industries and
management such as sustainable energy and environment, smart city technology,
ambient assisted living systems, and transportation and low carbon products (Azmir
et al. 2013; Nižetić et al. 2020). Moreover, these developments in IoT are one of the
pillars of the fourth industrial revolution due to its evolutionary innovations and
maximum benefits to the population. The IoT development also opens the window
and improves the field for other sections of industrial partners, such as Engineering,
11 Waste Minimization and Management in Food Industry 323

Fig. 11.2 Conceptual IoT supported framework for waste processing

Agriculture, and Medicine. However, it uses minimum of the resources and leaves
the footprint of different types of environmental pollutants. Various applications of
the same are not well explained and understood yet about implementation. Still,
there is clear evidence from the previous application that an intense research activity
must be conducted in the mainstream of the idea and, henceforth, discover the
importance of IoT technologies (Nižetić et al. 2019). The working framework of
the IoT is given in Fig. 11.2.

11.7.2 Necessities of IoT to Reduce Food Waste in the Food

Industry

The present global economic system is changing the world at a rapid pace than
anyone would have predicted before; hence, this pace must be maintained by other
industry partners as well. The global population growth rate is 1.1% every year, and
it is forecasted to reach ten billion by 2050. In the interest of the population,
production industries need to be precise and perfect to utilize the raw material in
its best possible way to maximize the productivity to meet the requirements of every
human being on this planet, out of this food security to every individual on this
planet is going to be a most difficult challenge (FAO 2020).
A significant infrastructure boost in both possible ways would be required as
manually and machinery as well. Henceforth, manual precision and perfection could
be attained by practice over time, but the machinery needs to be trained using IoT. In
324 R. Kumar et al.

the previous perspective, the widespread use of IoT and smart technologies might
play a significant role in bridging some critical infrastructural gaps in sectors. IoT
technologies are becoming increasingly important as a result of continued technical
breakthroughs and digitalization, which need a range of different electronic items to
be connected in a beneficial way (Digiesi et al. 2015). More efficient services and
adaptable procedures, in general, are required, which might be achieved with the
right use of IoT technology. IoT technologies have paved the way for a slew of
efficient services, smart networking, apps, and devices that may provide beneficial
synergies and advantages for businesses. The major advantage of IoT technologies is
their connectivity between agriculture, transportation, processing, supply chain, and
end-users, and this has enormous potential (Loius Columbus 2018).

11.7.3 IoT in Industry to Reduce Food Waste

The use of Internet of Things (IoT) technology in industrial applications will boost
production efficiency and enable more effective communication and networking
between operators and equipment. Finally, it would enable more competitive busi-
nesses on the market, with improved quality control and waste reduction (Li et al.
2020). For IoT implementation, one of the critical features would be developing,
designing, and integrating various useful sensors from production to processing,
processing to handling, handling to transportation, and transportation to retailers in
industrial applications. More rigorous and robust research efforts are needed towards
an efficient application of IoT technologies in the food industry and to better
understand how IoT technologies could be implemented in specific processing
such as high-temperature heating, low vacuum operations, freezing condition mon-
itoring during processing, and variable conditions of processing (Jagtap and
Rahimifard 2019). Advancement would be critical in terms of connecting various
unnatural condition sensors using and processing the acquired data to enable
improved industrial processes, such as ensuring that smart IoT-based Computer-
Integrated Food Manufacturing advantages are attainable.

11.7.4 IoT in Agriculture to Reduce Food Waste at Industry

In order to avoid future loss of fresh food due to a variety of circumstances, efficient
farm production coupled with industrial need via IoT is a must for our population. As
the first element, as previously said, is continual population expansion, the second is
connected to climate change difficulties if food waste is not stopped on a big scale. In
contrast, climate change impacts agricultural yields, and certain places are becoming
unsuitable for effective agriculture production. Food waste is one of the most serious
issues since it has become a global concern, particularly in industrialized nations.
11 Waste Minimization and Management in Food Industry 325

More than 28% of potentially arable land is “reserved” for food waste, yet more than
800  106 people are currently starving (FAO 2020).
In addition, it is expected that the use of IoT technology in agriculture would
assist in ensuring enough food supply and improve the efficiency of agricultural
production operations in general. Various important data about crops might be
collected and utilized for yield monitoring and early diagnosis of possible illnesses
that could drastically impact agricultural production. The monitoring of soil and
nutrients would help to optimize agricultural production processes and save water,
which is valuable in some areas and may be used through smart irrigation systems.
Furthermore, with the use of IoT, a more precise planting and fertility crop man-
agement, in general, might be assured based on previously existing data (Xin and
Tao 2020).
Due to a large number of variables and unexpected factors in agriculture, there are
several challenges with the efficient deployment of IoT technology in agriculture
production. Different sensing and monitoring technologies should be developed and
greater farmer education to cope with any disaster that is difficult to control using
IoT (Ojha et al. 2015).

11.7.5 IoT in Waste Management

Waste management in the direction of a circular economy (Fan et al. 2014) is a

critical contemporary population issue, and IoT technology may surely assist with
more efficient waste management in certain locations (Voca and Ribic 2020) and
recycling of various restricted food supplies (Kwan et al. 2018). Different IoT-based
technical solutions are now being developed to complement the smart waste man-
agement idea. Some of them are already on the market and are designed to handle
waste management more sustainably. The developed solutions are primarily focused
on smart waste bin monitoring (Dhana Shree et al. 2019), such as bin filling level
detection, waste temperature and fire detection, bin vibration occurrence and bin tilt,
presence of waste operators, waste humidity, and bin Global Positioning System
(GPS) location, and so on. IoT devices, in general, can successfully assist IoT-based
smart waste management systems. IoT technology might also be employed for smart
trash truck coordination (Idwan et al. 2020), resulting in increased efficiency for
waste utility firms and a reduction in hazardous emissions (pollutants) produced by
garbage trucks (Kozina et al. 2020). From the standpoint of smart technologies,
appropriate and IoT-based food waste management is critical (Kang et al. 2020) in
order to secure adequate raw materials to manufacture fresh food items, as previ-
ously stated. In this regard, IoT technology might be utilized to reduce food waste
through intelligent appliances and a well-developed management framework
(Liegeard and Manning 2020).
326 R. Kumar et al.

11.7.6 IoT in Transportation to Reduce Waste at Industry

The most critical part of food processing and preventing fresh produce waste is the
supply chain and transportation. As a result, transportation modes will alter dramat-
ically in the future decades (Jonkeren et al. 2019), particularly as the number of
electric automobiles on the market increases and becomes increasingly connected
with IoT devices. To enable optimal vehicle autonomy, certain vehicle technologies
require transportation infrastructure development. The internet of cars idea (Shen
et al. 2020) has just arisen, demonstrating the IoT’s promise in this vital field, IoT
could help with vehicle maintenance and failure prevention (Saki et al. 2020), which
would increase vehicle protection and lifespan. Taking all into account, IoT inno-
vations have the potential to fully transform the transport experience of raw and
processed food sector and enhance the efficiency of transportation networks in a
variety of ways by real-time information of the products on the way (Fisher et al.
2018).

11.8 Cloud Manufacturing Based Smart Waste

Management

11.8.1 Cloud Manufacturing Definition and Its Need

for Food Industry

A broad range of industrial settings such as food and drink, pharmaceuticals,

automobile, chemical industry, electronic devices manufacturing, and aerospace is
putting a lot of pressure on sustainable management of resources and create mini-
mum waste to protect future from scarcity of such raw materials (Fisher et al. 2018).
To meet the challenges of circular economy over linear economy, it is evident to use
some new advanced manufacturing models with the idea of collaboration, more
advanced automation, and sharing of knowledge and data across the supply chain of
the products. This advanced manufacturing model could enhance the process’s
customization, resource efficiency, and flexibility (Fisher et al. 2018; Nižetić et al.
2020). Recently, one of such platforms launched for such industrial manufacturing is
called cloud manufacturing (CM). This enables the manufacturers to make intelli-
gent decisions to select the most sustainable and robust manufacturing process they
can opt for based on the data available in the cloud about the requirements and raw
materials available. Moreover, CM has the ability to integrate with industrial IoT
(IIoT) and big data analytics in real-time (Loius Columbus 2018). In addition, it has
the potential to work for different industrial revolutions to take advantage of
on-demand access to a collective pool of manufacturing capital to create temporary,
reconfigurable supply chains with improved performance, lower production costs,
and efficient resource allocation. The implementation of such advanced manufactur-
ing models has been hampered before by limitations in computer processing
11 Waste Minimization and Management in Food Industry 327

capacity, data collection, data analytics, and security solutions (Kakkavas et al.
2020; Schiefer 2004).

11.8.2 Implementation of Cloud Manufacturing

It is necessary to identify the cloud users and their functions in order to comprehend
how the CM architecture will be implemented. Although the exact nomenclature of
each position varies, there are three generally specified roles (Ren et al. 2015). The
cloud client, cloud provider, and cloud operator are the three types of cloud users.
The obvious positions of cloud users and cloud providers are that one uses cloud
services for manufacturing and the other provides these services. The cloud operator
oversees the use, performance, and distribution of cloud services and the relationship
between cloud providers and customers (Singh et al. 2015). This position is often
split to include a fourth cloud user known as a cloud carrier, who serves as an
intermediary by providing connectivity and transport to allow customers and pro-
viders to exchange services. While it has been noted that a single consumer can
perform two or more of these functions, the consequences for manufacturing clouds
and their business models have only recently been investigated (Wang et al. 2008;
Huang et al. 2013; Fisher et al. 2018).
The manufacturing cloud implementation model determines the method by which
users interact. The manufacturing cloud can be deployed in four ways: private,
public, community, and composite (Xu 2012). A private cloud will house the data
and information of a single-tenant and offer services tailored to that company. With
the exception of hacking, being private within an enterprise allows for data protec-
tion (Xin and Tao 2020). Multiple tenants with a shared interest may share such
information or resources through the community cloud for mutual benefit.
Manufacturing tools, skills, and data are shared and maintained by a third party on
the public cloud. It is the portal from which tenants from different backgrounds will
be able to communicate with one another. Multiple external and internal clouds
make up composite or mixed clouds (Wu et al. 2013; Fisher et al. 2018).
There have been several proposed architectures for CM implementation since its
inception in 2010. Two related preliminary proposals are based on a layer hierarchal
system for a future CM architecture structure. The problem of defining and
virtualizing manufacturing tools and capabilities is present in both of these pro-
posals. These are encapsulated as resources in the cloud, which are operated by a
unified intelligent network. They are linked using IIoT technologies to provide real-
time control of these manufacturing services (Yang et al. 2016). The bottom layer of
the layered system is made up of this. The management of cloud resources is the
focus of the middle segment. It serves as the system’s backbone, performing tasks
such as scheduling, matching users with suppliers, quality control of facilities,
remote monitoring of resources, fee estimation, assessment, and process optimiza-
tion, among others. The framework’s higher layers are the application layers, which
serve as a gateway for cloud users to interact with the system. The cloud users may
328 R. Kumar et al.

communicate their specific requirements and make requests for different

manufacturing cloud services. Awareness is spread across the entire infrastructure,
and cloud protection is consistent and reliable across all proposed frameworks (Guo
2016).

11.8.3 Manufacturing on a Sustainable Journey with Cloud

Manufacturing

Sustainable manufacturing is characterized as producing manufactured goods that

are non-polluting, conserve energy and natural resources, and are both economically
and environmentally sound for workers, communities, and target groups (Schiefer
2004). A complex relationship exists between environmental responsibility, eco-
nomic growth, and social welfare in sustainable manufacturing. A combination of
these three separate and co-existing pillars of sustainability (environmental, eco-
nomic, and social) is needed to make more sustainable manufacturing decisions
(Giret et al. 2015). Servicification through distributed cloud-based manufacturing
systems (DCMS) is one trend in sustainable manufacturing. Owing to the rapid
growth and convergence of information technology in manufacturing, DCMS has
resulted in a paradigm change. Via mass customization and product tailorability,
actively engaging consumers in product growth, regional value creation, reduced
transportation and supply chain operations, lean production (e.g., removal of ware-
houses and inventory storage), and reduced product cost and distribution lead time,
the DCMS service industry will assist in the realization of sustainable manufacturing
(Nagarajan et al. 2018).
To take advantage of these smart technologies, sustainability assessment
approaches and techniques must be able to characterize dynamic parameters that
affect the complexity of the DCMS network in a long-term/adaptable manner. These
approaches and tools aid industrial decision-making by offering information on the
advantages and impacts of sustainability (Rauch et al. 2015). Few attempts have
been made in this direction, and existing structures are of a qualitative type.
Quantitative evaluation methods for distributed manufacturing are currently
constrained in their ability to characterize the three pillars of sustainability at the
same time. Engineering augmentation of collaboration is becoming an important
means for successful design, optimization, and control of future factories as con-
ventional centralized manufacturing structures and processes are transformed into
highly decentralized, intelligent, and autonomous networks of collaborative services
(Moghaddam et al. 2015).
11 Waste Minimization and Management in Food Industry 329

11.8.4 Valorization of Waste by Cloud Manufacturing

As CM is the collection and sharing of information available at one industry to others

to make intelligent, sustainable decisions. The CM-based system could play an
important role in waste valorization by providing smart suggestions about better
economic reuse of the by-products or unused parts of the food products (Jonkeren
et al. 2019). For example, solid waste pomace was generated in the manufacture of
olive oil. Previously, pomace had traditionally been used to make pomace–olive oil
and pomace wood. Still, due to the suggestions made by CM, it can now be
processed into a variety of higher-value-added products such as biomolecules,
dyes, and cosmetics. This transformation was attributed to the available information
in the cloud about the composition of the products and their raw materials used
(Malamis et al. 2015). In addition, CM could also suggest the best treatment for the
waste generated by the particular food industry (Fisher et al. 2018). Conversely, CM
versatile supply chains and real-time market price analysis can be used to determine
the most cost-effective waste management system and coordinate its execution. This
will be beneficial to small and medium enterprises (SMEs) who do not have their
own waste management infrastructure on-site and instead pay to have their waste
removed and handled by a third party (Fisher et al. 2018).
CM can be used to match various cloud users from food industries together
because it is a multi-tenancy platform, as shown in Fig. 11.3. Connecting and
improving communication among local food manufacturers will aid in the identifi-
cation of inefficient waste management methods and the identification of local,
sustainable alternatives for food waste. For example, CM can identify a
non-renewable source of feed for a process by analysing waste from another local
manufacturer. Since it is higher up the waste management ladder, this will be the
preferred treatment choice (Guo 2016). Cloud manufacturing can also find appro-
priate transportation routes and the link between food suppliers and customers. It has
the ability to make use of unused transportation power, increasing the transporter’s
economic return and lowering CO2 emissions from food manufacturing units. Cloud
manufacturing could endorse a food business model similar to Uber, where multiple
customers share a single vehicle on the same journey path. This is an extension of
CM food supply chain scheduling optimization service (Fisher et al. 2018).

11.8.5 Waste Minimization in Food Industry Using Cloud

Manufacturing

Waste reduction by lean manufacturing is a top priority because it can lower costs
and increase profit margins. Many companies are turning to CM to help them cut
down on production waste. There are various approaches by which CM is helping to
reduce food waste in food industries, such as early detection of defects, managing
330 R. Kumar et al.

Cloud Operator

Operation mechanisms Benefits from creating values

and rules for provider and customer

Cloud manufacturing plant

Service provision Customized jobs Competitive Customized

and benifits service on demand
demand

Cloud Provider Cloud Customer

Fig. 11.3 Crowdsourcing via cloud platform in social manufacturing (Ren et al. 2015)

waiting times for sample approval, transportation and minimizing excess production
(Schiefer 2004; Fisher et al. 2018; Jagtap and Rahimifard 2019).
In every manufacturing phase, there will always be faulty goods. An integrated
CM can help identify, control, and track defects by storing all production data. CM
will alert you of any failures or malfunctions on the production lines because of its
ability to track them. Until supplies join the manufacturing line, quality tests of raw
materials may be implemented. Although eliminating faulty goods can be challeng-
ing, CM may help reduce the degree of harm, saving both money and time.
Transportation and logistics are two areas where precious production time can be
wasted without even realizing it. One may also be using unnecessarily more inter-
mediaries in your business deliveries. CM can automate much of the operations
involved in shipping and distribution if you have a well-mapped CM system. CM
can also improve the supply chain’s productivity, monitor the success of logistics,
and avoid wastage (Buckholtz et al. 2015; Mannan et al. 2016).
11 Waste Minimization and Management in Food Industry 331

Food manufacturers can produce excess products that exceed warehouse storage
capacity and market demand. Inventory overstocks, ineffective supply chain man-
agement, overuse of production lines, and a lack of reliable forecasting are all
common causes of overproduction. It can be difficult to deal with, particularly
when perishable goods are involved. Adopting CM will help you avoid these
problems by giving you the resources you need for effective forecasting and demand
planning. It can use historical data to reliably forecast planned sales, guiding the
production plan and, as a result, reduce waste. With CM, you can be sure that output,
demand, and storage are all in sync (Canali et al. 2014; Garcia-Garcia et al. 2019).
During production processes, the movement of people and machinery can result
in wear, accidents, losses, and stress. Poor workstation layout and incorrect produc-
tion line configuration will result in movement-induced wastes. Food companies can
prevent such wastes by designing factory and warehouse layouts with modern and
efficient CM (Villarejo et al. 2014). For example, based on the demand for finished
goods, informative data gathered on a cloud system can be used to decide which
machines are used the most and what components should be stored where. CM
solutions can aid in reducing excessive movement, the better distribution of work-
load among machines, and the creation of a better working atmosphere for
employees. CM can assist companies in reducing or eliminating waste in processes
that could have been limiting production efficiency without their knowledge. If left
unchecked, waste or inefficiencies can quickly build up to devastating and damaging
levels within an industry, no matter how minor they seem at first. In terms of long-
term profitability, implementing effective CM can be extremely beneficial (Fisher
et al. 2018; Nižetić et al. 2019).

11.9 Data Big Data Analytics in Food Waste Management

The advancement of sensors has resulted in an ever-increasing volume of physical

data being collected from production lines. Since these “big data” contain a wealth of
knowledge relevant to machines and processes, figuring out how to effectively and
efficiently discover trends in the data to improve efficiency and economy has
become both a challenge and an opportunity to foster food industry productivity
and simultaneously reduce food waste (Gao et al. 2020). The implementation of the
big data and framework is shown in Fig. 11.4.

11.9.1 What Is Data and Big Data?

“Transmissible and storable machine information” (Gao et al. 2020) is the first
modern use of the word “data”. The definition of data has evolved to “information
output by a sensing system or organ that contains both useful and irrelevant or
redundant information and must be processed to be meaningful” as data has
332 R. Kumar et al.

Fig. 11.4 Implementation of big data and framework (Gao et al. 2020)

increasingly permeated all facets of modern society. Data has evolved from a passive
information carrier to an active value enabler, as shown by this change.
The term “big data” refers to data that is large in volume or variety, or that is
collected at a high rate with potentially high or low veracity, and that requires
increasingly specialized analytical technology to turn it into useful knowledge
(De Mauro et al. 2016). Computers, the Internet, sensors, mobile devices, and
smartphones have revolutionized the way data is produced, processed, distributed,
and stored. In terms of data volume, there were approximately 3 exabytes (3  1018
bytes) of data in 1986, but over 300 exabytes of data were stored by 2011. The rate at
which data is produced and collected has accelerated dramatically in recent years
(Fisher et al. 1998). Hence, big-data-driven analytics could help food companies
make better decisions in areas like pricing, product promotion, product creation, and
demand forecasting. Enhanced product innovation, higher sales effectiveness,
increased margins and profitability levels, extended consumer reach, and improved
marketing are just a few advantages.

