processes

Article
Optimization of Enzyme-Assisted Extraction of
Flavonoids from Corn Husks
Antonio Zuorro 1, * , Roberto Lavecchia 1 , Ángel Darío González-Delgado 2 ,
Janet Bibiana García-Martinez 3 and Pasqua L’Abbate 4
1 Department of Chemical Engineering, Materials and Environment, Sapienza University, 00184 Rome, Italy;
roberto.lavecchia@uniroma1.it
2 Chemical Engineering Department, University of Cartagena, Cartagena 130015, Colombia;
agonzalezd1@unicartagena.edu.co
3 Department of Environmental Sciences, Universidad Francisco de Paula Santander, Av. Gran Colombia No.
12E-96, Cúcuta 540003, Colombia; janetbibianagm@ufps.edu.co
4 Department of Civil, Environmental, Building Engineering and Chemistry, Polytechnic University of Bari,
70125 Bari, Italy; pasqua.labbate@poliba.it
* Correspondence: antonio.zuorro@uniroma1.it; Tel.: +39-0644585598




Received: 1 October 2019; Accepted: 29 October 2019; Published: 3 November 2019


Abstract: Corn husks are an important byproduct of the corn processing industry. Although they are
a rich source of bioactive compounds, especially flavonoids, corn husks are usually disposed of or
used as animal feed. In this paper, we investigate their recovery by an enzyme-assisted extraction
process consisting of a pretreatment of the plant material with cellulase followed by solvent extraction
with aqueous ethanol. A four-factor, three-level Box–Behnken design combined with the response
surface methodology was used to optimize the enzyme dosage (0.3–0.5 g/100 g), incubation time
(1.5–2.5 h), liquid-to-solid ratio (30–40 mL g−1 ) and ethanol concentration in the solvent (60–80%
v/v). Under the optimal conditions, about 1.3 g of total flavonoids per 100 g of dry waste were
recovered. A statistical analysis of the results was performed to provide a quantitative estimation
of the influence of the four factors, alone or in combination, on the extraction yields. Overall, the
results from this study indicate that corn husks are a valuable source of flavonoids and that they can
be easily recovered by a sustainable and environmentally friendly extraction process.

Keywords: flavonoids; corn husks; cellulase; enzyme-assisted extraction; waste valorization

1. Introduction
Corn (Zea mays L.) is a member of the family Poaceae and is one of the most abundant crops
cultivated worldwide [1]. In addition to being consumed in food products, some parts of corn have
gained interest as a source of therapeutic agents [2,3]. For example, corn silk (Stigma maydis), which is
made up of the stigmas and styles of the maize plant, has long been used in traditional medicine to
treat several diseases and disorders [4]. Its beneficial properties have been attributed to the presence
of various bioactive compounds—such as alkaloids, flavonoids, tannins, and vitamins—which are
thought to be responsible for its anti-inflammatory, antidiabetic and antitumor activity [5,6].
Corn husks are the thin cellulose-rich leafy sheaths covering the corn cob (Figure 1). They are
important byproducts of the corn processing industry and are generated in an amount of about 45
million tons worldwide [7]. As is the case of most agricultural residues, corn husks are usually
disposed of or used as animal feed, although several possible ways have been proposed to add value
to them. For example, their lignocellulosic nature makes them suitable as a starting material for the
production of sugars by chemical or enzymatic hydrolysis [8,9]. Some studies have investigated their

Processes 2019, 7, 804; doi:10.3390/pr7110804 www.mdpi.com/journal/processes

bilberry processing waste [24], olive pomace [25], mandarin peels [26], defatted seeds [27], and citrus
by-products [28]—has attracted a great deal of attention in recent years.
In this paper, we investigate the recovery of flavonoids from corn husks by enzyme-assisted
extraction. Enzymatic treatments are based on the ability of cell-wall degrading enzymes to
hydrolyze the structural components of the plant tissues, thereby facilitating the release of bioactive
Processes 2019, 7, 804 2 of 14
compounds [29]. We used cellulase as pretreatment agent since cellulose is the major component of
corn husks [12]. The main objective of this study was to evaluate the optimum conditions for the
recovery of flavonoids
use as substrate and the fermentation
in solid-state influence of theformain process parameters,
the production alone
of citric acid or or
[10] in rifamycin
combination, on
B [11].
the
Moreextraction
recently,yields. To this
it has been end, that
shown a statistical
celluloseapproach
fibers or based on Box–Behnken
nanofibers can be easilydesign andfrom
extracted response
corn
surface
husks and methodology was employed.
used as reinforcement in composite materials [12,13]. Corn husks are also a potential source
The results
of bioactive obtained especially
compounds, strongly support the useand
anthocyanins of enzymes as an effective
other flavonoids and
[14,15]. sustainable
However, onlymeans
a few
for improving
papers have been the published
recovery ofonflavonoids fromand
their recovery cornidentifications
husks. [16,17].