11.9.2 Data as Manufacturing By-Product

The volume of data was little, the quality was uneven, and the value associated with
it was insufficient to assist manufacturing process improvement when data was
recorded manually (Gao et al. 2020; Saki et al. 2020). The availability of large
amounts of high-quality, high-value data has fundamentally shifted the role of data,
making it an inseparable coproduct of current manufacturing. As digital sensors have
increasingly replaced manual data recording and sensor-rich machines have become
ubiquitous on factory floors, the availability of large amounts of high-quality,
11 Waste Minimization and Management in Food Industry 333

high-value data has fundamentally shifted the role of data, making it an inseparable
coproduct of current manufacturing. The amount of data generated from industrial
production has an event of sequence known as the industrial revolution. Because of
the invention of the steam engine, the first industrial revolution dramatically
increased manufacturing capacity, but it had little effect on data collection and use.
The second industrial revolution has illustrated the importance of managing output
quality as demand for mass production grows (Hyun Park et al. 2017; Choi et al.
2019). Consequently, studies of statistical analysis and correlation began to control
the quality. Subsequently, the development of analysis techniques took place, such
as statistical process monitoring and response surface methodology. The association
between product design and processing parameters was analysed (Kwan et al. 2018).
Later, with the adoption and proliferation of computers and sensors, the third
industrial revolution saw a transition from manual manufacturing to digital
technology-enabled automation. For process monitoring and fault detection, a
wide range of sensors and system controllers have been used (Kurada and Bradley
1997; Kozjek et al. 2017). Thus, manufacturing data has diversified from single
quality metrics to a mix of data from transactions, simulations, scheduling, distribu-
tion, and maintenance, all of which have considerable potential as sources of new
information generation (Gao et al. 2020).
As the demand for quality, versatility, and productivity in manufacturing rises
and new paradigms emerge, such as mass personalization (Ren et al. 2017), a better
understanding of production machines and processes as part of the cyber-physical
systems (CPS) paradigm has emerged as a central topic of the fourth industrial
revolution. This definition lays out the vision for future “smart factories”, which will
be defined by the timely acquisition, distribution, and utilization of data from
machines and processes on manufacturing shop floors, with big data analytics
playing a key role in dynamically linking all operations within the production
lines and trying to retrieve features from the dataset to allow in-depth analysis
(Gao et al. 2020).

11.9.3 Data Analytics for Waste Minimization

Food waste costs are rising, and legislation aimed at decreasing them is encouraging
food manufacturers to employ big data to battle the problem. By analysing waste
streams and calculating an “optimum inventory level”, inaccuracies in projecting
increased customer demand, which can result in large mounds of lost food, can be
minimized. According to the analytics, aids in addressing business challenges,
evaluating performance indicators, and establishing best practices across the firm,
increasing efficiency, and generating profitability (Girotto et al. 2015).
According to the research, data on retailers and locations that create the most trash
should be gathered, as this raises expenses for businesses and breaches food waste
regulations. The information gathered can then be utilized to alter supplies to these
sites. Continuous monitoring and analysis of this data, while taking into account
334 R. Kumar et al.

fluctuating needs and seasonal trends, might aid a company’s waste management
problems. Manufacturers can establish an “optimum inventory level” by analysing
sales data, weather predictions, and seasonal trends, which they can then employ to
limit the consequences of food waste (Henningsson et al. 2004).
Consumer demand predictions may then be generated at certain periods, and
promotional programs and sales methods can be built around sell-by and expiration
dates. Again, the goal is to reduce food waste and the resulting environmental and
financial consequences. Then, using data-driven supply chain and sales analytics,
actionable insights may be generated, resulting in less food waste. This may be quite
beneficial for firms dealing with regulations since it allows them to decrease
expenses while also reducing harmful environmental consequences (Gao et al.
2020).

11.10 Case Studies Related to Food Industries

Jagtap and Rahimifard (2019) studied the implementation of IoT in the ready-to-
meal food industry to real-time tracking of the food waste (FW) and notified the
stakeholders in the food supply chain in real-time as well. The IoT-based FW
tracking system was equipped with features such as identifying the type of waste,
reasons for its generation and real-time monitoring of the FW amount. The devel-
opment of an IoT-based system architecture with four layers (sensing, network,
operation, and application) had been completed (Jagtap and Rahimifard 2019). The
development of a digital food waste monitoring and tracking system necessitates
selecting appropriate hardware solutions that minimize human interference in data
collection and a software application to store, analyse, and communicate the col-
lected data to key decision-makers.
This digitized FW tracking system starts with an intelligent scale that measures
the weight of the food waste before feeding it into a custom software program. The
next step is to determine the waste form and categorize it. The unique approach
suggests using a human interface to check food waste information. This information
is then stored in a database using cloud storage and then analysed using the waste
tracker framework to provide key decision-makers with real-time information on
food waste created through a user-friendly food waste tracker dashboard. Since all
working staff had detailed information on FW created, this real-time digitized
system and it was one of the major factors in FW reduction through behavioural
changes among staff (Jagtap and Rahimifard 2019; Nižetić et al. 2020).
As an FW Tracker system was installed in the production area, all FW related to
Chicken Tikka Masala was reported from all departments. On the touchscreen, FW
weights were shown, and workers used it to record the form of waste and the reason
for its disposal. The software programme automatically registered the date, time, and
financial value in the context. The FW recording process took less than 4.5 min per
employee per week. The factory management did not need to hire any dedicated staff
11 Waste Minimization and Management in Food Industry 335

to measure FW, and this monitoring procedure could have lowered overall labour
costs by reducing waste and overproduction (Jagtap and Rahimifard 2019).
The FW for the Chicken Tikka Masala line was reduced by 60.7% compared to
before the IoT-based digitized FW tracking system was implemented. The Chicken
Tikka Masala line provided approximately 1400 kg of FW per week on average
before the IoT-based digitized FW tracking system was implemented (12,000 meals
produced per day on average). Trim waste accounted for approximately 51% of
waste, with quality issues (16%) and equipment failure accounting for the remaining
14%. The conventional paper-based method for documenting FW was time-
consuming, inefficient, and often inaccurate. The industry built and implemented a
real-time digitized IoT-based FW tracking device to address these problems. This
device was designed after an extensive study into both hardware and software
aspects. To measure the weight of FW, the hardware part consisted of a weighing
scale connected to a touchscreen. The program included an application that allowed
employees to confirm the form and cause of waste. The software’s other component
allowed for real-time FW data visualization, warnings, and detailed analysis. When
compared to the previous year’s figures, the company saved about £306,873 on
FW. In today’s world of razor-thin profit margins, these savings represent a signif-
icant financial benefit for the plant. Factory management was able to take prompt
steps to change waste practices and reduce FW by providing real-time digitized data
on FW status and observing patterns (Jagtap and Rahimifard 2019).
In another study, Short et al. (2014) studies revealed that optimum use of the
by-products from industry could foster economy and sustainability by using CM. It
was revealed that British Sugar produces 11 co-products from waste, ranging from
topsoil and animal feed to bioethanol and tomatoes, by reusing, recycling, and
recovering waste suggested by the CM technique. The Wissington Factory of British
Sugar has become one of the most productive sugar factories in Europe due to this
comprehensive waste valorization. It is an excellent example of the advantages of
waste valorization in process manufacturing.
There are several wastes valorizations routes available for sugar industry waste.
As previously mentioned, one of the main characteristics of CM is the gathering and
sharing of data and experience information, which assists CM in making intelligent,
long-term decisions. From this data, cloud manufacturing was used to determine the
most sustainable waste valorization process to develop topsoil as the fertilizer,
animal feed according to the composition needed to meet the dietary requirements
of cattle, and production of bioethanol with remaining waste (Fisher et al. 2018).
Singh et al. (2015) investigated the reduction of carbon footprint generated during
the transportation of the beef throughout the supply chain using cloud-based tech-
nology. During analysis and investigation, the conclusion drawn was that the
communication gap between all the stakeholders during the processing and supply
chain is the major culprit for the increased carbon footprint in the supply chain of
beef. Henceforth, it was realized that a framework is needed to assist all stakeholders
in reducing their carbon footprint and to make this knowledge available to all
stakeholders. Consequently, the stakeholders adopted a cloud-based solution to
achieve the target carbon footprint during the supply chain of the beef. Then, the
336 R. Kumar et al.

service provider will select the most effective, precise, and user-friendly carbon
calculator for all the stakeholders of the beef supply chain and upload it on the
private cloud.
The developed interface will take the information from every stakeholder at their
point of involvement and suggest the most sustainable approach for reducing the
carbon footprint. For example, for farmers, the software interface will generate
information about breed and food to reduce the production of carbon footprint.
The real-time relative data analysis will also be available for the farmer to compare
the emission with previous and current feed systems. Both stakeholders will be able
to see the information entered by the farmers and the results obtained at the farmer
level through the private cloud. This data can be put to good use for other stake-
holders to minimize their own carbon footprint by reducing the impact of contingent
variables or carbon hotspots. For instance, logistics companies will determine
whether any delays or inefficiencies in their operations are causing excessive carbon
emissions at the farms. They will work with farmers to solve the issue. Similarly, this
cloud-based technology can help the farm and abattoir and processor or between
abattoir and processor and the retailer to reduce their carbon footprint as well. This
conducted study and training for all stakeholders helped reduce the carbon footprint
significantly and bridged the gap between them (Moghaddam et al. 2015; Singh et al.
2015).

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Chapter 12
Co-Product Recovery in Food Processing

Abhay Tiwari , Garima Singh, Kanika Chowdhary, Gaurav Choudhir,

Vasudha Sharma, Satyawati Sharma, and Rupesh K. Srivastava

Abstract The inexorable increase in population growth and drastic changes in

lifestyles have scaled up the burden of managing food waste generated from various
industrial, agricultural, and residential sectors of the society. The Food and Agricul-
ture Organization (FAO, Constr Build Mater 184:258–268, 2011) reported that
about one-third of the food ends up either getting wasted or lost from the human
supply chain. This report had shed light on the ecological, environmental, and
economical negative effects the present situation holds. The challenging magnitude
of food waste management needs to be addressed globally at all levels of food
production. Food waste is rich in organic compounds, thus traditional approaches of
landfilling and incineration should be eliminated as much as possible and must be the
least adopted option. Alternatively, the research work that aims towards food
recovery from by-products and valorization of food waste must be promoted.
Therefore, employing an array of sustainable and eco-friendly strategies/technolo-
gies (enzyme-assisted extraction, solid-liquid extraction, pulsed electric field extrac-
tion, supercritical fluid extraction, amongst others) for appropriate reuse of food
waste must be channelized to generate co-products and value-added products from
food waste. The present chapter elaborates on the food waste generated from
different sectors and subsequently synergistic integration of various approaches for
maximizing co-product recovery of valuable bioactive compounds. It describes in
detail methods and procedures which have been successfully evaluated at the
lab-scale and require a necessary push for scaling them up for boosting the circular
economy for a better future.

A. Tiwari (*) · G. Singh · K. Chowdhary · G. Choudhir · S. Sharma

Centre for Rural Development and Technology, Indian Institute of Technology (IIT) Delhi,
New Delhi, India
V. Sharma
Department of Food Technology, Jamia Hamdard, New Delhi, India
R. K. Srivastava
Department of Biotechnology, All India Institute of Medical Sciences (AIIMS), New Delhi,
India

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 341
S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies,
https://doi.org/10.1007/978-981-19-1746-2_12
342 A. Tiwari et al.

Keywords Co-product recovery · Sustainable food packaging · Food waste

valorization · Bio-adsorption by food wastes · Technologies for co-product recovery

12.1 Introduction

FAO in 2013 described food waste as the part of food that was gathered/produced for
human consumption but it remained unutilized. However, food loss refers to the
portion of food which was initially meant for human consumption but got disposed
of along the entire food supply chain (FAO 2013). The US centered EPA agency
proposed a priority-oriented framework called “food recovery hierarchy” to prevent
and divert food wastage according to appropriate social, economic, and environ-
mental norms (Fig. 12.1a). Nevertheless, as per an estimate, about 1.4 Gt of food
suitable for human consumption is wasted annually at worldwide level. In India this
figure has been stated to be 40% of the food produced (Chauhan 2020). Industrial-
ization and ensuring progress in living standards has led to an increase in intake of
fruit and vegetables, and therefore creating a higher proportion of organic waste,
thereby generating tremendous food waste from various sources (Table 12.1 and
Fig. 12.1b). Several measures are considered to reduce the food waste to the least
possibility (Fig. 12.1c).

Fig. 12.1 (a) Food recovery hierarchy; (b) Sources of food waste; (c) Ways to minimize food waste
and (d) Water utilization in food industry
12 Co-Product Recovery in Food Processing 343

Table 12.1 Different kinds of food wastes generated from food industry and their origin
S. No. Food wastes Origins Reference
1 Waste generated from Slaughterhouses, butcher shops, egg and Jayathilakan et al.
processing of animal- fish processing plants (2012)
based food
2 Waste from processing Fruit and vegetable processing plant; oil Sagar et al.
of fruits and vegetables mills; manufacturer of cocoa, tea, coffee, (2018)
and other canned foods
3 Sugar manufacture Sugar manufacturers Partha and
waste Sivasubramanian
(2006)
4 Dairy manufactory Dairies Rad and Lewis
waste (2014)
5 Wastes from bakery Bakeries, confectionery and candy Lam et al. (2014)
production producers
6 Wastes from both Wineries, breweries, distilleries, fruit Okonko et al.
liquor and non-liquor juices, and other beverage (2009)
production units

World water demand is predicted to rise by 20–30% till 2050. The burden on the
world’s water resources is ever-growing. Out of this, developing countries will have
90% of the total share. Agriculture-related activities consume ~70% of global
freshwater. In the past three decades food production has amplified by >100%.
The other estimate of FAO stated that nearly 60% more food will be required in three
decades to meet the food consumption for the mounting global population (FAO
2017). The food processing industry is one of the most water-intensive industries
(Fig. 12.1d). Most water-intensive sectors are (a) dairy and poultry industries and
(b) the fruit and vegetable processing units (Mekonnen and Gerbens-Leenes 2020;
Alexandratos and Bruinsma 2012). The water footprint of an animal-based food
product is greater than the water footprint of any plant-based product having similar
nutritional value. For instance, it is estimated that animal food production is up to
20 times more water than for vegetables or fruits (Bhagwat 2019). To decrease water
minimization in the food sector it has been suggested to utilize recycled water and
replace water-consuming processes with efficient technologies (Nemati-Amirkolaii
et al. 2019).
It is noted that a typical Indian household contains an abundance of kitchen-
released waste as a wet waste having less calorific, higher moisture and organic
matter. The water content of FW generally ranges from 70% to 85% (Tsang et al.
2019). A recent study assessed that an Indian household produces about 1 kg of wet
waste per day, and this quantity is expected to increase up to 125 million tons by
2031. About 3/fourth of wet waste ends up in landfills and incineration factories.
Because of the scarcity of available technique, a newer technology called
biomethanation has been developed to address the problem. In this technology,
organic matter is converted into biogas (methane and carbon dioxide) by microbes
under anaerobic conditions. Briefly, wet waste is turned into a slurry which is fed
into the anaerobic digestion pit. The methane gas produced under pressure is then
344 A. Tiwari et al.

transformed into electricity (AC) which is then stored in high energy batteries.
Further, the leftover decomposed matter from the pit could be suitably utilized as
biofertilizer in horticultural and agricultural areas (Sahithi Reddy et al. 2021).
Integrated biorefinery holds the possibility of valorizing food wastes into numer-
ous valuable bioactive molecules and energy. This has been proven unprecedented
as a highly economical, sustainable and eco-friendly alternative (Chowdhary et al.
2018; Chowdhary and Kaushik 2019). Further, this systematic approach generates
reasonable employment for rural communities having least environmental impact
(Isah and Ozbay 2020). Inedible food components produced at any stage of food
manufacturing process in the EU ~ 30 Mt and this number is projected to upsurge up
to $4.1 trillion by 2024. Food waste has a high potential to be used as feedstock in
biorefineries to produce value-added products and chemicals. However, this process
has its own limitations. Some food wastes can deteriorate very quickly if left unused.
For instance, fresh seafood can succumb to oxidation and microbial contamination
impacting its possible use and transformation (Martínez-Alvarez et al. 2015;
Chowdhary et al. 2018).
The entire food business presently utilizes ~30% of the world’s total energy.
Countries with higher GDP consume more portion in processing and transport
operations while in low-GDP countries, cooking consumes the highest share.
Refrigerated storage of food products accounts for up to 10% of the total food
supply carbon footprint (Cleland 2010). Bulk preservation and the usage of passive
evaporative-cooling technologies are few of the possible solutions. Likewise, eco-
nomically viable stand-alone solar chillers are another option (Day 2011).
Food packaging has a vital role to play in containing, protecting, and extending
shelf life of food before it reaches the consumer. It is an instrument for conveniently
transporting food along the supply chain to the end customer (Fig. 12.2). The highest
utility of plastic is in packaging industries. Consumers must learn to redirect
materials soiled with food residues to a recycling infrastructure (Kale et al. 2007).

Fig. 12.2 Sustainable food

packaging
12 Co-Product Recovery in Food Processing 345

Fig. 12.3 Mechanisms of bio-adsorption by food wastes

Synthesis of bioplastics from FW is a sustainable process and these subsequent

bioplastics are biodegradable and compostable in the long run (Caldeira et al. 2020;
Singh et al. 2021a).
The fish feed has about 50% share in the aquaculture industry. The utilization of
food wastes to synthesize fish feed is a realistic solution. A recent study found fish
fed with food waste-based diets much safer for human consumption in comparison
with those fed the commercial diets (Wong et al. 2016). In addition, food waste
comprises components having high biosorption capacity, i.e., cellulose, starch,
lipids, lignin, hemicellulose, proteins, and other hydrocarbons. These functional
groups accelerate metal complexation leading to removal of heavy metals.
Bio-adsorption is a resourceful, ecological and low-priced substitute technology
over the conventional methods such as membrane filtration, ion exchange, and
chemical precipitation for the removal of toxic metal ions (Fig. 12.3). The presence
of multiple metal-binding functional groups has made agro-wastes and food wastes
as the prospective biosorbents (Ahmad and Zaidi 2020).

12.2 Potential Strategies for Eliminating or Reducing

Food Waste

The industrialization has provided an unprecedented upthrust to the world economy

on a large scale. However, the tremendous number of pollutants generated by the
industries is an area of paramount concern. Of all industrial sectors, the food industry
contributes highly to the generation of pollutants referred to commonly as
by-products due to the high water consumption and high release of effluents per
production unit. Nevertheless, it is imperative to develop effective routes to valorize
these by-products playing a significant role in contribution towards bioeconomy.
346 A. Tiwari et al.

Bioeconomy provides an integrated plan that suitably re-introduces the by-products

back into the production process for obtaining newer products having high health
benefits through sustainable methods to extract various nutritional components. The
unified management of these aggregated food wastes will help in the reduction of the
environmental deterioration caused due to traditional methods still being in practice
as open burning, dumping, thus also meeting the landfilling directives. With this in
picture, the section briefly discusses the food by-products generated in different
sectors and its potential utilization.

12.2.1 Waste Generated from Preparation and Processing

of Animal-Based Food Product

The highest demanding sector in the food industry is dairy. Dairy products such as
milk, milk powder, butter, and cheese generate solid and liquid wastes (Jaganmai
and Jinka 2017; Ahmad et al. 2019). Nevertheless, speedy industrial growth also
leads to toxic effluents detrimental to land, air, and water, thus impacting the
environment and human health.
A waste generation of 4–11 million is estimated yearly in the dairy sector. This,
mainly in the form of dairy wastewater affects the dissolved oxygen content.
Additionally, it contains lactose, nutrients, sulfates, fats, chlorides, along with
soluble organic components. However, harnessing its potential to effectively utilize
this waste as a raw material for producing other industrial products and energy utility
holds potential gains as enlisted in Table 12.2.

12.2.2 Waste Generated from Meat and Fish Processing

Industries

Animal slaughtering leads to the production of animal by-products. They may be

defined as entire bodies or animal parts, products of animal origin, or other products
obtained from animals, which can be but are not intended for direct human con-
sumption. Approximately 40% of the bovine live product and 30% of the porcine
live weight is generated as waste. These wastes, particularly blood, plasma, hydro-
lyzates, and collagen hold significance as they inherit the integrated inability to serve
as a natural preservative. These wastes being an inevitable part of the slaughter-
house, can serve many vital roles if utilized wisely, thus valorizing the waste
by-product to serve economically, thus enhancing the food production
utility (Przybylski et al. 2020).
Over the last 50 years, global fish consumption has almost doubled. Many factors
contribute to expanding worldwide fish consumption, including rising population,
rising affluence, urbanization, an increasing number of fishing enterprises, and new
12 Co-Product Recovery in Food Processing 347

Table 12.2 By-products from dairy wastes and their potential utility
Food
by-product Processing Product Reference
Whey Fermented using lactose fermenting Whey-derived Ahmad
bacteria products et al. (2019)
Wastewater Acutodesmus dimorphus cultivation Biomass converted to Chokshi
Whey Geotrichum candidum cultivated on the bioethanol and et al. (2016)
combination of oil press water and biodiesel
whey
Dairy sludge Growth medium for rhizobium Biofertilizers Pandian
et al. (2010)
Whey Kluyveromyces fragilis fermentation Biofuels Senthilraja
et al. (2011)
Wastewater Chlorella pyrenoidosa cultivation Biomass and biofuels Lu et al.
(2015)
Whey Cultivation with Propionibacterium Propionic acid Ahmad
shermanii Citric acid et al. (2019)
Actinobacillus succinogenes Succinic acid
Aspergillus niger
Yogurt pro- Lactobacillus casei Lactic acid Alonso
duction waste Fermentation et al. (2010)
Wastewater Cultivation with Enzyme lipase Spalvins
Aspergillus niger et al. (2018)
Pseudomonas sp.
Streptomyces sp.
Whey Lactose fermenting microorganisms Single cell protein Spalvins
cultivation et al. (2018)
Wastewater Candida bombicola cultivation Biosurfactants Jaganmai
and Jinka
(2017)
Whey (perme- Cultivation with Polysaccharides Spalvins
ate, Xanthomonas campestris (xanthan gum et al. (2018)
deproteinized) Streptomyces thermophilus exopolysaccharides)
Whey Latex from Maclura prolifera Bioactive peptides Corrons
et al. (2012)
Wastewater Microalgae Acutodesmus dimorphus Biofuel Hamawand
Cultivation et al. (2016)
Dairy waste Anaerobic digestion and acidogenic Bioenergy Chandra
fermentation et al. (2018)
Fatty waste Anaerobic digestion Biomethane Chokshi
et al. (2016)

and more modern ways of distributing processed frozen fish worldwide. As a result,
a significant amount of nutrient-rich fish waste is disposed of on the ground and
ocean every year. Against this background, high nutritional content in fish waste as
fish skin, bones, fins, and scales holds potential application towards its transforma-
tion into value-added products (Table 12.3).
Table 12.3 Waste generated from meat and fish processing industries
348

Animal waste By-products Properties Applications Reference

Fish waste Protein hydrolyzates Anti-oxidative functions by scav- Pharmaceuticals, diagnostic media, cosmetics, recom- Araujo et al. (2021),
enging free radicals, binant proteins, nutrition, food industries, biopolymers Bhuimbar et al.
pro-inflammatory cytokines, and (2019),
anti-microbial Nam et al. (2020)
Collagen Biodegradable, emulsifier, stabi-
lizer, foaming agent,
biodegradable
Fish oil Health benefits due to presence of
omega-3-fatty acids
Hydroxyapatite Structural similarity to mammal Bone replacement and prosthetic implants in maxillo- Akram et al. (2014)
bones facial, orthopedic, and dental applications
Slaughterhouse Blood Protein source Research, pet food, aquatic food
waste Hemoglobin
Bovine thrombin Thrombin Promotes coagulation of blood, treatment of wounds
Bovine plasma Fibrinogen, thrombin, globulin, Microbiology as a media to grow probiotic bacteria Mora et al. (2019),
porcine transglutaminase Restructure meat products Bah et al. (2013)
Porcine prothrombin Prothrombin Used as a precursor in thrombin purification and
production
Porcine plasma Protease inhibitors Surimi (A form of a fish gel)
Meat Fat Triglycerides Biodiesel
processing Mechanically recov- Protein hydrolyzates Pharmaceuticals, diagnostic media, cosmetics, recom-
waste ered meat binant proteins, nutrition, food industries, biopolymers
Rendering of entire Lard, tallow Cosmetics
carcasses of animals Chemicals
Water bonding
Cross-linking
Flavor enhancement
Gelation
Foaming
A. Tiwari et al.

Emulsifier
12 Co-Product Recovery in Food Processing 349

12.2.3 Waste Generated from the Processing of Vegetable

and Fruits

Vegetables and fruits are the most utilized commodities, covering approximately
65% and 38% of the horticulture sector. This enormous production generates around
60% of waste by-products, according to the Food and Agriculture Organization of
the United Nations (Sharma et al. 2016; Osorio et al. 2021). The by-products of the
agri-food industry comprise seeds, pomace, shell, leaves, and peels; which are
generally the food waste/loss originating in the food production chain; are a rich
source of antioxidants, phenols, pigments, antiviral, antibacterial effects, etc., thus
increasing its potential utility to be utilized in different sectors as discussed briefly in
Table 12.4.

12.2.4 Waste Generated from the Processing of the Spent

Mushroom Substrate

Spent mushroom substrates (SMS) are generated as waste products post cultivation
of mushrooms. It is being anticipated that harvesting of approx. 1 kg of mushrooms
generated a lump sum of 5 kg of spent substrate. With ever-increasing demand to
meet the requirement of optimal food production, the production of mushroom is
gaining exponential growth worldwide (Singh et al. 2021b). This huge quantity of
SMS produced is disposed of casually in open air begetting several environmental
concerns. Piling-up of SMS leads to loss of basic plant nutrients, groundwater
contamination along with production of greenhouse gases such as nitrous oxide
and carbon dioxide, triggering global warming along with loss of basic plant
nutrients (Barh et al. 2018; Lopes et al. 2015). On the contrary, SMS serves as a
rich source of nutrients, enzymes, and minerals, which holds potential application in
many sectors as enlisted in Table 12.5, thereby enhancing its potential utility.

12.2.5 Wastes from Bakery Industry and Sugarcane Industry

Bakery wastes are one of the other categories of food waste generated. This generally
comprises cookies, stale bread, and cereals. This waste possesses optimal character-
istics to undergo solid and submerged state products, leading to the utilization of
these wastes to develop biodegradable polymers, chemicals, and biofuels. Wang
et al. (2009) also reported successful utilization of the bakery wastes to produce
amylolytic enzymes using A. awamori. Haque et al. (2016) proposed the utilization
of bakery waste to produce enzymes and biocolorant using Monascus purpureus.
The waste bread has also been successfully converted to nutrient-rich hydrolyzates
by Kwan et al. (2018) and as a growth medium to baker’s yeast by Benabda et al.
350 A. Tiwari et al.

Table 12.4 Waste generated from vegetable and fruits industry

By-product Properties Applications Reference
Vegetables Rich source of Dietary fibers Sagar et al. (2018)
Onion peels, potato Cellulose Wine industries
peels, cauliflowers Hemicellulose
(stems and florets), car- Pectins
rot pomace, tomato Galactose
pomace Glucose
Fruits Arabinose
Apple peel, grape pom-
ace, mango by-product,
orange peel
Seeds Unsaturated fatty Oil extraction, pasta and da Silva and Jorge
Tomato, peach, apple, acids, carotenoids, sauce preparation (2017)
grapes, mango phenolics,
phospholipids
By-products of root and Starch Bread-making industry, Osorio et al. (2021)
tuber industry of potato, fermented beverages
yam, cassava (beer), processed foods
Shells, cakes, pellets Lignocellulose, Animal feed, flour for Akanbi et al.
protein values bread, cakes, and soups (2019), Osorio
et al. (2021)
Coffee husks Rich organic Compost, biofertilizer, Sagar et al. (2018)
content biofuel
Malt bagasse Fiber, carbohy- Confectioneries, pastry, Mello and Mali
drates, proteins, biorefinery, animal feed, (2014)
lignin alcohol fermentation
De-oiled cakes gener- Proteins, carbon Animal feed, enzyme Ramachandran
ated on pre-processing production, bioethanol, et al. (2007),
of cereals and lentils mushroom cultivation, Ancuța and Sonia
production of flours, (2020)
tofu, sausages, and
cereals
Pigments
Tomato peel Lycopene, Natural coloring, Rodman and
phytoene, antioxidant Gerogiorgis (2016),
phytofluene, and Kantifedaki et al.
β-carotene (2018), Osorio
Onion leaves Quercetin, et al. (2021)
cyanidin 3-O-
glucoside
Coffee exocarp Cyanidin
3-glucoside
The by-product of mul- Cyanidin Natural coloring,
berry industry 3-glucoside additives
Peel and waste pulp of Bethany and Natural dyes. Provides Koubaa et al.
red prickly pear iso-betanin protection against (2016)
low-density lipoprotein
oxidative modifications
(continued)
12 Co-Product Recovery in Food Processing 351

Table 12.4 (continued)

By-product Properties Applications Reference
Enzymes
Pineapple peel, core, Bromelain Improves food diges- Sagar et al. (2018)
stems, pulp residue tion, softens beef
transformation
Industrial waste from Lipase Wastewater treatment Silveira et al.
palm oil, olive oil cake, Antioxidant (2016)
mango seeds Ester hydrolysis to pro-
duce detergents
Banana waste, cabbage Amylase Fruit juice, starch syrup, Sagar et al. (2018)
waste, coconut oil cake, chocolate cake, brewing
cassava waste, date industries
waste, potato peel,
orange waste
Banana waste Cellulase Liberation of aroma rich Sharma et al.
Cabbage waste and extraction of (2016), Sagar et al.
Mango peel phenolics (2018)
Kinnow waste
Banana peel, sapota Invertase Candies, jam, confec- Sharma et al.
peel, orange, tionery, and pharmaceu- (2016), Sagar et al.
pomegranate tical products (2018)
Strawberry peel, apple Pectinase Fruit juices, wine Sharma et al.
pomace, banana peel, (2016), Sagar et al.
orange peel, lemon peel (2018)
Guava waste, grape Antioxidant Cosmetics Yarovaya et al.
seeds, rambutan skin, (2021)
oat shell, grapeseed
Orange peel, pineapple Antifungal, Utility in pharmaceutical Peanparkdee and
peel, mango seed, grape antibacterial, industries as effective Iwamoto (2019),
pomace, kaffir lime immunomodulatory against various cell lines Meneguzzo et al.
leaves (colon cancer, prostate (2020)
cancer,
hepatocarcinoma),
respiratory pathogens
Citrus fruit peels Antiviral, reduce Inhibition of hepatitis B Haque and Pant
infected cells with virus, chikungunya (2020), Hu et al.
the potential to virus, human respiratory (2020), Lin et al.
produce com- syncytial virus (2017), Osorio
pounds as Feasibility against the et al. (2021)
Tangeretin treatment of Covid-19
Hesperidin
Nobiletin

(2018). Gadkari et al. (2021) demonstrated the feasibility of waste bread to produce
succinic acid. Govindaraju et al. (2021) recently reported the utilization of bakery
waste for the development of compost. These approaches provide a sustainable
solution to waste management, more readily than being dumped openly, leading to
serious environmental concerns.
352 A. Tiwari et al.

Table 12.5 Utilization of spent mushroom substrates (SMS)

Spent
mushroom
substrate By-products Properties Applications Reference
Mushrooms Enzymes Redox substrate Decolorization Phan and Sabaratnam
Pleurotus • Cellulase molecules with of dyes (2012), Lopes et al.
ostreatus • Laccase broad substrate Degradation of (2015), Hanafi et al.
Calocybe • Xylanase specificity phenols and (2018), Singh et al.
indica • Lignin perox- polyphenolic (2021b)
Lentinula idase components
edodes • α amylase Biostimulation
Flammulina • β glucosidase agent
velutipes Biofuel
Hericium production
erinaceum
Pleurotus
sajor-caju
Agaricus
bisporus
Components Polysaccharides Rich source of Animal feed
Saw dust Vitamins nutrients due to in vivo
Paddy straw Trace elements dry matter
Wheat straw digestibility
Leafy Calcium Rich source of Compost
wastes Nitrogen nutrients Biofertilizer
Ash
Phosphorous
Rich source of Adsorption Wastewater
carbon, Biosorption Treatment
enzymes, and Heavy metal
nutrients uptake
bioremediation
Silica Rich source of Plant nutrient
nutrients, silica, and disease
and minerals management
in tomato
plants
Lime Bioaugmentation Acid mine
with sulfate- drainage
reducing bacteria

Of the other industry by-products, sugarcane is also one of the other essential cash
crops where a massive quantity of by-products is generated. Sugar mills consume
excessive amounts of water in their operations. Approximately 20–30 tons of water
is required to process a ton of sugar on an average and produces two categories of
polluted water: (a) effluent from cane molasses distilleries and (b) processed water
are the two main sources of pollutants. The various wastes generated by sugarcane
production can be sustainably utilized to convert them into usable products by
providing an efficient recycling route (Bhatnagar et al. 2016).
12 Co-Product Recovery in Food Processing 353

Sugar leaves which comprise a major chunk of sugarcane by-product are mostly
burnt as their rough texture limits its utility as an animal feed. This leads to adverse
effects on humans and the environment. Sustainable utilization of this waste lies in
converting the product to charcoal which is a promising adsorbent. This economical
solution towards renewable energy development can serve as a better contribution to
the field of fossil fuels (Porol et al. 2021).
Bagasse is a highly abundant and cheaper by-product of sugar industry. This
residual biomass is fibrous that remains after cane stalks are crushed for sugar
extraction. On the other hand, the precipitate in the form of sludge slurry after
filtration is termed as filter cake or press mud cake (Bhatnagar et al. 2016). Bagasse
is a rich by-product with ideal cellulose, hemicellulose, lignin, fat, wax, and other
essential minerals. This makes it a suitable substrate for the production of bioethanol,
food additives as vanillin and xylitol, and single cell protein. Bagasse fly ash could
be used as filler material in the development of paper and in landfilling. It is also
applied for removing pollutants from water and concrete materials (Bhatnagar et al.
2016; Zareei et al. 2018; Martinez-Hernandez et al. 2018).
Press mud, another by-product generated, is used in foaming agents, cement aid,
and compost development. The rich organic and nutrient content of press mud and
its inherited ability to serve as an inert material ingredient provide a suitable
replacement to use of other costly reported materials such as coco peat, talcum
powder, etc. (Bhatnagar et al. 2016; Kumar et al. 2017). Furthermore, recent articles
have found it as suitable substrate for biocontrol agents serving as carrier in
biopesticide and biofertilizer development (Singh et al. 2021b). Additionally, the
rich sugar content (5–15%) makes it a suitable substrate for biogas production
(Bhatnagar et al. 2016). Amongst the liquid waste generated is the molasses, left
over in the crystallization process of sugar from sugarcane. This by-product contains
high amounts of fermentative sugars and hence is used in bioalcohol production
(Valderrama et al. 2020). It is plausible to conclude that sugar industry wastes should
be viewed as economic resources that can be converted into valuable products to
proceed towards a long-term waste disposal solution.

12.3 Different Technologies for Co-product Recovery

and Valorization of Food Waste

Food wastes are a potential source of various industrial and health imparting
bioactive metabolites such as phytochemicals, antioxidants, coloring pigments,
and nutrients. Roots, barks, seeds, midribs, peels, bracts, and leaves are some
of the most common organic by-products of food production. The recovery of
by-products from food wastes always remained underutilized due to the lack of
sensitive extraction techniques. On the basis of nature of both the wastes and the
bioactive metabolites to be extracted, different extraction techniques are employed.
There is great probability of degradation of extracted bioactive compounds due to
354 A. Tiwari et al.

harsh environment and processing conditions (Drosou et al. 2017; Rehman et al.
2019; Shishir et al. 2018). Bioactive metabolites can be extracted using a number of
different techniques considering the type and properties of food, fruit and vegetable
wastes, chemical nature, functional properties, and use of end product.
Extraction conditions play a significant role as these are responsible for releasing
bioactive compounds from the plant matrix to the medium. An overview of extrac-
tion techniques is presented in this section.

12.3.1 Solid-Liquid Extraction

Solid-liquid extraction involves solubilization of bioactive compounds of a solid

matrices into the liquid aquatic organic solvent. The solvents are selected so that
lesser interference is caused by the matrix (Luthria 2008). The quantity and quality
of extracted bioactive compounds depends on the optimization of experimental
parameters. The significant parameters to be considered include pH, temperature,
time, particle size, solid-to-liquid ratio, solvent polarity amongst others. Herbal and
other food processing units apply this method when the vegetable matrix needed
extraction before further processing. Nevertheless, this method has a massive draw-
back in terms of using expensive, partly toxic, inflammable, explosive, and hazard-
ous organic solvents and the long times needed (Proestos and Komaitis 2008).
Futuristic research might lead to the use of cheap solvent water with some other
mild extraction methods.