Figure 1.
Figure Corn cob
1. Corn cob and
and husks.
husks.

Flavonoids are secondary plant metabolites that belong to the vast group of phenolic
2. Materials and Methods
compounds [18]. They play an important role in plant defense mechanisms and are considered
to beChemicals
2.1. largely responsible for the health benefits of fruit and vegetable consumption [19,20].
and Plant Material
The health-promoting properties of flavonoids are believed to arise primarily from their ability to
Ethanol
scavenge free(CAS 64-17-5),
radicals and/ormethanol (CAS
chelate metal 67-56-1),
ions [21]. Inaluminum
addition tochloride (CAS 7446-70-0),
being effective antioxidants,sodium
some
hydroxide (CAS 1310-73-2),
of these compounds possess sodium nitrite (CAS
chemopreventive 7632-00-0),
properties, citric
which acidbeen
have (CAS 77-92-9),
related andcapacity
to their disodiumto
hydrogen phosphate (CAS 7558-79-4) were purchased from Sinopharm Chemical Reagent
interfere with the carcinogenesis process (initiation, promotion, and progression) [22,23]. Furthermore, Co., Ltd.
(Shanghai, China). Rutin
mounting evidence from in[3,3′,4′,5,7-pentahydroxy-flavon3-(o-rhamnosylglucoside)]
vitro, in vivo, and epidemiological studies suggests that (CAS they may153-18-4)
exert
was purchased from Winherb Medical Technology Co.,
anti-inflammatory, anti-allergic, and antibacterial activities [21]. Ltd. (Shanghai, China). Phosphate-citrate
bufferFor(PCB) at 0.1
all the M and
above pH 5the
reasons, was prepared
recovery of by adding proper
flavonoids amounts of disodium
from agro-industrial hydrogen
residues—such as
phosphate and citricwaste
bilberry processing acid [24],
to double distilled[25],
olive pomace water. All chemicals
mandarin were
peels [26], reagent
defatted grade
seeds and
[27], used
and as
citrus
received.
by-products [28]—has attracted a great deal of attention in recent years.
In this paper, we investigate the recovery of flavonoids from corn husks by enzyme-assisted
extraction. Enzymatic treatments are based on the ability of cell-wall degrading enzymes to
hydrolyze the structural components of the plant tissues, thereby facilitating the release of bioactive
compounds [29]. We used cellulase as pretreatment agent since cellulose is the major component of
corn husks [12]. The main objective of this study was to evaluate the optimum conditions for the
recovery of flavonoids and the influence of the main process parameters, alone or in combination, on
the extraction yields. To this end, a statistical approach based on Box–Behnken design and response
surface methodology was employed.
The results obtained strongly support the use of enzymes as an effective and sustainable means
for improving the recovery of flavonoids from corn husks.

2. Materials and Methods

2.1. Chemicals and Plant Material

Ethanol (CAS 64-17-5), methanol (CAS 67-56-1), aluminum chloride (CAS 7446-70-0), sodium
hydroxide (CAS 1310-73-2), sodium nitrite (CAS 7632-00-0), citric acid (CAS 77-92-9), and disodium
hydrogen phosphate (CAS 7558-79-4) were purchased from Sinopharm Chemical Reagent Co., Ltd.
(Shanghai, China). Rutin [3,30 ,40 ,5,7-pentahydroxy-flavon3-(o-rhamnosylglucoside)] (CAS 153-18-4)
was purchased from Winherb Medical Technology Co., Ltd. (Shanghai, China). Phosphate-citrate buffer
Processes 2019, 7, 804 3 of 14

(PCB) at 0.1 M and pH 5 was prepared by adding proper amounts of disodium hydrogen phosphate
and citric acid to double distilled water. All chemicals were reagent grade and used as received.
Cellulase (EC 3.2.1.4), with a claimed activity of 10,000 U/g, was supplied by Macklin Biochemical
Co., Ltd. (Shanghai, China). One unit is defined as the amount of enzyme that releases 1 µmol of
glucose from cellulose in 1 h at pH 5 and 37 ◦ C.
Corn husks were obtained from fresh corn harvested from fields in the suburbs of Jilin (Changchun,
China). The material was dried at 50 ◦ C in a forced-air dehydrator operating at atmospheric pressure
and ground in an electric mill. Then it was sieved to 60 mesh (250 µm) and stored in the dark at room
temperature until use.