12.3.2 Soxhlet Extraction

This method involves repeated washing of powdered plant matrices with hot solvent
that facilitate greater solubilization of bioactive compounds and have used in
processing of food matrices. Soxhlet extraction is a comparatively low-cost extrac-
tion technique. It saves time, energy and affects the financial input to the extraction
of bioactive compounds of interest. On small-scale extraction it is used as a batch
process but in medium- to large-scale extraction, it can be employed as a continuous
method. There is limited use of this technique in food and food waste processing.
Soxhlet extraction proved better than other traditional methods of extraction except
the extraction of temperature sensitive compounds (De Castro and Priego-Capote
2010). It is advantageous to many other advanced techniques such as automated,
high-pressure, ultrasound-assisted and microwave-assisted Soxhlet extraction.
Soxhlet extraction shortens the time of extraction when using auxiliary forms of
energy and automation of energy.
12 Co-Product Recovery in Food Processing 355

12.3.3 Enzyme-Assisted Extraction

An enzyme alone, or in combination of other enzymes optimize extraction of

bioactive ingredients from disrupted cells. It is an auspicious alternative strategy
for solvent-based traditional extraction methods. The efficiency of the method
depends upon the selectivity and specificity of enzymes under prevailing normal
atmospheric conditions in aqueous medium (Gardossi et al. 2010). Hydrolytic
enzymes such as pectinases, cellulases, hemicellulases, etc. hydrolyze cell wall
constituents lead to increase in its permeability to bioactive compounds including
polysaccharides, oils, natural pigments, flavors, antioxidant, and medicinal active
compounds (Puri et al. 2012). The enzymes used can be obtained from bacteria,
fungi, animals and plants tissues. Optimization of conditions like temperature, pH,
pressure, time, and concentrations of enzyme and substrates should be appropriately
established to increase the yield. To reduce the consumption of solvents, time of
extraction along with high yield and quality of bioactive compounds specific
enzymes can be used for pretreatment of waste food or whatever the substrate is
utilized. The main limitation to enzyme-assisted extraction is its high cost of
processing raw materials at industrial scale (Baiano 2014).
Conventional methods entail pessimistic thermal effects on yield and quality of
extraction. This approach implies large expenditure of solvent and energy.
A few among them are supercritical fluid, subcritical water, ultrasound-assisted,
microwave-assisted and pulsed electric field for the extracting phenolics, anthocya-
nins, flavonoids, tannins, carotenoids, and vanillic acid (Pattnaik et al. 2021).

12.3.4 Ultrasound-Assisted Extraction (UAE)

Ultrasonic waves are sound waves with 20 kHz. In liquid media UAE is a nice
choice for extraction of bioactive metabolites from food, agro-wastes, fruit and
vegetable wastes via acoustic cavitations, vibration and their mix effect. For quality
extraction of bioactive metabolites, frequency range 20 to 100 kHz is generally
employed (Cravotto et al. 2008). The efficiency of UAE depends on the physical
forces generated due to acoustic cavitation, which leads to the destruction of cell
walls and facilitates extraction (Vardanega et al. 2014). In addition, acoustic pres-
sures generate zones of low and high pressure in the liquid medium. When exposed
to acoustic fields, cavities are formed by microbubbles. Acoustic field depends on
the frequency of acoustic cycles. The microbubbles expand and contract due to
negative and positive pressures, respectively. The expansion and contraction leads to
the exchange of gases. As a result of exchange of gases, the size of bubbles increase
considerably due to accumulation of mass or through fusion of microbubbles. After
achieving a critical size the bubbles collapse (Alzorqi and Manickam 2015). Due to
cavitation a large quantity of solvent enters into the cell matrix and releases the
phenolic compounds into the solvent by cell wall dislocation. Cavitation gets
356 A. Tiwari et al.

affected by temperature and occasionally an increase in temperature increases the

rate of solvent diffusion by depleting the interactions between solvent and matrix
(Kaderides et al. 2015). When a range of temperature 30 to 70  C employed
maximum phenol extraction was observed at 70  C indicating optimum extraction
at higher temperatures (Ahmed et al. 2020). Comparative studies indicated that UAE
is more efficient as compared to the conventional extraction methods like soxhlet
extraction (Drosou et al. 2015; Safdar et al. 2017).

12.3.5 Microwave-Assisted Extraction (MAE)

MAE is another extraction technique that can be used in combination with conven-
tional one but is superior to them because it utilizes less solvent, high extraction
efficiency and needs shorter duration (Delazar et al. 2012). The electromagnetic field
generated by microwaves varies in the range between 300 MHz and 300 GHz. Polar
molecules absorb this energy and then transform into heat due to dielectric heating.
In MAE the solvents with greater dielectric constants are commonly used for
extracting bioactive compounds from plant matrices. Such solvents absorb micro-
wave waves maximally and convert into kinetic energy. Highly energetic molecules
enter into the plant material by diffusion and solute molecules carried into the
solvent (Jaitak et al. 2009). The mechanism of MAE involves three steps. To
begin with, localized heating near the boiling point due to absorption of microwaves
by water glands inside the plant materials expands water and disrupts cell walls.
Heating leads to breakdown of hydrogen bonds and associated interaction between
the solute and active site of plant matrices which finally causes the cell wall to
disrupt. Second, ruptured cells encourage the mass inflow of solvent in the plant
matrix and solute into the solvent. In the third and last step extracted solutes spread
in the nearby solvent (Alupului et al. 2012). Microwave-assisted extraction can be
performed in closed and open apparatuses. Closed MAE apparatus includes sealed
vessels with invariable microwave heating. Closed systems are faster and more
efficient in extraction as there prevailed conditions of high temperature and pressure
but need extra safety measures. On the other hand, open systems need less safety
concerns comparatively and a beautiful option for extraction of thermolabile bioac-
tive metabolites (Chan et al. 2011).
Sometimes the plant materials or the food waste are directly heated by micro-
waves which release the bioactives into cold solvent (Liu et al. 2018). In comparison,
traditional Soxhlet extraction methods need a large amount of solvent and time.
Zhang et al. (2005) carried out comparative study on different extraction methods
like percolation, UAE, MAE, and maceration to extract alkaloids from Macleaya
cordata. MAE was observed to yield the highest amount of alkaloids within the
shortest extraction time. MAE has another advantage to use the fivefold to tenfold
reduced amount of solvent if compared to the classical methods of extraction. Using
MAE needs special attention in designing closed reaction vessels as there are
chances of solute degradation and explosion in the closed vessel MAE equipment.
12 Co-Product Recovery in Food Processing 357

12.3.6 Pulsed Electric Field Extraction (PEF-E)

This is an evolving extraction technique for extraction of bioactive metabolites from

plant samples. This method does not involve heating that destroys cell structure. In
this method electric pulses of moderate electric field strength are used for very short
duration (Azmir et al. 2013). Application of these electric fields generate trans-
membrane potential on the cell surface. When the trans-membrane potential crosses,
a critical limit electroporation occurs. As a result, membrane permeability increases
and there is an efflux of compounds from the cell interior. PEF-E is beneficial to
increase the yield of bioactive at lower energy costs and threats to the environment
(Siddeeg et al. 2019). PEF-E is useful to extract thermolabile bioactive compounds
from the sample matrix.

12.3.7 Supercritical Fluid Extraction (SFE)

Supercritical fluids (SCF) are maintained above their critical temperature and pres-
sure. Under such conditions they show properties between pure liquid and gas and
are known as compressible liquid or dense gas. SCF show liquid like densities,
diffusivity greater than liquids, good solvating power, reduction in surface tension,
low viscosity, and gas like properties, hence exhibiting high penetration to the solid
matrices (Pitchaiah et al. 2019). Water at 374  C and 22.1 MPa and CO2 at 31.3  C
and 7.38 MPa exist as supercritical fluid. SCFs can easily diffuse into the solid
matrix like a gas and dissolve solute efficiently like a liquid. These properties are
responsible for higher yield in shorter extraction duration (Soquetta et al. 2018). SFE
is a method of extraction of bioactives from the sample matrix using supercritical
fluid as extraction solvent. Carbon dioxide is the most useful supercritical
fluid, sometimes modified using non-toxic, non-explosive and non-polar co-solvents
such as ethanol and methanol, which can easily extract slightly polar compounds. Its
easy removal from the final product made it a preferred solvent for extraction of
bioactive compounds from plants and food by-products (Wang and Weller 2006).
SFE stepwise procedure for extraction involves placing raw material in the extrac-
tion chamber provided regulated temperature and pressure conditions. Then it is
pressurized with the fluid by a pump regulating the temperature conditions. The
bioactive compounds dissolved in fluid are carried to the separation units and
collected at the lower part of the structure. The fluid is then recycled or released
(Da Silva et al. 2016). Due to slight polarity shown by supercritical CO2, the
bioactive metabolites from the solid matrix exhibits reduced solubility. To overcome
this barrier, co-solvents or modifier like water and ethanol are used in addition to
supercritical CO2 (Da Porto et al. 2014).
Supercritical anti-solvent (SAS) process has been utilized for precipitation of
bioactive compounds. In this process the sample containing bioactive compounds is
first dissolved in an organic solvent. Then the continuous flow of CO2 in the
extraction system is maintained under regulated temperature and pressure
358 A. Tiwari et al.

conditions. The solute-solvent mixture is then sprayed into supercritical CO2; here
organic solvent is separated from the mixture. Under supercritical conditions there is
high solubility of organic solvent in the supercritical CO2; an instant mutual diffu-
sion occurs at the interface of solute and supercritical CO2; this leads to the
saturation and phase separation of solute in supercritical CO2, which results in
nucleation and precipitation of the desired compound (Zhong et al. 2008). A number
of bioactive metabolites have been extracted using SCFs such as flavonoids from
onion peels (Munir et al. 2018), pectin from Jackfruit wastes (Li et al. 2019), and
phenolics from grape waste (Elmi Kashtiban and Esmaiili 2019). Baysal et al. (2000)
extracted lycopene and beta-carotene from tomato pomace, crushed skins of fruits
and seeds using supercritical CO2 and ethanol. SFE is an appropriate method to
extract caffeine up to 97% from green tea leaves with no effect on useful catechins
and flavonols (Perva-Uzunalić et al. 2004). Oil from rice bran successfully extracted
using 100 g supercritical CO2 at 10,000 psi pressure and 80  C temperature resulted
in highest yield (Perretti et al. 2003).
At industrial scale if this method is used in isolation, it yields suboptimal results
while on using in combination with certain pretreatments and scale-up methods
optimum extraction can be achieved. The integration of SFE with prior cleavage and
separation, microorganisms-mediated partial breakdown of feedstock, pretreatment
of plant matrices with certain chemical and enzymes to release the bioactive com-
pounds, etc. can uplift the efficiency.

12.3.7.1 Subcritical Water Extraction (SCWE)

SCWE is an alternative extraction technique to the conventional ones, this promising

technique is environment friendly as well as less toxic. The process involves heating
of water at 100–320  C at a pressure of about 20 to 150 bar. Under such conditions
water remains in liquid phase but its dielectric constant changes from 80 to 27 which
is at the level of ethanol and methanol under normal conditions. This decrease in
dielectric constant of water increases the solubility of nonpolar solutes in water
(Gbashi et al. 2017). This unique property of water is utilized for extraction of a
variety of bioactive compounds. Munir et al. (2018) compared the extraction of
phenolic compounds from onion peels using SCWE for half an hour and ethanol for
3 h. They found higher amounts of phenolics and flavonoids in SCWE as compared
to ethanol. This was due to breaking of no-covalent interactions like hydrogen
bonds, van der Waals forces, and low viscosity of water between solute and matrix.
A pretreatment is given to improve the rate of extraction, minimize long exposure of
heat-sensitive bioactive compounds. Commonly the plant samples are pretreated
with ultrasonication, microwaves and gas hydrolysis with N2 or CO2. Microwaves
and ultrasonication diffuse the bioactive compounds into solvent while N2 replaces
oxygen in water, this forms a shielding effect on the reaction milieu that enhances the
extraction of bioactives (Zhu et al. 2008). Among all the pretreatments, microwaves
proved to be the best for extraction of bioactive compounds from the spent ground
coffee. Some limitations for this method are its high cost of processing per unit
sample and high reactivity of water under specified conditions.
12 Co-Product Recovery in Food Processing 359

12.3.8 Cold Plasma Assisted Extraction

Two major constraints in solvent extraction techniques are that a large quantity of
solvent is needed with low yield. The use of solvents like methanol affects the
quality of processed products and is also harmful to the environment. Conventional
solvent extraction generally carried out at high temperature for longer durations that
consume more energy (Brglez Mojzer et al. 2016; Mokhtarpour et al. 2014).
Plasma is a partially ionized gas containing activated particles, i.e., ions, free
electrons, radicals, and photons, and it is often referred to as the fourth state of
matter. Plasmas are classified into two types: high-temperature plasmas and
low-temperature plasmas. High-temperature plasmas also called fusion plasma con-
tain equilibrium at temperatures higher than 107 K (Rutscher 2008).
Low-temperature plasma may be of thermal and non-thermal plasma. Thermal
plasma components remain at equilibrium at higher temperatures than non-thermal
plasma. Non-thermal plasma or cold plasma is operated at temperatures lower than
400  C. It produces reactive gas species, UV radiation, energetic ions, and charged
particles, all of these can cause significant physicochemical reactions in treated
samples (Hoffmann et al. 2013). Dielectric barrier discharge (DBD) and plasma jet
generated cold plasma is commonly used in food processing (Misra et al. 2016). The
influencing factors in this technology are treatment period, applied voltage, working
gas, and relative humidity (Lotfy et al. 2020). The cold plasma properties like cell
wall rupture and modification of surface ease the diffusion of internal molecules and
enhance the extractability of secondary metabolites especially the phenolic com-
pounds and essential oils from the waste food debris. Kodama et al. (2014) extracted
comparatively higher amounts of essential oils from orange peels using cold plasma.
It is observed that cold plasma treatment affects the food products’ total phenolic
contents inconsistently. In orange and white grape juice, low total phenolic contents
are reported when treated with cold plasma (Almeida et al. 2015; Pankaj et al. 2017).
The same treatment enhances total phenolic content in cashew apple juice
(Rodriguez et al. 2017). This indicates that the mechanism of interaction at molec-
ular level between cold plasma reactive species and phenolic content is still
unknown.

12.4 Conclusion: Current Challenges and Future

Opportunities

In today’s world, “waste” is the biggest environmental problem and the dearth of its
management has serious consequences on animal life and health. As society has
moved from paucity to wealth, food waste has become more of an ethical and social
issue. The magnitude of this nuisance is so immense ecologically, that it is pivotal to
pay consideration towards optimal food waste management recycling procedures. It
is not only unwise but also cruel to dispose of food (either raw or prepared). For a
360 A. Tiwari et al.

cleaner environment, sustainable methods should be applied for food waste man-
agement. The highlighted key points which can be concluded from this chapter are as
follows: (a) Water usage has to be minimized and use of recycled water should be
adopted; (b) residential kitchen waste could be channelized to generate electricity via
biomethanation; (c) Fish feed is the best substitute for food waste. Fish processing
industries give several peptides (thrombin, globulin, and prothrombin) for biomed-
ical applications; (d) Dairy by-products have been appropriately used in biorefineries
to produce bioethanol and biodiesel and value-added metabolites (i.e., citric acid,
succinic acid, lactic acid, etc.); (e) Food waste is also a powerhouse for producing
industrially relevant enzymes such as lipase, cellulose, pectinase amongst others.
Natural dyes and pigments (lycopenes) can be derived from food waste.

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Chapter 13
Upcycling Technologies in the Food
Industry

Rubeka Idrishi, Divya Aggarwal, and Vasudha Sharma

Abstract Food waste is a global issue upon which many countries are concerned.
Moreover, among the consumers of the upper strata of the pyramid, the dietary
habits and lifestyle changes have undoubtedly imparted the increasing concern of
food waste, and to the lower part people who cannot afford nutritious food the
prolonged starvation causes deaths worldwide. Waste valorization is a key to
endow them with essential food commodities. It is an ethical issue that plays a
key role in developing a sustainable economy. Focusing on the sustainability
aspect of the food cycle will thus contribute to SDG2030 which aims to “end
hunger, achieve food security and improved nutrition and promote sustainable
agriculture”, responsible consumption and production, climate action, life below
water and life on land and sustainable food production, consumption patterns, and
efficient agricultural practices by ensuring the accessibility of safe food to the
people are directly related to them.
In this chapter, various upcycling technologies have been discussed concerning
its present status, technologies used, challenges, and future prospects because many
studies reveal that there is a huge gap in understanding the vicious cycle in upcycling
food commodities.

Keywords Upcycling · Food industry · Hunger · Recycle · SDG · Food waste

R. Idrishi (*)
Indian Institute of Technology Guwahati, Guwahati, Assam, India
e-mail: rubeka1995@iitg.ac.in
D. Aggarwal
CSIR-Central Food Technological Research Institute, Mysuru, Karnataka, India
V. Sharma
Department of Food Technology, Jamia Hamdard (Deemed to be University), New Delhi, India

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 367
S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies,
https://doi.org/10.1007/978-981-19-1746-2_13
368 R. Idrishi et al.

13.1 Introduction

Food wastage and food loss hamper the sustainability of the food systems globally
and to encounter the same, various multifaceted technical and innovative solutions
are being proposed, researched and policies are being laid by all the stakeholders in
and outside the food chain (Ojha et al. 2020). Throughout the food value chain from
farm to fork almost all the stages of production, postharvest, processing, distribution,
and consumer purchases include a certain degree of food loss and/or food waste.
This waste can be categorized industry-wise into agriculture, horticulture, brewery,
dairy, feed-fodder, and miscellaneous (Ravi et al. 2020). Henceforth, upcycling is a
great way to tackle food shortage and food waste issues at all stages of the supply
chain. It is an exciting sustainable trend in the food sector that involves upgrading
and recycling the substantial amounts of underutilized fractions into innovative and
essential ones. In food industries, the utilization of by-products has been considered
an object of interest to minimize unexpected wastage and adverse environmental
impacts. Closing the food loop by recycling nutrients in food waste is an important
way of limiting the use of mineral nutrients, as well as improving national and global
food security.
To close this loop of food waste all the stakeholders such as researchers, author-
ities and government should look into the hierarchy of the food waste to minimize
the generated waste within their sector itself, and to assure that the waste which is
flowing into other sectors is being sent into optimal conditions to be treated,
processed, or reused (McConville et al. 2015). Therefore, owing to the rising food
and nutrition security issue the need for upcycling technologies in the food industry
is escalating which are now backed up by policies like the United States Environ-
mental Protection Agency (USEPA) by the United States Department of Agriculture
(USDA), and Environmental Pollution Act (EPA) India. These policies are collab-
orating to reduce and recover food waste (McConville et al. 2015). The life cycle
assessment (LCA) studies w.r.t different streams of waste from food industry at
different stages is completely lacking as only household waste LCA has been studied
to some extent (Gao et al. 2018).
Provided all these research backgrounds, a global cohesive approach is needed to
bring all the stakeholders of the food chain and the allied sectors to a single platform.
This chapter aims at drawing attention towards various upcycling approaches and the
research done for the same, and hence adds its bit towards maintaining a closed loop
in terms of the life cycle of food.

13.2 Various Waste Streams in Food Industries

Recycling food waste streams is one of the major concerns of the food industry and
globally Japan and South Korea have been the major players in upcycling the same.
There are various concerns with regard to regulations and legislations for different
13 Upcycling Technologies in the Food Industry 369

Table 13.1 Various food industries and their waste streams

S. No. Industry Major waste streams Reference
1. Meat Meat and bone meal, feather meal, Henchion et al. (2017), Rao et al.
industry and blood meal, skin, nails, (2021)
feathers, bones, hides, horns, vis-
ceral mass
2. Bakery Ground biscuits, bakery meal, and Shurson (2020)
industry potato crisps
3. Fish and Fish meal, shells of molluscs, and Araujo et al. (2020), Garcia-
marine viscera Sifuentes et al. (2009), Kangas
industry et al. (2013), Ucak et al. (2021)
4. Fruits, veg- Peel, seeds, pomace, bagasse Dhumal and Sarkar (2018),
etables, and Fierascu et al. (2019)
tubers
5. Milk Whey Pame et al. (2020), Rao et al.
industry (2021), Skryplonek et al. (2019)
6. Cereal Husk, spent grains, bran, germ, and Alexandri et al. (2020)
industry milling meal
7. Nuts and Pressed cake, meal, and residues Kangas et al. (2013), Rao et al.
oilseed (2021)
industry
8. Coffee Coffee mucilage Alexandri et al. (2019), Heeger
industry et al. (2017)
9. Spirit pro- Spent grains, waste distilled Rao et al. (2021), Roth et al.
ducing fractions (2019)
industry

waste streams and the risk of transmission of pathogens, bacteria, viruses, parasites,
and prions (Shurson 2020). Therefore, it is necessary to know about various waste
streams of the food industry in order to understand all the necessary attributes to
model food waste management systems and their assessment (Garcia-Garcia et al.
2019). Following are the major waste streams of food industries (Table 13.1).

13.3 Upcycling Technologies of Food Industry Waste

Streams

13.3.1 Biotechnology and Fermentation

It is one of the promising upcycling technologies intended for the recovery of

valuable food products. As food is a matrix for several biomolecules including
carbohydrates, proteins, fats, and oils, it is hydrolysed to simpler molecules that is,
sugars, amino acids, glycerol, and fatty acids, respectively. The molecules in food
waste are also cleaved similarly via the route of either acids or biological/enzymatic
routes. Nevertheless, enzymatic processes are preferred as they specifically target the
370 R. Idrishi et al.

biomolecules to desired moieties, otherwise using chemical (acid) hydrolysis, may

lead to the production of inhibitors or unpleasant by-products.
The technology involves biocatalyst-based processes which are considered to be
highly selective and environmentally safe in food processing. Enzymes, classified as
microbial or non-microbial, are used as catalysts to enhance the activity of chemical
reactions. The development of rational biocatalysts using enzymes for chemical
modification of food processing waste is also an effective means which overcome
the limitations of the traditional catalyst systems (Andler and Goddard 2018). These
catalysts aid in the cleaning of food waste streams and developing novel products out
of them. Some of the potential enzymes are discussed here:
Proteases belonging to the group of hydrolases are generally employed to
solubilize proteins in food waste streams, thereby resulting in the recovery of
nutritious solid and liquid concentrates (Karam and Nicell 1997). A study reported
the action of alkaline proteinase from Bacillus subtilis on waste chicken feathers
from poultry slaughterhouses. Upon enzyme hydrolysis, it was revealed that the end
product contains a very high protein content that can be used as a feed constituent
(El-Nagar et al. 2006).
Amylases are one of the crucial industrial enzymes that catalyze the hydrolysis of
starch into smaller fragments, glucose, and maltose. For example, potato is a high
carbohydrate food and is considered a staple vegetable to be consumed around the
world. Using potato peels as a carbon substrate, it was identified that amylase could
be isolated from the Bacillus subtilis K-18, one of the bacterium species that could be
a potent strain for biofuel production (Mushtaq et al. 2017).
Lipases are immense biocatalysts that can catalyze certain reactions including
hydrolysis, esterification, and transesterification under mild conditions. In a study
conducted, lipases from Thermomyces lanuginosus and Candida antarctica B were
used for hydrolysis and esterification, respectively, to obtain biodiesel from waste
cooking oil. It could alleviate the energy demand of plants as well as the costs of
conventional downstream processes (Vescovi et al. 2016).
Pectinases are a group of enzymes that act on the degradation of pectin, a
polysaccharide commonly found in the cell wall of plants. The sources of pectin
include apples, bananas, peaches, apricots, etc. The pectin degrading enzyme, pectin
esterase is produced from Clostridium thermosulfurogenes. A food processing
waste, apple pomace is used as a substrate for producing butanol and it has been
assessed that up to 80% of the sugars were consumed, and the residue remained after
the separation of butanol possess the potential to be an excellent animal feed
(Blaschek 1992).

13.3.2 Supercritical Fluid Extraction

For many years, it has been believed that food wastes are considerable sources of
potential bioactive compounds. However, a vast majority is yet to be exploited.
13 Upcycling Technologies in the Food Industry 371

Table 13.2 Sources, their bioactive compounds and the operational parameters for CO2 supercrit-
ical fluid extraction
Bioactive Parameters (Temperature
S. No. Source compounds and pressure) Reference
1. Tomato peels Lycopene, 80
C and 30 Mpa Sabio et al.
and seeds β-carotene (2003)
2. Peach seeds Phytosterols 40
C and 20 Mpa Ekinci and Gürü
(2014)
3. Citrus peel Volatile oils 35
C and 10 Mpa Omar et al.
(2013)
4. Passion fruit Fatty acids 56
C & 26 Mpa Liu et al. (2009)
seeds
5. Banana peels Essential oil 40
C and 30 Mpa Comim et al.
(2010)
6. Grape pomace Phenolic 50–60
C and 30 Mpa Oliveira et al.
compounds (2013)

Compared to the conventional solvent extraction technique for isolating these

compounds, supercritical fluid extraction proves to be an environmentally effective
alternative that yields products free from toxic residues (Hauthal 2001). The appli-
cation of this technique lies in the efficient utilization of industrial wastes and
transforming them into valuable products having no or little economic value (Viganó
et al. 2015). Carbon dioxide is one of the most frequently used supercritical fluids
that contribute to the greener extraction processes for phenolic compounds. It is a
low-cost, non-toxic, non-mutagenic, non-flammable, thermodynamically stable, and
high purity solvent. Additionally, because of its moderate critical temperature
(31.3
C) and pressure (7.38 MPa), it can also be used in the extraction of thermally
labile compounds (Sabio et al. 2003), (Torres-Valenzuela et al. 2020). Some of the
examples of food processing wastes that utilize CO2 as a solvent in supercritical fluid
extraction and produce significant bioactive compounds are highlighted in
Table 13.2.

13.3.3 Separation Techniques for Upcycling

The global demand urges for the treatment of food industrial wastes. The sustainable
approach to this is to effectively recycle the waste streams and recover valuable
products from them. This section emphasizes the practical applications of different
separation methods, whether it be physical or chemical.
The separation technologies are based on the principle of changing the phase of
the matter in a single or multiple-step operation. Adding steps to the process may
enhance the quality of the end product while also increasing the capital and operation
costs. Suppose if the technique involves m steps, each having similar recovery
372 R. Idrishi et al.

efficiency of ɛ, then overall recovery efficiency may be calculated as follows

(El-Mashad and Zhang 2007):

Overall recovery ¼ ɛm

When the m steps possess distinct recovery efficiencies, the overall recovery
efficiency can be calculated as follows:

Overall recovery ¼ ɛ1  ɛ2  . . .  ɛm

13.3.3.1 Physical Processes

Screening
Generally, it is employed as a primary separation method to separate solid materials
from waste streams which then undergo several unit operations such as drying with
rotary vacuum filters and can be converted to animal feed sources. Like in cereal
industries, raw materials are subjected to various cleaning operations to separate
impurities. Screening is one of the vital steps to remove foreign particles such as
stones, chaffs, crop seeds, etc. according to the differences in their physical charac-
teristics, e.g. shape, size, density. To increase the flowability of solid materials and
ameliorate the screening rates, methods like mechanical agitation and screen incli-
nation may be implemented (Li et al. 2002).
Flotation
One of the primary separation methods utilizes three phases, solid phase, liquid
phase, and gaseous phase separately. In principle, it is a surfactant-based separation
process wherein on adding certain surfactants, a scum appears on the surface after
the gas bubbles transport through the solution and solid materials are removed
(Kyzas and Matis 2019). Liquid biphasic flotation is a novel technique that has
immense applications in the recovery of potential substances from food waste. For
example, betacyanin extraction from the peel and flesh of red-purple pitaya (Yi et al.
2019), and protein extraction from expired milk products (Yap et al. 2019) that
approaches a green and sustainable environment.
Sedimentation
It involves the use of gravity to separate solid substances from industrial waste
streams. The differences in the specific gravities make these substances settle/deposit
as sediments which are then removed using certain clarifiers. In some cases, this
physical treatment can also be done in combination with chemical processes. Like in
treating wastewater, the use of divalent ions (calcium and magnesium) serves to
enhance the sedimentation rates by destabilizing the unwanted particles (O’Melia
1998). A newly fabricated flotation-cum-sedimentation system has been used for the
separation of skin and seeds from tomato pomace which was then used for extracting
lycopene, a vital phytochemical compound (Kaur et al. 2005).
13 Upcycling Technologies in the Food Industry 373

Centrifugation
It is an efficient separation technique that utilizes centrifugal force to separate
particles based on their density differences. It is widely used as a pre-treatment
process in the dairy industry for separating cream from skim milk. The food waste
generated from kitchens ends up contributing to municipal solid wastes that get
treated via fermentation processes followed by centrifugation, resulting in the
production of short-chain fatty acids. These can further be used in manufacturing
high-quality poly-L-lactate (PLLA) biodegradable plastics (Sakai et al. 2003) as well
as in generating biofuel that can be used for cooking purposes (Karmee 2016). It is
also observed that centrifugation followed by pH shift (acidic and alkaline) results in
the isolation of high-protein fractions from sardine fishmeal waste (Garcia-Sifuentes
et al. 2009).
Crystallization
It is a process in which molecules orient themselves into a structure known as crystal,
forming a solid phase from an aqueous solution. It may occur due to certain physical
(changes in temperature, pH) and chemical changes. It has significant applications in
minimizing food waste, ranging from recovery of protein from whey and phospho-
rous from wastewaters using lactose (Božanić et al. 2014) and struvite (Le Corre
et al. 2009) crystals, respectively. A unique crystallization process referred to as
drowning-out crystallization is specifically appropriate for the separation of heat-
sensitive compounds such as a sustainable recovery of polyphenols from olive mill
wastewater (Dammak et al. 2016).

13.3.3.2 Chemical Processes

Precipitation
A chemical technique used to separate components from the food waste streams
depends upon their solubility characteristics. Due to these factors, the soluble
compound turns to an insoluble solid known as precipitate and forms a suspension
by getting dispersed in the solution. Precipitation is a significant operation in the
recovery of polysaccharides as well as proteins. Usually, proteins are taken to an
insoluble state by the action of heat, or by altering the composition of the solution
(pH, ions, electrolytes) followed by their extraction using solid-liquid separation
techniques. A traditional separation technique, iso-electric precipitation has been
generally used for producing casein precipitates and soy protein isolates from milk
and soy, respectively. These methods can be performed at a large scale using simple
equipment, and at low costs too (Zaror 1992), (Singh and Singh 1996). It also has
numerous applications in the treatment of food waste. For instance, bromelain is a
protein-digesting enzyme extracted from the pineapple stem which is also reported
in the core, peel, and crown of the fruit, major wastes generated from the pineapple
processing industry. It was identified that the acetone precipitation yielded a higher
recovery of bromelain activity from pineapple wastes (Chaurasiya and Hebbar
2013).
374 R. Idrishi et al.

Coagulation
Chemical coagulation is customarily applied in the treatment and purification of
industrial wastewater. This method utilizes certain chemical coagulants that desta-
bilize the colloidal particles and aggregates to form micro-floc materials, referred to
as flocculation. These flocculant materials are then removed in subsequent filtration,
flotation, and sedimentation stages, etc. The technique involves mainly inorganic
metal salts, including ferric chloride, aluminium, ferric sulphates, etc. Generally,
these metals decrease the pH of waste streams from the alkaline levels to nearly
neutral values that exert a strong positive effect in reducing turbidity, suspended
impurities as well as chemical oxygen demand (COD) (Renault et al. 2009) as being
elucidated in a study wherein a cationic carbohydrate polymer, chitosan was used as
a chemical coagulant and the results revealed that the concentration of suspended
solids and turbidity were reduced to 97% and 83%, respectively, along with a 45%
reduction in COD in seafood processing streams. The process involved the recovery
of organic compounds, notably a large concentration of flavour-related free amino
acids, including arginine, alanine, glycine, glutamic acid, and serine (No and Meyers
1989). It has been suggested that coagulation followed by ultrafiltration, is a
potential method for recovering the polyphenols and proteins from flaxseed hulls
(Loginov et al. 2013).

13.3.3.3 Membrane Processes

Reverse Osmosis
A general separation technique that applies pressure against the osmotic pressure to
force the movement of solvent from a region of high solute concentration to a less
concentrated solution through the semipermeable membrane. It is considered as the
leading and optimized membrane-based solution in purifying water by desalination
process that refers to the removal of salts and minerals from the sea or brackish water
(Qasim et al. 2019). It proves to be an alternative process in managing the food
industrial wastes, for example in treating the solid wastes generated from the orange
juice industry (Mayor et al. 2011), and in reclaiming the wastewater from the dairy
industry (Suárez et al. 2015).
Micro- and Ultrafiltration
In recent years, the use of membrane-based techniques is gaining more attention in
food processing industries. These pressure-driven membrane processes are based on
certain pore sizes of the membranes to separate the dissolved substances. Indeed,
they offer various advantages over conventional separation methods, including
non-use of chemical agents, including non-use of chemical agents, operated under
mild conditions of temperature and pressure, thereby preserving the functional
attributes of food products. Additionally, they are highly selective towards specific
compounds of interest, and use simple equipment with a lesser number of processing
steps and hence, low energy consumption (Castro-muñoz et al. 2018). The ultrafil-
tration technique has been employed for the separation and recovery of phenolic
13 Upcycling Technologies in the Food Industry 375

compounds from almond skin extracts (Prodanov et al. 2008) and grape seeds
(Nawaz et al. 2006) depending on their molecular weight. In a study conducted,
microfiltration and ultrafiltration techniques were known to recover biomolecules
(protein and fat) and permeate (containing NaCl and acetic acid) which makes post-
production marinating brines re-use in fish marination and not discharged as futile
effluent (Nędzarek et al. 2017).

13.4 Upcycling of Waste from Food Industries into Other

Value-Added Products

13.4.1 Dairy

For many years, this industry has gained global attention due to the consumption of a
wide range of products including butter, cheese, curd, yoghurt, milk powders, etc.
Milk is composed of approximately 87% water, and additional water is required for
the cleaning and sanitation of dairy processing plants, thereby producing substantial
amounts of liquid wastes. Whey, being the predominant waste generated from the
processing of cheese, notably 1 kg of cheese produced is expected to give rise to nine
kilograms of whey (Martínez-Ruano et al. 2019). Due to its high biological oxygen
demand (BOD) and COD concentrations, it contributes to polluting the environment.
Nevertheless, it is considered a significant source for valorization into functional
products, including whey protein, whey permeate, bioethanol, biopolymers, hydro-
gen, methane, probiotics (Yadav et al. 2015) and D-lactic acid (Alexandri et al. 2019;
Sakai et al. 2003), and whey protein isolates (Sani et al. 2021). Casein is the second
component which leaches out in the dairy waste streams; owing to its biodegradable
nature it is suitable for forming edible films which have favourable mechanical and
optical properties (Sani et al. 2021). In addition to this, it elicits certain applications
in developing value-added food products such as dietary protein, bio protein (for
food and feed applications), whey protein isolates for infant formula, beverages,
food, and dietary supplements for medical purposes (Rao et al. 2021), functional
fermented beverages (AbdulAlim et al. 2018), low-fat meat products (Pame et al.
2020), and ice popsicles using an underutilized crop “jamun”, a natural bioactive
source with potential therapeutic benefits (antidiabetic) and enhanced organoleptic
properties (Jan et al. 2021). The advanced technological methodologies for the
isolation of whey protein and its derivatives include novel separation methods
such as vibratory shear enhanced processing, chromatographic technology, high
power ultrasound, ion exclusion, and molecular recognition-based isolation tech-
niques (Rao et al. 2021).
376 R. Idrishi et al.

13.4.2 Meat, Poultry, and Its Derivatives

The meat industry trashes enough slaughterhouse by-products, including meat meal,
meat and bone meal, feather meal, and blood meal, skin, nails, feathers, bones, hides,
horns, visceral mass, etc., that contribute to around 60% to 70% of the slaughtered
carcass, of which 40% and 20% form edible and inedible waste, respectively
(Bhaskar et al. 2007). Carcass rendering in the meat industry is one of the risky
affairs because of the perceived risk of incomplete destruction of all pathogens,
viruses, and prions (Shurson 2020). To minimize these wastes, one of the significant
approaches is anaerobic digestion that has been proven to be a promising and green
alternative for the recovery of nutrients (N, P, Vitamins, Protein) as well as energy
from the industry-derived organic wastes with high protein and fat content. This
strategy aids in the anaerobic bioconversion of these wastes into the generation of
biofuel and biofertilizers (Onwosi et al. 2020). Bioactive peptides, mineral binding
peptides, and plasma proteins derived from blood and collagen obtained as meat
industry waste have various health promoting properties (Rao et al. 2021). Addi-
tionally, the poultry processing industry generates feather by-products that are
known to be a considerable source of structural protein (keratin) that can be used
as a raw material for producing cheap keratinase enzymes followed by their further
valorization into sustainable value-added products by hydrolysing it into feather
meal (Tesfaye et al. 2017). Also, the development of protein hydrolysates from the
pre-treated sheep visceral mass explains their role as functional ingredients in raising
the protein quality of foods (Bhaskar et al. 2007) mainly as a flavouring agent (Rao
et al. 2021). The blood meals obtained from these industries can be spray dried and
can be used for feed and microbiological media (Shurson 2020). Meat processing
waste is also a source for various technological applications in the food industry such
as immunoglobulins, fibrinogen, and serum albumin which can be used as emulsi-
fying and gelation agents, plasma proteins as foaming and protein enrichment, white
blood cells (WBC) as an antimicrobial agent, enzymes such as fibrinogen and
thrombin as binding agents in meat product processing, gelatin obtained from
collagen as a gelling agent, stabilizer, clarifier, biodegradable edible films (Sani
et al. 2021), and coating materials.

13.4.3 Seafood

With increasing urbanization, the worldwide consumption of fish and its associated
products is increasing rapidly and around 70% of the fish consumed globally is
processed in the industries before being sold to consumers (Araujo et al. 2020). After
processing, the industry generates a huge quantity of waste that poses a serious
ecological threat to the environment. These seafood by-products are considered to be
novel sources for the recovery of valuable biomolecules such as collagen and gelatin
which can be utilized in developing functional food ingredients (Pal and Suresh
13 Upcycling Technologies in the Food Industry 377

2016). Astaxanthin, a keto carotenoid recovered from ornamental fish, seafood

industry wastewater is widely used as a colouring agent in fish diets and serves as
a precursor of Vitamin A exerting high antioxidant effects. It has been highlighted
that the application of solid waste (fish scales) as a natural adsorbent has resulted in
the isolation of this pigment from the seafood industry wastewater (Stepnowski et al.
2004). Also, seafood waste has the potential to be used as a cheap medium for the
growth of several proteolytic and chitinolytic microbes with the simultaneous
recovery of value-added products (Satyanarayana et al. 2012). Fish viscera is also
a by-product of the fish industry which is used in the conversion of poultry and fish
feed (Shurson 2020), and shells from molluscs are converted into handicrafts and
decorative pieces. Also, the fish processing wastes are a reservoir of value-added
bioactive compounds such as protease enzymes obtained from fish visceral waste
which may exhibit potential applications in de-staining capabilities against blood-
stained cloth and dehairing goat skins (Sabtecha et al. 2014).