2.2. Determination of Total Flavonoid Content

Total flavonoids were determined according to the method proposed by Khorasani et al. [30]
with some modifications. Briefly, 1 mL of properly diluted extract, 4 mL of 30% (v/v) methanol,
0.3 mL of 0.5 M NaNO2 and 0.3 mL of 0.3 M AlCl3 ·6H2 O were added and mixed in a test tube. After
incubation at room temperature for 6 min, 2 mL of 1 M NaOH were added. The mixture was brought
to 10 mL with 30% (v/v) methanol and the absorbance at 510 nm was measured with a double-beam
spectrophotometer (722N, Jingke Scientific Instrument Co., Ltd., Shanghai, China). The results were
expressed as rutin equivalents using a calibration curve obtained with rutin standards (0–50 mg/L).

2.3. Enzyme-Assisted Extraction

One gram of powdered corn husks and 10 mL of 0.1 M PCB at pH 5.0 were added and mixed in a
screw-capped glass flask. Cellulase was subsequently added in an amount so as to achieve the required
enzyme dosage. The resulting solution was incubated at 40 ◦ C for the desired time. Afterwards, the
enzyme was inactivated in boiling water for 5 min. The pretreated material was then subjected to
solvent extraction. Batch extraction experiments were performed at 80 ◦ C for 2 h using aqueous ethanol
as solvent. The liquid-to-solid ratio was varied between 20 and 45 mL g−1 and the concentration of
ethanol from 40 to 90% by volume. At the end of the extraction, the flask was rapidly cooled under tap
water, the liquid was filtered and assayed for total flavonoids.
The flavonoid extraction yield was calculated as

c×V
y = 100 , (1)
m
where c is the mass concentration of total flavonoids in the sample, V is the volume of the liquid, and m
is the dry weight of corn husks.

2.4. Experimental Design

The experiments were carried out according to a Box–Behnken design (BBD) with four factors
and three levels. The factors considered were the enzyme dosage (D), the incubation time (T),
the liquid-to-solid ratio (R), and the ethanol concentration in the extraction solvent (C). The levels of
each factor were selected based on the results of preliminary experiments and on previous studies on
similar systems. The actual and coded levels for the four factors are listed in Table 1. Coded levels
were determined using the following transformations:

D − 0.4
x1 = , (2)
0.1
T−2
x2 = , (3)
0.5
R − 35
x3 = , (4)
5
Processes 2019, 7, 804 4 of 14

C − 70
x4 = , (5)
10

Table 1. Actual and coded levels of the factors of Box–Behnken design (BBD).

Factor Factor Level Unit

−1 0 +1
Enzyme dosage (D) 0.3 0.4 0.5 g/100 g
Incubation time (T) 1.5 2.0 2.5 h
Liquid-to-solid ratio (R) 30 35 40 mL g−1
Ethanol concentration (C) 60 70 80 % v/v

The flavonoid extraction yield (y) was taken as the response variable. The central point was
replicated five times to estimate the pure experimental error and check the reproducibility of the
results. Overall, the BBD consisted of 24 + 5 = 29 runs, which were performed in randomized order to
minimize the effects of uncontrolled factors (Table 2).

Table 2. Experimental design layout and observed response (y). SO and RO are the standard and the
run order of experiments.

SO RO Factor Level y (g/100 g)

x1 x2 x3 x4
1 2 −1 −1 0 0 1.047
2 3 1 −1 0 0 1.018
3 27 −1 1 0 0 0.890
4 4 1 1 0 0 1.070
5 9 0 0 −1 −1 1.022
6 8 0 0 1 −1 1.037
7 13 0 0 −1 1 1.103
8 25 0 0 1 1 0.990
9 17 −1 0 0 −1 0.991
10 15 1 0 0 −1 1.092
11 26 −1 0 0 1 1.131
12 20 1 0 0 1 1.215
13 22 0 −1 −1 0 1.093
14 10 0 1 −1 0 1.120
15 21 0 −1 1 0 0.992
16 23 0 1 1 0 1.138
17 14 −1 0 −1 0 1.040
18 29 1 0 –1 0 0.898
19 1 −1 0 1 0 0.969
29 12 1 0 1 0 1.096
21 16 0 −1 0 −1 0.966
22 6 0 1 0 −1 1.010
23 7 0 −1 0 1 1.070
24 5 0 1 0 1 1.136
25 18 0 0 0 0 1.387
26 19 0 0 0 0 1.260
27 11 0 0 0 0 1.334
28 24 0 0 0 0 1.311
29 28 0 0 0 0 1.404

The design of experiments and the analysis of results were carried out using the Design-Expert®
software (vers. 8.0.6.1, Stat-Ease, Minneapolis, MN, USA).
Processes 2019, 7, 804 5 of 14

2.5. Heat-Reflux Extraction

One gram of powdered corn husks and 10 mL of 0.1 M PCB at pH 5.0 were mixed and incubated at
40 ◦ C for 2 h. Afterwards, ethanol was added to obtain a solution at 70% by volume of this component
and the extraction was carried out at 80 ◦ C for 2 h. Then, the mixture was cooled, filtered and assayed
for total flavonoids.

3. Results

3.1. Model Fitting and Analysis of Response Surface

The experimental data concerning the effects of enzyme dosage (D), incubation time (T),
liquid-to-solid ratio (R), and ethanol concentration (C) on the flavonoid extraction yield were fitted to
different models (linear, two-factor interaction, quadratic, and cubic). The best results were obtained
using the quadratic model
X X XX
y = a0 + ai xi + aii x2i + aij xi x j , (6)
i i i j

where y is the flavonoid extraction yield and xi are the coded independent variables. The model
contains 15 unknown parameters: the intercept (a0 ), four linear (ai ), four pure quadratic (aii ), and
six interaction (aij ) coefficients. They were estimated using a stepwise procedure, which consists in
a progressive modification of the model by iteratively adding or removing terms in order to keep
only the statistically significant ones (p < 0.05). Application of this procedure, with the constraint of
maintaining the hierarchy of the model, led to the equation

y = a0 + a1 x1 + a2 x2 + a3 x3 + a4 x4 + a11 x21 + a22 x22 + a33 x23 + a44 x24 + a13 x1 x3 , (7)

The 10 parameters of the reduced model were estimated by the least-square methods. They are
listed, together with their standard error (SE), p-value and F-value in Table 3.

Table 3. Estimated regression coefficients of the reduced model described by Equation (7) with the
associated standard errors (SE) and 95%-confidence intervals (CI).

Coefficient Term Value SE Low CI High CI

a0 intercept 1.339 0.031 1.274 1.405
a1 D 0.027 0.020 −0.016 0.069
a2 T 0.015 0.020 −0.028 0.057
a3 R −0.005 0.020 −0.047 0.038
a4 C 0.044 0.020 0.002 0.086
a13 D×R 0.067 0.035 −0.006 0.141
a11 D×D −0.160 0.028 −0.217 −0.102
a22 T×T −0.148 0.028 −0.206 −0.090
a33 R×R −0.155 0.028 −0.212 −0.097
a44 C×C −0.121 0.028 −0.179 −0.064

Overall, the model described reasonably well the experimental data, with an average percent
error between experimental and calculated results of about 3.5%. An examination of the ANOVA
results summarized in Table 4 reveals that the model was statistically significant (p < 0.0001) while the
lack-of-fit was not (p = 0.3567). Moreover, the residuals were randomly scattered between −2 and +2
(Figure 2), further supporting the soundness and effectiveness of the model.
Processes 2019, 7, 804 6 of 14

Table 4. ANOVA results for the reduced model described by Equation (7). DF denotes the degrees of
freedom, SS the sum of squares, MS the mean squares, F the F-value, and p the p-value.

Source DF SS MS F p
Regression 9 0.410 4.60 × 10−2 9.32 <0.0001
Residual error 19 0.093 4.91 × 10−3
Lack-of-fit 15 0.080 5.32 × 10−3 1.57 0.3567
Pure error 4 0.014 3.39 × 10−3
Processes2019,
Processes 2019,7,7,804
804 66ofof14
14
Total 28 0.510

44

33
Residuals
Studentized Residuals

22

11

00
Studentized

-1-1

-2-2

-3-3

-4-4
00 55 10
10 15
15 20
20 25
25 30
30
RunNumber
Run Number
Figure 2. Studentized model residuals as a function of run number.
Figure2.2.Studentized
Figure Studentizedmodel
modelresiduals
residualsas
asaafunction
functionof
ofrun
runnumber.
number.
From the Pareto chart displayed in Figure 3, it can be seen that:
Fromthe
From thePareto
Paretochart
chartdisplayed
displayedin inFigure
Figure3,3,ititcan
canbebeseen
seenthat:
that:
(a) All of the four investigated factors affected the flavonoid extraction yield through both a linear
(a)
(a) Allof
All
and of thefour
a the fourinvestigated
quadratic investigated
term; factorsaffected
factors affectedthetheflavonoid
flavonoidextraction
extractionyield
yieldthrough
throughboth
bothaalinear
linear
(b) andaaquadratic
and quadratic
Concerning term; terms, the R factor had only a marginal effect on the response variable,
the term;
linear
(b)
(b) Concerning
Concerning the
the
while the remaining linear
linear terms,
terms,
factors theRRfactor
the
provided factor hadonly
had
a significantonlyand
aamarginal
marginal effect
positiveeffect onthe
on theresponse
contribution, response variable,
variable,
increasing in the
while
while the
order: the remaining
T <remaining
D < C; factors provided a significant and positive contribution, increasing
factors provided a significant and positive contribution, increasing in inthe
the
order:TT<<DD<<C;
order: C;
(c) There was a positive interaction between D and R, suggesting that the enzyme dosage had a more
(c)
(c) Therewas
There wasaapositive
positiveinteraction
interactionbetween
betweenDDand and R,suggesting
suggesting thatthethe enzyme dosage had a
pronounced effect on flavonoid recovery at higherR, liquid-to-solidthat
ratios. enzyme dosage had a
more pronounced effect on flavonoid recovery at higher liquid-to-solid
more pronounced effect on flavonoid recovery at higher liquid-to-solid ratios. ratios.