13.4.4 Cereals and Pulses

Cereals and their products are consumed by a vast majority of the population and
hence are considered staple foods in their daily diet. Since starchy endosperm (a rich
source of carbohydrates and energy) is mainly used in the cereal processing indus-
tries, the outer layers of cereal grain kernel (germ, husk, and bran) remain as
by-products (Roth et al. 2019). Research studies emphasize that these by-products
comprise a huge nutrition potential and can be valorized into functional products. It
has been elucidated that the fortification of brans of the three different local kinds of
cereal (maize, rice, and sorghum) significantly ameliorated the nutrient profile
(lipids, proteins, fibres) of breads, suggesting bran to be a vital ingredient in the
formulation of functional foods, and can be used as an alternative in preventing
various chronic non-communicable diseases (Pauline et al. 2020). Rice straw also
contributes to an agricultural waste rich in cellulose that can be upcycled to sustain-
able bioplastic, a potential eco-material for different applications (Bilo et al. 2018).
Protein fractions obtained from waste streams of pulses provide a very good source
of forming biodegradable packaging material. Sani et al. (2021) upon reviewing
various articles found out that proteins of soybean and peas have good mechanical
properties but poor barrier properties.
Upcycling of Distiller Grains
The cereal grain-based fuel-ethanol plants generate distiller’s dried grains with
solubles (DDGS) as one of the principal co-products of the dry-grind distillation
process. It is believed that 100 kg of grain delivers approximately 40 L of ethanol,
32 kg of CO2 as well as 32 kg of DDGS (Chatzifragkou et al. 2016). And, the rapid
increase in ethanol production has led to their production in excess amounts.
Although, since earlier times, DDGS has been marketed as a feed for livestock but
it is essential to embrace its potential for value-added uses. Certain nutrients,
378 R. Idrishi et al.

including linoleic acid, dietary fibre (beta-glucan), and antioxidants (e.g. Vitamin E)
are concentrated in the DDGS and hence, it is suggested to recover them (Gibreel
et al. 2011). In a research study, wet solids were proven to be more suitable as a raw
material for protein extraction, unlike DDGS extracts where it was probably due to
the protein aggregation during the drying process. The upcycling of distiller grains
direct towards future implications in such a way that they can be explored for the
development of biodegradable coatings, films, and biodegradable plastics, which can
be utilized for food and agricultural purposes (Chatzifragkou et al. 2016).

13.4.5 Fruits and Vegetables

Fruits and vegetables are regarded as perishable commodities that often undergo
processing for their shelf-life extension while the fruit and vegetable-based indus-
tries generate a huge quantity of horticultural waste, (including skin, seeds, pomace,
etc.) accounting for 25–30% of the total product. The sustainable solution for
managing such waste lies in its utilization and developing valuable products such
as biosorbents, carbon dots, edible films, probiotics, nanoparticles, etc. (Kumar et al.
2020). The pomace from apple, tomato, and other fruits can be converted into animal
feed as a whole and the derivatives such as dietary fibre, antioxidants, and pigments
find its use in food application by using suitable extraction technologies by keeping
their toxicological components into account such as toxin amygdalin in apple seed,
pesticides on apple skin, tomatine (a toxic glycoalkaloid in tomato), solanine in
potato peels (Rao et al. 2021). Owing to the higher concentration of bioactive
compounds and nutraceuticals (such as carotenoids, dietary fibre, fatty acids, phe-
nolic compounds, proteins, etc.) present in the fruit and vegetable residues, they find
immense applications in formulating functional foods and food additives (Jiménez-
Moreno et al. 2020). The peel, seeds, and membranes of fruits, vegetables, and
tubers can be used for extracting high value products such as fibre, soluble sugars,
organic acids, lipids, vitamins, minerals, and flavonoids (Rao et al. 2021).
For example, the incorporation of grape pomace powder in developing value-
added functional cookies elucidates a sustainable approach of utilizing food industry
waste streams by the application of 3D printing technology (Jagadiswaran et al.
2021). Another potential way is the incorporation of pea pod powder (by-product
emanating from the pea processing industry) in formulating functional products like
instant pea soup powder (Hanan et al. 2020) and mayonnaise (Rudra et al. 2020),
thus representing pea pod shells as a promising candidate for supplementation of
foods with explicit nutritional benefits. Also, the recovery of bioactive compounds
from the valorization of industrial wastewater represents an exciting opportunity for
developing value-added products as well as minimizing adverse environmental
impacts (Chen et al. 2019).
13 Upcycling Technologies in the Food Industry 379

13.4.6 Nuts and Oilseeds

Major waste streams from these industries are of oilseed cakes and meals which are a
source of nutritionally rich and low cost like spent grains from which the cake
fraction being already nutritionally rich finds its applications in the bakery, infant
food, animal feed and supplements but they are not directly used in food-based
applications because of the antinutritive compounds like polyphenols and phytic
acids which gets concentrated after oil expel but it can be reduced by using aqueous
ethanol extraction. These antinutritional properties are found more in rapeseed and
soybean cake and meals (Rao et al. 2021). The cake and meals of oilseeds and nuts
can also be used for the production of bioactive compounds like amino acids,
flavours, vitamins, pigments, enzymes, phenolic acids, lignans, and flavonoids.
These cakes also act as an excellent substrate for solid and submerged fermentation
and mushroom cultivation.

13.5 Polyphenols from Waste Streams in Food Industries

The food industry is undoubtedly one of the largest sectors that generate enough
amount of waste causing harm to the environment. In a review study, it was
highlighted that in developed countries, 42% of food waste is produced by house-
hold chores, 39% losses occur in food processing industries, 14% in the food service
sector (catering, and restaurants), while the rest 5% occurs during retail and distri-
bution (Mirabella et al. 2013) (Fig. 13.1). Over the last years, several studies have
indicated that these wastes are known to be a valuable source of nutrients, especially
polyphenols and bioactive compounds that are of significant use for human health.
However, these are discarded along with trashing the waste, and hence, it is
necessary to recover them. Polyphenols are generally classified into certain catego-
ries such as phenolic acids, flavonoids (flavones, flavonols, flavanones, flavanols,
isoflavones, proanthocyanidins), and their derivatives, stilbenes, and lignans. These
are reported to exert pharmacological effects by acting as antioxidants,

Fig. 13.1 Percentage of Food waste

food waste generated in
developed countries
5%
Household chores
14%

42% Food processing industries

Food service sector

39%
Retail and distribution
380 R. Idrishi et al.

antimutagens, antimicrobial, and anticancer agents and minimize the risk of devel-
oping associated disorders (Gharras 2009).
The extraction of these compounds is of major interest. Traditionally, the extrac-
tion was based on solvent extraction methods, such as solid-liquid extraction, liquid-
liquid extraction, etc. but as encompassing the time, today’s era direct towards an
approach of using novel technologies, including ultrasound, microwave, enzyme-
assisted, and membrane separation (Cai et al. 2021).
Availability of these techniques supports the optimal recovery of the phenolic
compounds and provides an opportunity for using them in developing certain
functional food products. As observed in the case of citrus (e.g. orange, lemon,
clementine) peel wastes, extraction of value-added polyphenols proves to be a
sustainable method in reducing citrus processing waste with immense interest in
formulating therapeutic foods to prevent chronic diseases (Gómez-mejía et al. 2019).
Moreover, it has also been recognized that the total polyphenol content is higher in
peels of citrus fruits, including lemons, oranges, and grapefruits than those of peeled
fruits (Gorinstein et al. 2001). The upcycling of polyphenol-rich almond skins, a
by-product of the confectionery industry in the development of functional biscuits
proves to be another example (Pasqualone et al. 2020). Several examples of food
wastes that yield potential bioactive compounds are listed in Table 13.3.
In a research study, several secondary metabolites have been elucidated from two
sub-streams of agricultural waste, grape pomace, and olive leaves. The former was
known to contain phenolic compounds such as resveratrol, anthocyanins,

Table 13.3 Polyphenols obtained from the food waste streams and their sources
S. No. Source Waste Phenolic compounds Reference
1. Coffee Pulp, Chlorogenic acid, protocatechuic acid, gallic acid, Heeger
husk and rutin et al.
(2017)
2. Olive Leaves Secoiridoids, oleuropein, apigenin, kaempferol, Talhaoui
luteolin, caffeic acid, tyrosol, hydroxytyrosol et al.
(2014)
3. Wheat Bran Ferulic acid, lutein, cryptoxanthin, syringic, Zhou et al.
p-hydroxybenzoic, vanillic, coumaric acid (2004)
4. Tomato Skin, Caffeic, chlorogenic, p-coumaric, ferulic, rosmarinic Ćetković
seeds acid, quercetin, rutin et al.
(2012)
5. Grape Skin, Gallic acid, p-hydroxybenzoic acid, gallic acid, Mattos
seeds syringic acid, caffeic acid, ferulic acid, p-coumaric et al.
acid, catechins, proanthocyanidins, quercetin, (2017)
myricetin, rutin resveratrol, kaempferol
6. Mango Kernel Gallic, ellagic, caffeic, coumaric, protocatechuic, Mwaurah
ferulic acid, mangiferin, homomangiferin, et al.
isomangiferin, anthocyanins, kaempferol, quercetin (2020)
7. Onion Fleshy Quercetin, kaempferol, myricetin, isorhamnetin Pal and
scales Jadeja
(2019)
13 Upcycling Technologies in the Food Industry 381

proanthocyanidins, catechins, and quercetins while the latter contained secoiridoids

and oleuropein. These are all compounds that exert protective effects against the
oxidation of low-density lipoprotein (LDL) in blood circulation along with improve-
ments in lipid metabolism, hence reducing the risk of obesity (Cravotto et al. 2018).
Also, extracts rich in polyphenol content possess the potential to fortify food or
nutritional supplements to enhance the antioxidant and antimicrobial efficacy of
daily diets (Mourtzinos and Goula 2019). For example, the industrial processing of
coffee yields by-product formation such as coffee pulp and husk that reports 29%
and 12% of the dry weight of the original cherry coffee, respectively, and are found
to contain a considerable number of polyphenols, including chlorogenic acid (most
abundant) followed by the presence of protocatechuic acid, gallic acid, and rutin. It
has been recognized that such underutilized by-products can be valorized by formu-
lating a refreshing and nutritious Cascara beverage loaded with a high level of
antioxidants (Heeger et al. 2017).

13.6 Recovery and Upcycling of Macronutrients from Food

Industry Side Streams

The ultimate fate of macronutrients (Nitrogen (N), Phosphorus (P), Potassium (K),
Calcium (Ca), Magnesium (Mg), and Sodium (Na)) from the food industry are not
completely to the gut of the consumer rather few parts of it leach out in the side streams
as well. Thus, for recovering valuable nutrients from various side streams of the food
industry, for instance, feed and food processing water (Matassa 2016) the treatment
process is very important (Buckwell and Nadeu 2016) in which the liquid fraction of
digestate produced from the food industry is usually processed as shown in Fig. 13.2.
A review done by Chojnacka et al. (2020) highlighted that bones and bone mass
streams from slaughterhouses are a valuable source of phosphorus recovery and

Fig. 13.2 Flow chart of recovery of macronutrients from food industry side streams. VSEP
vibratory shear enhanced processing, WTP wastewater treatment plant
382 R. Idrishi et al.

phosphate fertilizers can be made by pyrolysis of slaughter waste. Carcass rendering

from the meat industry can be a major source for N and P (Shurson 2020). Fish waste
side streams of the food industry when combined with few bulking agents provide a
valuable fertilizer enriched with N, P, and Ca whereas the waste keratin materials
(hairs, feathers, etc) provide a good source of N when digested with strong acids. K
can be recovered from waste streams of the feed and fodder industry.

13.6.1 Proteins

Out of the total global waste which is above 450 million kg/year, 10% is protein.
Side streams of industries like cereal, brewery, oilseeds, dairy, fish, and slaughter-
house are good sources of protein that can be processed into high-quality foodstuffs
or ingredients, before finally ending up into raw materials for the compost, fertilizers,
or biogas. Protein extracted from side streams of the food industry provides a
sustainable alternative approach to meet the protein needs globally (Schweiggert-
Weisz et al. 2020) but the process of separation is challenging in terms of texture,
taste, and increased bioavailability. Here are few examples of protein recovery from
side streams of major industries:
For recovery of proteins from slaughterhouse side streams like bone materials, the
important processing parameters are hydrolysis time, the liquid to solid ratio, and the
enzyme to substrate ratio. The liquid: solid and the enzyme: substrate ratio has a
significant role on protein recovery which is approximately 90% under optimal
conditions. From the fish industry side stream, the process is as followed:

Fish head bones

Enzyme assisted processing Solids, bones

Soluble protein and oil

From oilseed side streams, the protein recovery process is as follows:

Oilseed meal

Enzymatic treatment, carbohydrate degrading enzyme

Centrifugation fibrous residual mass

Supernatant

Membrane filtration Permeate: phytochemicals, minerals,

sugars, peptides

Protein concentrate
13 Upcycling Technologies in the Food Industry 383

The proteins recovered from wheat and rice bran streams are considered to be of
high quality (Schieber 2017). They can be used for rearing the insects as a promising
source of conversion of protein to protein (Ojha et al. 2020) and there are insects
(e.g. black soldier fly larvae) which in turn helps in the nutritional upcycling of agri-
food industry side streams and waste biomass which are reported to yield high-value
protein, chitin, lipids, and frass (the combination of undigested leftovers of the
substrates and organic refuse excreted by insects) (Ravi et al. 2020). The side stream
protein sources and processes for other industries and new products simply take into
account the following factors (Pihlanto 2019):
1. Adaptation of their industrial process with their production, transformation to
products.
2. Better knowledge of their health impact and their nutritional value.
3. Social acceptance of sidestream protein sources consumption.

Production of Single-Cell Protein, Edible Mushroom, or a Vegan Protein

Source
Agricultural and food industrial side streams can be used as a sole source of carbon
for the submerged propagation of mushrooms. Ahlborn et al. (2019) performed the
same with apple pomace in flasks which resulted in the biomass of mushroom-
substrate combination having a composition of 21% protein, lipids (4%), ash (2%),
and carbohydrates (74%) with dominating fatty and amino acids like linoleic acid,
glutamic acid/glutamine, and vitamin D. The production of single-cell protein,
microbial protein, and fungal cells (Matassa 2016) allows the use of food waste
side streams as a substrate which helps in secretion of enzymes such as cellulases,
amylases, pectinases, inulases, proteases, and lipases, into the surrounding medium
and hydrolyse plant polysaccharides (e.g. cellulose, starch, pectin, inulin), proteins,
and lipids (Meyer et al. 2020). Extracted enzymes like phytase, amylase,
β-glucanase, and xylanase are added to cereal-based diets to increase the utilization
of dietary phosphorus, starch, beta-glucans, and arabinoxylans (Ghorai et al. 2011).
Souza Filho et al. (2018) developed a vegan-mycoprotein concentrate from a
pea-industry by-product using edible filamentous fungi with potential application
in human nutrition in terms of enhancement of protein content in the final product.

13.6.2 Carbohydrates

The cereal bran in general is a good source of bioactive components, for example,
non-starch carbohydrates, polysaccharides and oligosaccharides, phenolic com-
pounds, lipid-soluble vitamins and folic acid, and phytosterols. The valorization of
carbohydrate-based food waste streams results in the production of lactic and
succinic acids and ethanol, recovery of ferulic acid for subsequent conversion to
vanillin, protein extraction for the production of amino acids, and arabinoxylan
384 R. Idrishi et al.

extraction which can be used for probiotic encapsulation as a potential application.

Wheat bran contains high-quality proteins, which may be used for the fortification of
foods and the production of amino acids and biologically active peptides. Valuable
compounds present in rice bran include proteins, lipids, dietary fibre, minerals, and
antioxidants such as vitamin E and oryzanol (Schieber 2017). The olive oil pomace
also contains mainly carbohydrates, polyphenols, minerals, and residues of lipids
and thus can be recovered from the same taken into account that the recovery of these
nutrients from wastewater generated from olive oil production facility is not easy
because of acidic nature and black coloured stream (Schieber 2017).

13.7 Smart Technologies for Upcycling Side Streams

1. Using insects as biofactories for nutritional upcycling of food wastes: The

upcycling of nutrients with insects guarantees a consistent macronutrient profile
that can be incorporated efficiently in feed formulations (Ravi et al. 2020). This
technology of upcycling is gaining popularity with the rise of interest in the
alternative protein sector. One such case study is of OrganicFe Co. (Indonesia)
which utilizes black fly larvae waste stream obtained from vegetable and fruit
supply chain to produce organic fertilizer and maggot flour which is again sold to
crops supply chain, poultry, and fish chain which helps in maintaining a circular
economy (Nattassha et al. 2020).
2. Using agricultural and food side streams as biomass for the cultivation of other
valuable products: Valorization of the substrates obtained from the side streams
offers an additional advantage to the yield in the form of enhanced nutrition
(Ahlborn et al. 2019) when compared with usual cultivation methods. This can be
used for microalgae-based processes (Acién Fernández et al. 2018), cultivation of
single-cell protein, edible fungi, lab-grown meat (Matassa 2016), and its vegan
alternatives such as mock meat, fish, and dairy products.
3. Fuelling biogas reactors: Organic wastes and residues from side streams from
agri-food processing industries (such as animal by-products from abattoirs,
brewers spent grains, etc.) act as a principal substrate combining with the
digestate of biogas plants that comprises of other raw materials like animal
manure, slurries and crop residues (Drosg et al. 2015).
4. Upcycling by-products and side streams to develop sustainable packaging: The
presence of bioactive compounds in agro-industrial by-products elicits their
application in developing renewable and biodegradable biopolymers with signif-
icant antimicrobial properties (Dilucia et al. 2020). Moreover, several waste
streams act as substrates for their bioconversion into bioplastics such as
polyhydroxyalkanoates (PHA) (Yadav et al. 2019). This application renders the
physicochemical properties of food biowaste which is also used for converting
the waste into smart materials for food packaging and sensors (Halonen et al.
2020). Valorization of streams of acid whey, coffee mucilage, and rice husk can
be separately used as alternative feedstock for D-lactic acid production which in
13 Upcycling Technologies in the Food Industry 385

turn can be used for the synthesis of polylactic acid (PLA—a biodegradable
polymer) which is used for the development of biodegradable packaging mate-
rials for food, cosmetic, and pharmaceutical industry (Alexandri et al. 2019).
5. Thermal processing of waste streams into animal feed: Conversion of waste
streams into animal feed can be done by adequate thermal processing of waste
streams in order to avoid disease outbreaks due to pathogen transmission.
Shurson (2020) in their study highlights that Japan, South Korea, and Taiwan
have developed a well-established substantial infrastructure with a conversion
rate of 35–43% waste streams into pig feed. This technology is very much
efficient in converting food waste streams into swine and poultry feed.
6. Valorization of Carbohydrates, Proteins, and Lipids present in food and agri-
cultural waste streams using immobilized enzyme system: Along with proteins
and lipids, various carbohydrate derivatives (monosaccharides, disaccharides and
polysaccharides) can also be valorized by using immobilized enzymes. The
selection of immobilization methods, source of enzymes, and implementation
of enzyme systems is carried out with regard to cost efficiency. The suggested
means are using purified enzymes, inexpensive carriers along with immobilized
enzyme systems that utilize whole-cell or crude extracts, genetic modification of
enzymes that enables site direction, and trying multi-enzyme systems (Andler and
Goddard 2018). The designed biocatalysts by using immobilization methods have
the potential to improve sustainability of the food industry through the creation of
value-added products and have a positive impact on reducing food waste gener-
ated from food processing waste streams.
7. Conversion of waste oil into biofuels by using enzymes: Transformation of waste
frying oil into biofuels can be done via transesterification which produces soap
when a chemical catalyst is used in this method and to avoid the same lipase
enzyme transesterification is done which prevents soap formation and reduces
by-product formation. This method promotes green processing conditions which
is active in solvent-free conditions (Andler and Goddard 2018; Vescovi et al.
2016).

13.8 Conclusion

Valorizing heavy and bulky wastes from the food and agricultural industries is a
complex, energy-consuming process. Thus, the upcycling of food waste biomass
maintains a circular economy and is an important step towards food sustainability
through which macronutrients, micronutrients, and substrates can be obtained. To
eradicate the food wastes and rescuing them from going into vain, several initiatives
across the globe have come forward to upcycle those foods along with the objective
of formulating innovative and health-oriented products which can be from any
stream of the food chain, be it from the organic leftovers of fresh fruits and
vegetables, scraps of cereal grains, by-products of the dairy industry. The remnants
of foods including peels of fruits and vegetables have been found to transform into
386 R. Idrishi et al.

edible coatings, referring to the natural biopolymers. Therefore, food waste stream
upcycling technologies are escalating not only to reduce waste but also for recycla-
ble approaches such as sustainable processing and packaging along with preserva-
tion of various kinds of perishable foods having a limited shelf life. The future
implication of utilizing such upcycling technologies lies in directing towards a route
of achieving sustainable attributes, minimizing food wastage along formulating
value-added products, and more emphasis should be given to the treatment of food
waste streams along with a mixture of other substrates and by using different
methods.

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Chapter 14
Sustainable Value Stream Mapping
in the Food Industry

Himanshi Garg and Soumya Ranjan Purohit

Abstract Food wastage and loss are the biggest issues of the industry due to poor
inventory practices and management approaches. To achieve sustainability, a com-
prehensive approach is required in product designing, manufacturing processes and
systems, and the entire supply chain. Green and lean manufacturing are the two
holistic approaches to deal with waste, pollution, and sustainability issues in the food
industry. Further green manufacturing is efficient in the management of raw mate-
rials, energy usage, process, health, safety, and waste concerns all of which are
necessary for achieving socio-economic and environmental sustainability goals.
Thus, this chapter presents the integration of lean and green manufacturing, Internet
of Things (IoT) integration with lean management, case studies focusing on Sus-
tainable Value Stream Mapping (SVSM) establishments in the food industry with
smart lean practices. Further, the chapter describes challenges in the application of
smart lean principles and future perspectives of implementing this system.

Keywords Sustainability · Lean manufacturing · Value stream mapping · Life cycle

assessment · Zero defects in food manufacturing

14.1 Introduction

Industries are critical to the global economy. Many different types of resources are
used and discarded during the manufacturing process, resulting in waste generation,
carbon emissions, environmental pollution, and ecological deterioration. Several

H. Garg
Amity Institute of Food Technology, Amity University Uttar Pradesh, Noida, Uttar Pradesh,
India
S. R. Purohit (*)
Amity Institute of Food Technology, Amity University Uttar Pradesh, Noida, Uttar Pradesh,
India
Food Engineering and Technology, Tezpur University, Tezpur, Assam, India
e-mail: srpurohit@tezu.ac.in

© The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2022 393
S. Sehgal et al. (eds.), Smart and Sustainable Food Technologies,
https://doi.org/10.1007/978-981-19-1746-2_14
394 H. Garg and S. R. Purohit

steps have been taken to increase manufacturing efficiency and resource utilization
to accelerate the transition to sustainable manufacturing and a circular economy.
Increasing material utilization necessitates that businesses consider various methods
of reducing, recycling, and reusing their raw and waste materials, as well as
extending the life cycle of their products. However, with the growing interest of
industries and consumers in sustainability, global industries are targeting to double
the output with 50% fewer resources and only 20% of current carbon emissions
(Hedlund et al. 2020). Also, it is the social responsibility of industries to use
eco-friendly methods to reduce the burden on the environment and waste generation.
In this regard, one of the widespread philosophies used in the manufacturing
industries is lean manufacturing, which corresponds to a system that addresses a
range of practical management applications. The main goal of lean production is
eliminating waste, reducing cost, and increasing efficiency.
Subsequently, Value Stream Mapping (VSM) is considered as a lean method for
efficient processes by identifying energy, cycle times, downtimes, delays, waste, and
material flows. VSM is also a potential to evaluate value creation throughout the
entire value chain from raw material collection to finished product. However, due to
societal and environmental constraints and emerging new possibilities from the
circular economy, VSM is not an eco-friendlier solution.
Lean manufacturing practices are increasingly being evaluated and used as a
catalyst to develop better green and sustainable manufacturing strategies (Mahlmann
Kipper 2018). Therefore, finding the potential of Value Stream Mapping has found
great attention in identifying environmental and societal impacts/waste (Faulkner
and Badurdeen 2014). LCA is the most objective tool in environmental practices
because it can assess a system’s potential environmental impacts (Bhatt et al. 2019).
Manufacturing companies must understand the environmental impacts of their
products at each stage of production. A life cycle perspective can help manufacturers
identify potential improvements throughout the industrial system and at all stages of
the product life cycle. The lean improvement process can be focused on specific
environmental improvement actions by combining LCA and VSM. It also provides
immediate benefits in terms of tracking the environmental effects of lean improve-
ment initiatives. As a result, this chapter discusses LCA and VSM integration, smart
and lean manufacturing in the food industry, and case studies focusing on
sustainable VSM.

14.2 Lean Manufacturing

Lean means “downward slope” in the graph, while management means “planning
and controlling processes systematically.” This term has been initially developed for
the automobile industry. It can be regarded as a system approach that necessitates the
collaboration of all value chain actors with the common goal of increasing customer
satisfaction (Halloran et al. 2014).
It is a set of principles for reducing waste, time, defects, and other unimportant
aspects of food manufacturing. Furthermore, despite its origins in the manufacturing
14 Sustainable Value Stream Mapping in the Food Industry 395

Defects
Materials, labor
and time used in
non – compliant
products
production Transport
Overproduction
Transport of materials
Production more
across the locations in
than the required
non- value adding

Unnecessary 7 Types of
Inventory Waste Movement
Working capital and Unnecessary
space used for raw movement of
materials, components, person and
work in progress machinery
and finished
products.

Overprocessing
Waiting
Oversized equipment,
Delay in meeting
unnecessary
customer demands
technology
utilisation

Fig. 14.1 Seven types of waste identified in industry

industry, lean can be applied to any process-driven environment, regardless of

industry. The ultimate goal of lean is to identify and then eliminate or change any
part of a process that does not add value. Lean enables faster responses to changing
customer demands, resulting in more robust production, higher quality, and lower
costs. However, due to the perishability of a wide range of food products, the
complexity of the agri-food supply chain, and dynamic consumer preferences, its
penetration into the agricultural sector has been slow (De Steur et al. 2016; Dora
et al. 2016). It entails identifying the seven lean wastes presented in Fig. 14.1.
Five principles should be followed to reduce these waste forms:
• specify the worth, where the worth must be specified in terms of quality, time, and
price from the customer’s perspective;
• locate the value flow along the value chain by identifying value streams;
• make the value flow in an unbroken stream wherever and whenever possible;
• allow customers to pull value from the end of the value chain rather than stocking
to avoid unnecessary waste;
• strive for perfection through constant improvement.
396 H. Garg and S. R. Purohit

14.2.1 Value Stream Mapping

Value Stream Mapping (VSM) is defined as “a tool that helps in seeing and
understanding the materials and information flow of a product as it makes its way
through the value stream” (Liu et al. 2020). Poor quality, reduced productivity, and
increased costs are resultant of unawareness regarding waste types. A few examples
of waste identified in the food industry are presented in Table 14.1.
Conducting a VSM enables identification of challenges, where time is wasted on
non-value-added activities. Therefore, VSM can be a potential tool in industrial
applications, which can help in the visualization of the interdependence between
processes, improving the effectiveness of value chain analysis by enhancing con-
sumer value at each stage, boosting food production and service (Ziara et al. 2018),
minimizing wastes in convenience food manufacture and improve the efficiency of a
food contract manufacturer.

Table 14.1 Hotspots requiring VSM application

Targeted Targeted
industry step Problem identified Solution Reference
Canned peach Peeling Energy-consuming steps Using steam in Folinas et al.
processing Pasteurizing Increased chemical load in place of lye peel- (2015)
wastes and production ing
cost Condensed steam
and insulation of
pipes will reduce
energy loss
The neutralization
step could be
omitted
Bread Cooling and Breakage during slicing Use first in first Goriwondo
manufacturing slicing due to improper cooling out (FIFO) tech- et al. (2011),
Inventory hence generating waste nique for the raw Sathiyabama
Motion Improper line balancing and finished and Dasan
Storage Manual work product (2013)
Poor and overtopping, Line balancing to
overbaking, nutrient loss, reduce unneces-
variation in size/shape sary motion
Overproduction Manual slicing
Excess raw or finished was replaced with
product semi-automated
Product defect slicing
Short shelf life Proper inventory
Food service/ – Menu and nutritional A menu detailing Ahmed et al.
hospitals considerations; food pro- with ingredients (2015)
curement; food produc- Patient
tion; foodservice and satisfaction
patient expectations
14 Sustainable Value Stream Mapping in the Food Industry 397

Fig. 14.2 Steps involved in

VSM establishment

14.2.2 How Does a Value Stream Map Look like?

The idea of VSM is to be able to visualize an operation completely, showing how

value is added to a product at each and every step against a timeline. Though there
are certain similarities between VSM map and a process map, however, distinctive
features in VSM are as follows:
• Presence of a timeline
• The data boxes, which are used to collect data needed from each process
VSM is a method in which the present and future state of a product/process are
first mapped. Once both the current and future states have been mapped, the third
step in the VSM method is to create an action plan that will lead to the future state as
presented in Fig. 14.2.
For a long time, organizations are prioritizing processes and value chain devel-
opment (Hedlund et al. 2020; Porter 2011). To be more competitive there is a need to
improve efficiency. Thus, VSM can assist enterprises in identifying and reducing
waste while also improving value creation. This could be accomplished by viewing
the entire process through the eyes of a system. Waste reduction can take many
forms besides traditional seven wastes. However, VSM has not yet systematically
incorporated the principles of circular economy, environmental impact, and energy
consumption.

14.3 Sustainable Value Stream Mapping

Norton and Fearne (2009) and Simons and Mason (2002) have proposed a method
termed as Sustainable Value Stream Mapping (SVSM) to ensure sustainability by
taking carbon footprint into account with due importance to time for value-addition.
398 H. Garg and S. R. Purohit

Conversely, SVSM approach has been applied to understand carbon dioxide emis-
sions through the supply chains for cherries, apples, and lettuce. When attempting to
improve the sustainability of a supply chain, it is critical to include the process steps
and to measure other performance indicators in addition to CO2 emission (Norton
and Fearne 2009), which gives a complete idea of carbon footprint.

14.4 Applications of VSM and SVSM in Industry

Implementation of lean principle with compromised weightage to impact of envi-

ronmental performance may not be effective in ensuring sustainable processing and
supply chain. Researchers have examined the impact of lead time compression on
CO2 emissions using a simulation model (Norton and Fearne 2009). They have
reported that a lean supply chain may lead to higher CO2 emissions. For example, in
a refrigerated/frozen product supply chain, the lean principle suggests frequent
deliveries of smaller quantities to reduce storage and refrigeration burden on the
manufacturer side. Holding smaller stocks ensure less electricity consumption and
associated greenhouse gas emission. However, the frequent deliveries do contribute
to CO2 emissions and may aid to overall increase in CO2 emissions. Thus, there
should be an optimal order size within any individual supply chain that balances
inventory level and delivery frequency to ensure the lowest CO2 emissions. How-
ever, CO2 emission is just one aspect of the environmental performance of a supply
chain.

14.5 Integration of LCA and SVSM

Industrialization has altered society and its interactions with the environment by
increasing natural resource usage and the rate at which new products and processes
are developed. LCA is an environmental assessment tool that investigates potential
environmental impacts of products and services through the whole life cycle from
“cradle to grave.” The environmental impacts of a product are assessed at every
stage, from raw material extraction to materials processing, manufacturing, and
distribution, and finally disposal or recycling. An LCA (Life Cycle Assessment)
study consists of four main phases:
• Target and scope of assessment: It articulates the background of the assessment
and ensures technical information essential for the assessment.
• Life Cycle Inventory (LCI): It involves the entire inventory like water, energy,
material, etc. in an inventory flow form and also includes various discharges and
releases to the environment.
• LCIA: Life Cycle Impact Assessment (LCIA) identifies and evaluates the degree
of potential environmental impacts and whether they are under ISO standards.
14 Sustainable Value Stream Mapping in the Food Industry 399

• Interpretation Phase: Identifies, quantifies, and evaluates the results obtained

from LCIA.
In a study, Vinodh et al. (2016) recorded the environmental impacts using a
sustainable value stream map having parameters like acidification, carbon footprint,
eutrophication, and total energy consumed. Based on that a future state process maps
were modeled for the sustainable performance of the industry. Further detail on this
can be accessed through bibliographic information at the end of the chapter.

14.6 Lean Manufacturing in Food and Beverage

The word lean means a slope indicating a downward trend in a graph. Whereas
management means planning and systematically controlling different processes. It
deals with reducing waste, time, defects, and other negative quotients related to
various manufacturing processes and has been functioning in the food industry for
decades. It is an intricate and detailed process involving factors like scientific
management, industrial engineering, machine automation, and supply chain man-
agement. To fully grasp this concept, it is crucial to understand the four key
components of lean management which are as follows:
• Pull: It means the raw material supply must be generated only when there is
demand from the industry, hence helping in reducing wastage and shortage of
limited resources.
• One-Piece flow: It focuses on one product production at a time to improve quality,
reduce hurdles and wastage of energy.
• Pulse: It focuses on increasing efficiency and productivity with better time
management of production units.
• Zero Defect: This key focuses on the elimination of errors from the root. Or in
other words, it is the reverse approach of quality control and assurance.
The food industry deals with various challenges in meeting consumer demands
like readily perishing items that are not easy to transfer and store. However, the most
dominant areas of lean management implementation in the food industry include:
• The Warehouse: The place where raw materials are collected. With the help of
LM process and special storing procedures lesser waste of raw material, longer
preservation, and better results could be achieved.
• The Production Line is where humans and machines perform various activities
like cutting, mixing, peeling, cooking, brewing, and packaging. The automation
due to Lean Management has helped in reduced time and increased output.
However, better precision throughout production (in terms of defects and
record-keeping) has been achieved with AI and software integration in the food
industry.
400 H. Garg and S. R. Purohit

• Quality Assurance: With the implementation of LM at operation units the product

quality is no longer dependent upon merely tossing out the defective batches of
food and beverages. It depends on finding the root cause.
• The Logistics: LM has aided the supply chain and food industry in the develop-
ment of packaging for long-term and cost-cutting purposes.
• The Costs: Implementing lean management in the food and beverage industry
means producing more products in less time, storing them better, and improving
logistics. As a result, manufacturing costs decreased.
Hence, in a nutshell with the implementation of LM in the food industry based on
its broad areas of application, practical implementation, and tremendous advantages
have the most prominent benefits which are as follows:
• Less Waste: Raw material and finished product waste is reduced as a result of
improved warehouses, limited production, and more advanced manufacturing
techniques.
• Preservation of Limited Resources: Resources such as freshwater, fruits and
vegetables, electricity, and labor are scarce. Lean management aids in the pres-
ervation of these resources and the creation of more utility from them.
• Demand-Based Production: Selective production in response to viable demand
has enabled manufacturers to save money and reduce stock losses.
• Enhanced Productivity: Lean management has aided food scientists and engi-
neers in producing better products in less time. As a result, overall productivity
has increased.
• Efficient Logistics: Because of lean management, raw materials last longer, and
products are produced in response to demand. It has also helped to reduce supply
chain lags.
• Higher Quality: With lean management, the root causes of defects are addressed.
As a result, the possibility of defects has been reduced to a bare minimum.
• Lesser Prices: At every stage, lean management has revolutionized the food and
beverage manufacturing industry. When production costs fall, consumer costs fall
as well.
In the last decade, lean management has become an essential component of the
food and beverage manufacturing process. This system’s innovations and solutions
have benefited all stakeholders from farm to fork or throughout the supply chain.