Index
Index Factor
Factor
11 DD
22 TT
33 RR
44 CC

Figure3.
Figure
Figure 3.3.Pareto
Paretochart
chartfor
forthe
themodel
modelcoefficients.
coefficients.
coefficients.

Theeffect
The effectofofthe
thefour
fourfactors
factorson onthe
theextraction
extractionyield
yieldcan
canbe bebetter
betterappreciated
appreciatedby
byexamining
examiningthe the
perturbationplots
perturbation plotspresented
presentedin inFigure
Figure4.4.InInthese
theseplots,
plots,each
eachfactor
factorwas
waschanged
changedover
overthethefull
fullrange
range
explored(−1,
explored (−1,1)1)while
whilesetting
settingthe theremaining
remainingfactors
factorstototheir
theirmidpoint
midpointvalues
values(0).
(0).As
Asapparent,
apparent,the
the
response variable exhibited a non-monotonic variation for all of the factors, with a
response variable exhibited a non-monotonic variation for all of the factors, with a maximum locatedmaximum located
around the
around the central
central point
point (x(xi i == 0).
0). The
The relatively
relatively steep
steep slope
slope ofof the
the two
two branches
branches of
of the
the curves
curves isis
Processes 2019, 7, 804 7 of 14

The effect of the four factors on the extraction yield can be better appreciated by examining the
perturbation plots presented in Figure 4. In these plots, each factor was changed over the full range
explored (−1, 1) while setting the remaining factors to their midpoint values (0). As apparent, the
response variable exhibited a non-monotonic variation for all of the factors, with a maximum located
around the central point (xi = 0). The relatively steep slope of the two branches of the curves is
indicative of a quite high sensitivity of the flavonoid extraction yield to changes in the factor values.
Processes 2019, 7, 804 7 of 14

1.4 1.4

(a) (b)

1.3 1.3
y (g/100 g)

y (g/100 g)
1.2 1.2

1.1 1.1
-1 -0.5 0 0.5 1 -1 -0.5 0 0.5 1
x1 x2
1.4 1.4

(c) (d)

1.3 1.3
y (g/100 g)

y (g/100 g)

1.2 1.2

1.1 1.1
-1 -0.5 0 0.5 1 -1 -0.5 0 0.5 1
x3 x4

Figure
Figure 4. 4. Perturbation
Perturbationplotsplotsforforthethefour
fourfactors:
factors:(a) (a)
enzyme
enzyme dosage; (b) (b)
dosage; incubation time;time;
incubation (c)
liquid-to-solid ratioratio
(c) liquid-to-solid and and
(d) ethanol concentration.
(d) ethanol y is the
concentration. y isflavonoid extraction
the flavonoid yield and
extraction yieldxiand
is the
xi
coded level of
is the coded factor
level i. Each
of factor diagram
i. Each was plotted
diagram by keeping
was plotted the levels
by keeping of the
the levels other
of the three
other factors
three at
factors
their central
at their values.
central values.

To visualize
To the combined
visualize the combined effects
effects of factors on
of factors on the
the recovery
recovery ofof flavonoids,
flavonoids, response
response surface
surface and
and
contour plots were generated from the model equation. The plots shown in Figures
contour plots were generated from the model equation. The plots shown in Figures 5 and 6 were 5 and 6 were
obtained by
obtained by holding
holding two
two of
of the
the four
four factors
factors constant
constant at
at their
their midpoint
midpoint values. The results
values. The results clearly
clearly
indicate that the recovery process can be optimized by appropriate selection of extraction conditions.
indicate that the recovery process can be optimized by appropriate selection of extraction conditions.
 Processes2019,
Processes 2019,7,7,804
804 88ofof14
14
Processes 2019, 7, 804 8 of 14

(a) (b)
(a) (b)
g) g)

g) g)
y (g/100

y (g/100
y (g/100

y (g/100
(c) (d)
(c) (d)
g) g)

g) g)
y (g/100

y (g/100
y (g/100

y (g/100

Figure 5. Response surface plots showing the influence of: (a) liquid-to-solid ratio (R) and enzyme
Figure
dosage5.5.(D);
Response surface
(b) ethanol plots showing
concentration the incubation
(C) and influence of: (a) (T);
liquid-to-solid ratio (R) and enzyme
Figure Response surface plots showing the influence of:time (c) ethanol ratio
(a) liquid-to-solid concentration (C) and
(R) and enzyme
dosage
enzyme (D); (b) ethanol
dosage (D); andconcentration
(d) incubation (C)time
and(T)
incubation
and timedosage
enzyme (T); (c)(D)
ethanol
on theconcentration
flavonoid (C) and
extraction
dosage (D); (b) ethanol concentration (C) and incubation time (T); (c) ethanol concentration (C) and
enzyme
yield (y). dosage
For each (D); andthe(d) incubation time (T) and were
enzyme dosage (D) on the flavonoid extraction
enzyme dosage (D);plot,
and (d)levels of the time
incubation other factors
(T) and enzyme held at their
dosage central
(D) on thevalues (D = 0.4
flavonoid g/100 g,
extraction
yield (y).
T = 2(y). For
h, RFor
= 35each plot, the
g , Cthe
mLplot,
−1 levels
= 70% of
v/v). the other factors were held at their central values (D = 0.4 g/100 g,
yield each levels of the other factors were held at their central values (D = 0.4 g/100 g,
TT== 22 h,
h, R
R== 35
35 mL
mL g g−1, ,CC==70%
70%v/v).
−1
v/v).
g–1g) –1)
C (%v/v)
C (%v/v)

R (mL
R (mL

T (h) (a) D (g/100 g) (b)

T (h) (a) D (g/100 g) (b)
Figure 6. Representative contour plots showing the influence of: (a) ethanol concentration (C) and
Figure 6. Representative contour plots showing the influence of: (a) ethanol concentration (C) and
Figure
incubation6. Representative
incubation
time (T); and (b)contour plots showing
liquid-to-solid
time (T); and (b) liquid-to-solid
ratio (R)the
andinfluence of: (a) (D)
enzyme dosage
ratio (R) and enzyme ethanol
dosage concentration (C) and
on the flavonoid extraction
(D) on the flavonoid
incubation
yield. time
For each (T);
plot, and
the (b)
levels liquid-to-solid
of ratio
the other factors (R)
were and
held enzyme
at dosage
their central (D)
values on = 0.4flavonoid
(D the g/100 g,
extraction yield. For−1each plot, the levels of the other factors were held at their central values (D = 0.4
= 2 h, R = 35 mLFor
Textraction , C =plot,
g/100 g, T yield.
g each 70%the
= levels of the other factors were held at their central values (D = 0.4
v/v).
= 2 h, R = 35 mL g−1, C 70% v/v).
g/100 g, T = 2 h, R = 35 mL g , C = 70% v/v).
−1
Processes 2019, 7, 804 9 of 14

3.2. Optimizazion of Enzyme-Assisted Extraction

A numerical procedure based on the gradient descent method was used to maximize the response
variable. The following results were obtained: x1 = 0.08, x2 = 0.05, x3 = 0.00, x4 = 0.18, with
y = 1.345 g/100 g. In terms of actual factors, the maximum was achieved at: D = 0.41 g/100 g, T =
2.02 h, R = 35 mL g−1 , C = 71.8% (v/v). The model was validated by performing additional experiments
(n = 3) under the optimum conditions, which gave: yobs = 1.308 ± 0.082 g/100 g. The percentage error
between the observed and predicted values was 2.83%.

3.3. Comparison of Enzyme-Assisted Extraction and Heat-Reflux Extraction

Heat-reflux extraction experiments performed at 80 ◦ C for 2 h with 70% ethanol as solvent gave an
extraction yield of 0.946 ± 0.103 g/100 g. This value is about 30% lower than that of 1.308 ± 0.082 g/100
g obtained in enzyme-assisted extraction. Therefore, it can be deduced that an enzymatic treatment of
corn husks by cellulase has a positive effect on the recovery of flavonoids from the plant material.