14.7 Smart Lean Manufacturing in Industry

No doubt, to meet consumer demand and stay competitive in the market,

manufacturing companies look for new alternatives for the betterment. In this regard,
lean manufacturing has been in the role for the last 20 years which is a simple and
less technical approach. But with the increasing demand for customized food
products, strict competition, 360 stakeholders have led to the integration of IT,
14 Sustainable Value Stream Mapping in the Food Industry 401

Table 14.2 Lean manufacturing and smart lean manufacturing

Lean manufacturing Smart lean manufacturing Reference
Decentralized control Centralized database Åhlström
Focus on transparency Disconnect reality and abstract et al. (2016)
information
Weak and simple Rigid, complex, and robust
Addresses problem from the root cause and Encourages workarounds rather
authenticate employees to take action than addressing the root cause

sensor-equipped, self-configured digitalization of the food industries using cheaper

and powerful networking sources such as wireless technology, cloud computing, big
data, and artificial intelligence. All this led to the introduction of Industry 4.0 which
targets the automation and digitalization of manufacturing companies (Buer et al.
2020; Shahin et al. 2020). LM and smart LM can be presented in Table 14.2:
If the contrasting statements presented are ignored, then the objective of lean
manufacturing and Industry 4.0 is the same to improve the industry performance.
Lean efficiency and establishment are being hampered by the complexity of
manufacturing systems, changing global market trends, customer behavior, and
short product life cycles. In this regard, manufacturers are increasingly focusing on
the use of industry 4.0-based digitized techniques to improve operations while
ensuring complete customer satisfaction (Netland 2015; Ramadan and Salah
2019). The lack of real-time monitoring of the systems is challenging in this regard,
as production systems are dynamic and difficult to capture matters (Metz et al. 2012).
Furthermore, in traditional lean environments, workers do not always follow lean-
based established instructions due to a lack of real-time mechanisms that improve
lean instruction practice. As a result, there is a scarcity of established systems with
real-time monitoring that ensures lean manufacturing tools, particularly in the
context of Industry 4.0 (Ramadan and Salah 2019). As a result, researchers con-
cluded that Industry 4.0 and lean manufacturing complement each other conceptu-
ally, and then described how Industry 4.0 can support specific lean tools and
practices to achieve lean targets (Mayr et al. 2018).
To emphasize the interaction between lean manufacturing and Industry 4.0,
Satoglu et al. (2018) attempted a methodology that guides Industry 4.0 within the
context of lean manufacturing. Industry 4.0 ensures capturing real-time data from
smart entities engaged in manufacturing processes for control and analysis for
making quick and efficient decisions. Hence, a smart lean-based Industry 4.0 or
lean-based smart factories framework called Dynamic Value Stream Mapping was
introduced (Ramadan 2016). It was a real-time RFID-VSM system that interacted
with processes, people, materials, and any other constraint relevant to the
manufacturing situation. It enables managers to make the right decision at the right
time depending on the real situation, where laborers will make changes to processing
capacity, labor requirements, flow, and cell layout, to advance and plan for the future
state. In the further section, the concept of LM and Industry 4.0 will be discussed in
402 H. Garg and S. R. Purohit

Table 14.3 Integrated Industry 4.0 and LM approaches

Integrated
system Application Results Reference
Mobile Completing checklist Enhanced productivity Demirkol and
devices + Getting, meeting, and tracking and increased revenues Al-Futaih (2020),
automated orders for the companies Morkos et al.
production Real-time observing and opti- (2012)
mizing production areas and
environment
IoT and Enables machine function Improve operational Cheruvu et al.
machine without humans like sensors, efficiency and perfor- (2019)
networks, APIs, data mance in manufacturing
Human to Intelligent manufacturing with Better operations and Ma et al. (2019)
machine effective information transfer faster production with
interaction and feedback zero defects
GPS Package, delivery tracking, Efficient and timely Demirkol and
immediate response, inspec- delivery Al-Futaih (2020)
tions, resource control
RFID and Monitor, diagnose, and control Improve and enhance Zou (2016)
smart sensors data manufacturing
3D printing Additive manufacturing of Fast and inexpensive Muthurajan et al.
snacks and different food items manufacturing, reduced (2021)
with personalized nutrition waste
Big data ana- QR scans Compact and predict- Yiannas (2018)
lytics and able methods
blockchain Better traceability
Cloud A large number of users can Reduced production Wang et al. (2017)
computing access data at a time like mon- cost
itoring High performance, pro-
Modeling process design ductivity, reliable, and
secure

more detail. Integration of Industry 4.0 with lean management has found various
applications in the food industry as presented in Table 14.3.
High-quality sensor products, particularly viscosity sensors, hardness, surface
finish, configuration, and color, are in high demand in industrial automation. And
this is subject to stringent quality controls (Schütze et al. 2018). Smart technologies,
when combined with factory systems and supported by accelerated intelligent sensor
technology, have a profound impact on the performance of the industrial system,
ultimately leading to high quality, flexibility, and productivity in manufacturing
systems. A new RFID and wireless technology-enabled real-time VSM has been
implemented in a food chain inventory and logistics, resulting in time savings, error
reduction, waste reduction, and increased consumer trust. It also enables managers to
make more accurate and timely decisions (Chen et al. 2021). Another study proposed
a new framework called “VSM 4.0,” which combines traditional VSM with an
innovative data collection and handling system (Meudt et al. 2017). In the next
section, various real-time studies and examples are discussed where VSM has been
established or taken care of in a lean and sustainable way.
14 Sustainable Value Stream Mapping in the Food Industry 403

14.7.1 IoT and Lean Concepts in Food Industry

IoT has a huge impact on the food industry and a combination of IoT and lean
concepts can help in bringing positive changes soon. Following are the applications
of the IoT in the food sector in Fig. 14.3.
• Equipment Management
IoT devices could alert or solve the problem of equipment maintenance before
it becomes a major issue, saving time and money on routine maintenance. For
example, in the hospitality industry, if a device is about to defer, the IoT’s
signaling and reform features would notify the owner. This is critical because a
disruption in the workflow results in a loss of market reputation as well as
customer satisfaction. During these days, the income also decreases or changes.
• Intelligent Refrigerators
In the food sector, the refrigerator plays a crucial role from storing leftovers to
fresh and perishable foods like meat, fruits, etc. Further, different food requires a
different storage temperature. While fragrance and flavors should also not be lost
with time. Thus, it is critical to certain refrigerator conditions. Here, IoT integra-
tion will not only improve food conditions but also preserve nutrients, thus,
preventing wastage and time.
• Reduce the energy consumption
Food industries use hefty machines having huge power consumption. Thus,
IoT will help in saving time and energy. For example, if a worker left an oven on
after removing the food from it, then sensors installed could help in getting it off
on its own or may alert to switch it off, thus help in maintaining cost.

Fig. 14.3 IoT and LM in food industry

404 H. Garg and S. R. Purohit

• Stock management
The application-based IoT helps in tracking and managing stock in ware-
houses or restaurants. These applications are wireless systems that could be
operated from mobiles or tablets and could alert the owner about the requirement
or status of any item in stock so that advanced orders could be placed resulting in
inefficient operation and reduced waste.
• Oven designing
Using a sensor, the temperature of the oven could be controlled, thus
preventing overcooking, food burning, and oven damage. These sensors may
notify the handler about the food condition in the oven like the temperature is
optimum or not. In case of no response from the other end, the oven will switch
off automatically.
• Reduce logistics charges
With the help of technologies, the real-time monitoring of the supply chain is
achieved, hence resulting in reduced transportation and logistics costs.
• Data analytics report
To know about the direction of improvement for any industry, data needs to be
tracked and market trends need to be considered. This indicates that customer
feedback and demand records need to be maintained. Thus, here, the Internet of
Things plays a critical role as it helps in tracking the data and records of the food
chain.
• Food safety regulations
To meet the quality regulations setup for the food industry IoT enabled
recorders are required to ensure premium food quality.
IoT is the game-changer of every field. Even in recent times, the globe has
sustained the food requirements of the population. This has become possible only
because of the advent of IoT in the food sector. Though, privacy and complexity
are the major drawbacks of IoT. These fields, however, can be covered with
proper technical research and applications.

14.8 Various Lean Concepts Based Case Studies

14.8.1 Just in Time

Nowadays, the fast-food service industry is expanding, becoming more competitive

and diverse. The fast-food industry is dealing with two major issues. The first is that
sales are slowing and operating costs are rising. Furthermore, customers are in high
demand, and the services they receive are becoming increasingly selective. As a
result, restaurant managers must understand how to maintain market profitability
while providing more efficient and high-quality services to the target market.
As fast-food restaurants are unique operational systems designed to provide
customers with the fastest and most responsive services, they must focus on these
subsystems (input, output, and process) and their interactions to improve efficiency,
14 Sustainable Value Stream Mapping in the Food Industry 405

quality, and responsiveness. Just-in-time (JIT) inventory is the current operation

trend. It is a method of providing supplies to customers “just in time,” as the name
implies. It is also an inventory strategy that businesses use to improve efficiency and
reduce waste by receiving goods only when they are needed in the production
process, thus lowering inventory costs. JIT approach is used by numerous food
and beverage enterprises due to the short life span of finished goods. However, the
problem here is reliable real-time inventory management to prevent stock-out
problems. Here, IoT-based inventory systems could be installed where the managers
could be alerted before stock out using RFID tags, hence resolving stock out issues.
This could be understood from the example presented in the next section.
Another implementation of the JIT approach is adopted by the PICNIC system
which delivers groceries fresh, hence reducing food loss problems found in groceries
shops. Perishable goods (meat, apples, avocados) become part of a dynamic supply
chain and are never stored for extended periods. This shortens the time between
harvest and consumption, ensuring maximum freshness for all products ordered by
the customers. Running a just-in-time supply chain, however, is a difficult task. It
necessitates in-the-moment coordination. From production to receiving orders from
suppliers, turnaround, and final delivery, every step must be coordinated.
PICNIC prepares a forecasting model using machine learning and AI networking,
which provides real-time visibility into supply and demand data and allows the
company to purchase only what customers want. This precision means that, unlike
traditional supermarkets, it does not keep an oversupply of goods, reducing waste
throughout the supply chain. Furthermore, when there is an oversupply during the
harvest season, the system is so adaptable and responsive that it collaborates with
suppliers to distribute the excess by offering deals to the customers. Additionally, the
system has well-equipped facilities to ensure freshness, well-designed loading and
unloading resulting in smooth flow throughout the supply chain. In this way, the
online store maximizes operation efficiency with precision and reduces food waste.
McDonald’s is one of the most well-known franchise fast-food restaurants in the
world. In its business operations, McDonald’s Fast-Food Restaurant has used the
just-in-time system. The just-in-time system has assisted McDonald’s restaurant in
lowering inventory costs and reducing waste. When using just-in-time systems,
McDonald’s can produce burgers in 90 seconds. McDonald’s products are available
pre-cooked. Customers can order the product and it will be manufactured. JIT is a
new technology that has been presented as a challenge to McDonald’s. McDonald’s
success mantra, however, is high flexibility or adaptability.
McDonald’s uses a make-to-order approach for production and order fulfillment
because customer satisfaction is important to the company. And a customer in any
restaurant expects quick service that is also of high quality and flavor. As a result,
McDonald’s considers the length of time it takes to deliver the order from the time
the order is placed; this is referred to as lead time. The greater the lead, the less
satisfied the customer, resulting in the customer not returning.
Conversely, McDonald’s is a big restaurant that has a billion customers and
employees. Therefore, the production speed is needed to fulfill the customer’s
need and they are the main choice of customers because of the service they provide
406 H. Garg and S. R. Purohit

to customers which actually attracts consumers to buy their product. Also, being a
brand, it maintains the quality of the burger using high techniques in processing,
resulting in better productivity. Just in time has helped McDonald’s to become more
sustainable in the fast-food industry.

14.8.2 Kaizen

The word itself is Japanese and a combination of two words. “Kai” refers to change,
and “zen” refers to good and better. As a result, the word “kaizen” can be translated
as “good change” or “change for the better.” Kaizen methodology is a method of
continuously improving work, work environment, and employees. The method’s
fundamental core revolves around employee collaboration and communication, as
well as the work they do together. Its primary goal is to reduce potential risks and
problems, increase productivity, instill a positive attitude in the workplace, and
innovate.
Building a Kaizen culture in an organization is a difficult task. Companies that
invest time and effort into studying and promoting it to their workforce, however,
reap a plethora of benefits that can only be obtained by assiduously incorporating its
tenets into daily work. And it is about more than just improving quality and lowering
waste. Kaizen, when properly implemented, can have a positive impact on every
level of your organization. Here are its most important benefits:
• Reduces waste like less downtime, less unnecessary movement, lead time, etc.
• Improves operation efficiency with higher equipment and resource utilization
resulting in higher productivity.
• Predictable production will result in higher delivery rates with higher customer
satisfaction.
• Happier, more productive employees as all staff are openly contributing to
continuous improvement.
• Ensures an open system for improvements.
• Successful both in the short and long term.

14.8.3 Nestlé and Kaizen

One of the most important industries focusing on implementing and applying the
Kaizen philosophy is the food industry. Nestlé is one of the world’s largest corpo-
rations, with products in a wide range of industries. Nestlé’s main goal in lean
production is to reduce waste. Because their products can have an impact on the
health of those who use (or consume) them, it focuses on reducing waste. This waste
consists of both food and plastic. Nestlé promises that by 2025, all of its packaging
will be recyclable and reusable. To accomplish this, the company invested $2 billion.
14 Sustainable Value Stream Mapping in the Food Industry 407

It also donates a large portion of the food produced in its factories to animal shelters
and people in need.

14.8.4 5S Methodology

The 5S strategy objective is to maintain excellent working conditions with contin-

uous improvement in a sequence throughout the processing, storage, and organiza-
tion. It helps the organization in meeting international standards with little effort and
cost. Even though it is a simple system, however, its implementation is not easy, the
reason being it should be accepted by employees in terms of attitude, commitment,
and involvement along with top managers.
The 5S and the explanation of the acronyms are presented below in Table 14.4.
When companies achieve the first 3S, they face the most difficult aspect of 5S,
which is trying to keep workplaces in excellent condition over time. To accomplish
this, businesses must standardize the rules defined in the preceding steps, which
should be done in collaboration with employees because they are the most knowl-
edgeable about their workplaces, equipment, and the most common problems/
anomalies (Lopes et al. 2015). This step should ensure that all rules are followed
so that organization, storage, and regular cleaning become habits, thereby preventing
the recurrence of previous bad habits (Patel and Thakkar 2014). In this regard,
regular inspections need to be performed and employees must be trained about all
aspects of the methodology to ensure they meet their responsibilities.

Table 14.4 5S Principles

Japanese and
Indian
meaning Role LM application References
Seiri (arrang- Distinguishing required from non-required Higher possible Kumar
ing properly) and eliminating the non-required utilization of et al.
workspace (2007)
Seiton (order Keeping the documents in places that are Product diversifi- Lopes et al.
lineness) easy and quick to trace as and when cation (2015)
required Reduced cost
Seiso Performed in parallel with organization Strict delivery Lopes et al.
(cleanliness) and order schedules (2015)
Ensures the workplace and machines are Improved safety
cleaned and are operating well High quality
Seiketsu To ensure the long-term cleanliness of the Patel and
(standardize) workspace some sets of rules are Thakkar
established (2014)
Shitsuke (dis- Developing a constant habit of maintaining Patel and
cipline or the establishing procedures Thakkar
sustain) (2014)
408 H. Garg and S. R. Purohit

With the successful 5S implementation following advantages are achieved by the

industry:
• efficient operation;
• organized, clean, productive, and safe;
• improvement of working conditions and employees value;
• a better view of problems;
• an embodiment of daily activities by employees;
• increased productivity, flexibility, quality, safety, and motivation of employees;
• cost savings, unproductive time, space, and movements; and
• cost savings related to failures and breaks.
In the next section, we will discuss the SMED implementation in another
company with its effect on production.

14.8.5 SMED

Single-minute exchange of die (SMED) aims at systematically reducing changeover

times, ideally to single-digit minutes. Changeover time is the period between two
good products coming out of a machine where the second product is from a different
production order—activities performed during this time are usually non-value
adding. Changeover can be divided into three main periods:
(a) run-down or clean-up, removal of material remaining from previous production
and cleaning;
(b) setup, physically converting machines to enable producing new products; and
(c) run-up or start-up when steady-state manufacturing is re-established, meeting
required productivity and quality rates, which typically includes adjustments and
quality checking.
Rapid changeover is critical for reducing lot sizes and thus improving flow and
manufacturing flexibility. These are important aspects in business because they
measure efficiency and competitiveness and are an effective way to reduce costs,
which are the main benefits of SMED (Lopes et al. 2015). Furthermore, the company
lacks standards or documents that explain how changeovers should occur, operator
training, variation in operations sequence and working methods, no coordination,
insufficient tools, and equipment calibration and adjustments.
As change over time has been divided further into three main periods, thus,
SMED has been extended to address these periods (Ferradás and Salonitis 2013).
Further, SMED focuses on improvements in the organization as well as manufactur-
ing equipment design (Cakmakci 2008).
SMED has been successfully implemented in various industries; however, in the
food and beverage industry it has been limitedly explored. The three main stages of
the SMED methodology are presented in Table 14.5.
14 Sustainable Value Stream Mapping in the Food Industry 409

Table 14.5 SMED in food industry

Stage Description Reference
Separate Crucial step Ferradás and
Classify activities as external or internal based on the possi- Salonitis (2013)
bility of performance in-house setup
Video recordings and routing diagrams can be used
Classify the change over time as well
Convert Analyze the classified internal activities for any error Lopes et al.
Attempt the internal activities and convert them to external (2015)
using equipment design improvements like, standardizing
tools and using intermediary jigs
Streamline All change-over aspects must be streamlined and simplified Ferradás and
Systematically improve all operations by reducing adjustments Salonitis (2013)
and eliminating.
Implement parallel operation and use tools efficiently

All the three steps must be evaluated to ensure most time-improving and cost-
efficient measures are employed.
The SMED implementation in the bottle manufacturing industry presented the
reduced change over time loss like earlier it used to perform 30 steps which on
SMED implementation came down to 20 resulting in 23% improvement. From the
above case study, it could be observed that with the implementation of SMED in the
food industry not only productivity will improve but also production flexibility,
employee engagement, motivation, and continuous improvement will be observed
which are critical for successful lean implementation.

14.9 Future Road Map

The food loss and waste along the supply chain are observed either in the form of
discard or nutrient loss. Discarded food is comprised of inappropriate processing,
overproduction, and defects as per lean principles. In concurrence with the above-
said defects, the non-conformance to standards is also explicitly highlighted in the
food industry like size, weight, shape breakages, and shelf life of the product
(De Steur et al. 2016). Additionally, if equipment and operation are not standardized
it may result in loss and wastage during processing (Papargyropoulou et al. 2014),
thus pointing to the need for process controls not only in processing but also
throughout the supply chain to achieve holistic waste reduction (Mena et al.
2014). Excess food stock be it raw or prepared due to poor demand forecasting is
a problem of both the world. Lean manufacturing, the just-in-time principle facili-
tates production based on demand, hence preventing overproduction and
overstocking (De Steur et al. 2016).
Critical awareness regarding consumer behavior wants and choices beforehand
could help food chain actors in predicting target markets. Moreover, instead of
discarding surplus food management should be followed like donating food, thus
410 H. Garg and S. R. Purohit

contributing to a noble cause of food insecurity (Garrone et al. 2014). Similarly,

there should be an emphasis on food waste causes and reusing approaches among
consumers, employees of the food industry, thus requiring considerations for the
consumption level as part of the supply chain.
Processing techniques may have a profound effect on the nutrient content of food.
For example, overbaking and pasteurization could result in loss of essential heat-
labile micronutrients such as thiamine, vitamin A and C. Besides heat, oxygen and
light could also result in nutrient loss, rancidity problems, etc. Even cutting, peeling,
and milling followed by washing result in losses (Atungulu and Pan 2014).
All the above-mentioned points imply that VSM is effective in identifying both
nutrient loss and waste. However, innovative strategies and methodologies are
needed to be developed that integrate both types of losses along the supply chain,
as the current evidence shows that both kinds of losses could be attributed to similar
causes. Further, single food products and food companies are considered which
could be problematic for generalizing the concept. Further, today’s need is for
qualitative and quantitative approaches. It is important to include supply chain actors
in SVSM approaches. Furthermore, performance indicators need to be established
with full quantification as observed in the case of the coffee sector case study. So
that, lean implementation effects could be observed.
There have been few empirical studies conducted from farm to fork. Although
previous studies have mentioned the need to address this issue holistically, they have
failed to move from posturing to application. There is a need for a multi-stakeholder
approach and further highlight the mitigation potential of SVSM. Another concern is
establishing possible links of nutritional value with food loss.

14.10 Conclusions

Lean implementation in the agri-food industry is still growing; the potential of

SVSM, VSM, Kaizen, JIT, 5S, and SMED has been demonstrated in this chapter.
Regardless of challenges, the integration of IoT and lean management has been
shown to improve the visibility of the entire value stream and consequently creates
an opening for information sharing required for an integrated food system. Lean and
green practices integrally improve production efficiency by reducing production
costs and waste which could favor the vulnerable and hungry population. Further,
future scientific research could be extended to the application knowledge of SVSM,
lean principles with IoT into an unexplored and complementary approach, with the
potential to sustainably enhance production with minimized food wastage, better
operation efficiency, full employee involvement, and betterment.
14 Sustainable Value Stream Mapping in the Food Industry 411

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