4. Discussion
This study was undertaken to investigate the recovery of flavonoids from enzymatically
treated corn husks. The use of agricultural wastes as sources of value-added products is an
important step towards a circular economy, with beneficial effects on the environment and the
management of agro-resources. However, contrary to other wastes produced during fruit and
vegetable processing—such as bilberry peels [24], olive pomace [25], and citrus byproducts [28]—corn
husks have not yet been specifically investigated as a potential source of flavonoids. In particular,
the development of an efficient and easily scalable process for the extraction of flavonoids from this
material has not been addressed in previous studies. For this reason, the final goal of the present
research was to evaluate the optimal extraction conditions of a process based on the use of cellulase
and aqueous ethanol for the recovery of flavonoids from corn husks.
Like other phenolic compounds present in vegetables and fruits, flavonoids are usually located
within the plant tissues, often in association with cell-wall polysaccharides [31]. The fact that
the plant matrix acts as a significant barrier to solvent diffusion and the quite strong interactions
between flavonoids and cell-wall components are responsible for the low extraction efficiency of these
compounds from the plant sources. As a result, pretreatments of the plant material can be necessary to
obtain acceptable extraction yields [32].
Enzyme-assisted extraction processes rely on the capacity of cell-wall degrading enzymes to
hydrolyze the structural components of plant tissues, thus facilitating the release of bioactive compounds
into the surrounding medium [33,34]. Cellulose, hemicellulose, and pectin are the main structural
components of plant cell walls [35]. Cellulose is a linear polymer of β-(1,4)-D-glucopyranose units. It is
organized into microfibrils of amorphous and well-packed hydrogen-bonded crystalline regions. These
microfibrils form a fairly rigid polymeric network that is cross-linked by hemicellulose molecules,
especially xylans and xyloglucans [36]. The network is embedded in a matrix of hydrated pectic
substances and lignin.
Since cellulose is the key structural component of the cell wall, enzymatic pretreatments with
cellulases can be expected to have a beneficial effect on the recovery of flavonoids from plant
materials [37,38]. This has indeed been observed in several studies on different materials, such as
grape pomace [39], plant leaves [40], wood sawdust [41], and fruit residues [42].
In enzyme-assisted extraction processes, cellulase can be used either alone or in combination
with other cell-wall degrading enzymes, in single- or multi-stage treatments. In this study, we used
cellulase in a single-stage treatment. This was done to develop a simple and easily scalable treatment,
and in consideration of the high cellulose content of corn husks, which can reach up to 60% of the
biomass dry weight [43,44]. The beneficial effects resulting from the enzymatic treatment of corn husks
Processes 2019, 7, 804 10 of 14

suggest that cellulase is capable of degrading, or at least loosening, the cell wall, favoring the release of
flavonoids into the extraction solvent.
The susceptibility of plant cell walls to cellulase attack is known to depend on the relative
amounts of amorphous and crystalline cellulose [32,38]. In fact, while the crystalline fraction of
cellulose is quite resistant to hydrolytic degradation, amorphous domains are much more reactive
and easily hydrolyzable. Accordingly, it is likely that, during the enzymatic treatment of corn husks,
the amorphous cellulose is attacked first, followed by the hydrolysis of the crystalline regions.
An important point emerging from the present study is the existence of optimal values for all
of the factors investigated. Enzyme dosage and incubation time are two important factors affecting
the enzymatic treatment of biomass materials [29,45,46]. Enzymes are typically applied at dosages
ranging from 0.01 to 10% (w/w) [38]. In general, the higher the dosage, the greater the extraction yields.
However, above a certain value depending on the enzyme used and the characteristics of the biomass,
no apparent improvements or a decrease in extraction efficiency are observed. For the enzymatically
treated corn husks, the extraction yield was maximum at a cellulase dosage of 0.4% (w/w). This could
be due to the combined effects of enhanced degradation of the cell wall at higher enzyme dosage and
non-productive adsorption of cellulase on corn husks.
During the enzymatic degradation of lignocellulosic materials, non-productive adsorption
phenomena may result from the interaction of cellulase with lignin on the surface of the plant
material [47,48]. These phenomena have been widely investigated, especially in relation to the
conversion of lignocellulosic biomass into fermentable sugars, but the exact mechanisms involved
are far from being fully understood [49–51]. Lignin is a complex, highly branched, aromatic polymer
composed of p-hydroxyphenyl, guaiacyl, and syringyl units [52]. It has a strong affinity for cellulase,
which is bound through hydrophobic and electrostatic interactions [53]. Some evidence also suggests
that the irreversibly bound cellulase may lose its folded structure and become denatured [49].
The adsorbed cellulase is unable to carry out the hydrolysis reaction and, since lignin is tightly
associated with cellulose, it may cause steric hindrance to the free cellulase molecules. As a result, the
amount of enzyme available to attack cellulose is reduced and the hydrolysis rate decreases, negatively
affecting the extraction yields.
As for the incubation time, in published studies this quantity was varied from a few tens of
minutes to 24 h or more [38]. The existence of an optimal incubation time can arise from two opposing
effects: (a) the increased release of flavonoids resulting from a more extensive disruption of the cell
wall and (b) the higher susceptibility of the released flavonoids to degradation. The optimal incubation
time will depend on the relative contribution of these effects at the treatment temperature, which
influences both of them.
Another important point to emphasize is the dependence of the extraction efficiency on solvent
composition. The existence of an optimal composition, close to 70% (v/v) ethanol for corn husks,
has been evidenced in studies on different plant materials such as spent coffee grounds [54], mango
by-products [55], brewers’ spent grain [56], bilberry residues [57], and artichoke waste [58]. Several
factors are likely to be involved, such as solvent affinity for the extracted compounds and various
indirect effects of the solvent on the plant tissue. The latter include weakening of the interactions
between the bioactive compounds and cell-wall polysaccharides [59], protein denaturation [60], and
swelling of the plant tissue [61]. Swelling originates from the adsorption of solvent molecules on
specific functional groups of plant tissue components, especially cellulose fibers. This causes an increase
in inter-fiber spacing and an expansion of the plant material, which facilitates the penetration of solvent
molecules. Water and ethanol, the two components of the solvent used in this study, are known to be
effective swelling agents, being characterized by small molar volume, large basicity, and high hydrogen
bonding capability [62,63]. As a result, it can be speculated that all of the above factors may play a role
in determining the observed influence of solvent composition on flavonoid recovery.
A last point to be mentioned here is that the biomass residue obtained from the enzyme-assisted
extraction process could be further exploited to recover proteins or other corn husk components and/or
Processes 2019, 7, 804 11 of 14

to produce bioenergy. Likewise, the remaining biomass could be used to create additional value-added
products for the food industry. For example, it could serve as a substrate in solid-state fermentation
(SSF) to produce chemicals [64,65], crude enzymes [66,67], or other products [68]. In addition to the
resulting economic and environmental benefits, this strategy would contribute to providing a transition
of the vegetable oil sector to a circular economy through an integrated biorefinery approach.

5. Conclusions
Corn husks are an important byproduct of the corn processing industry, but at present they
constitute an unused or underutilized resource. In particular, they are a rich source of bioactive
flavonoids that could be used in a variety of applications. In this study, we have shown that these
compounds can be efficiently recovered by performing an enzymatic treatment of corn husks followed
by solvent extraction with aqueous ethanol. Although the mechanisms involved in the overall
extraction process are complex and only partly understood, the process can be optimized by carrying
out a reasonably small number of experiments on the material of interest. In this regard, the use of a
factorial design, such as the BBD, combined with the response surface methodology can be a powerful
and effective approach to achieving the above purpose.
Future research should be directed at determining whether and to what extent the recovery
of a particular flavonoid present in corn husks could be maximized by proper selection of process
conditions. It would also be interesting to apply the life cycle assessment (LCA) methodology to
evaluate environmental and economic indicators for assessing the sustainability of the proposed
process. Finally, the economic feasibility of the recovery process at the industrial scale should be
carefully assessed. In this regard, it is worth noting that several commercial cellulase preparations
of relatively low cost are currently available and that ethanol, the extraction solvent, can be easily
evaporated and recycled for reuse in the process.

Author Contributions: Methodology, A.Z., R.L., J.B.G.-M., and P.L.; Investigation, J.B.G.-M. and A.D.G.-D.;
Writing—original draft preparation, A.Z., R.L., and P.L.; Writing—review and editing, A.Z., R.L., and Á.D.G.-D.
Funding: This research was partially supported by grants from Sapienza University of Rome (Italy). The authors
gratefully thank the University of Cartagena (Cartagena, Colombia) and the University Francisco de Paula
Santander (Cúcuta, Colombia) for their support.
Conflicts of Interest: The authors declare no conflict of interest.

References
1. Shiferaw, B.; Prasanna, B.M.; Hellin, J.; Bänziger, M. Crops that feed the world 6: Past successes and future
challenges to the role played by maize in global food security. Food Secur. 2011, 3, 307. [CrossRef]
2. Lopez-Martinez, L.X.; Oliart-Ros, R.M.; Valerio-Alfaro, G.; Lee, C.H.; Parkin, K.L.; Garcia, H.S. Antioxidant
activity, phenolic compounds and anthocyanins content of eighteen strains of Mexican maize. Food Sci.
Technol. 2009, 42, 1187–1192. [CrossRef]
3. Ramos-Escudero, F.; Muñoz, M.A.; Alvarado-Ortíz, C.; Alvarado, A.; Yáñe, J.A. Purple corn (Zea mays L.)
phenolic compounds profile and its assessment as an agent against oxidative stress in isolated mouse organs.
J. Med. Food 2012, 15, 206–215. [CrossRef] [PubMed]
4. Hasanudin, K.; Hashim, P.; Mustafa, S. Corn silk (Stigma maydis) in healthcare: A phytochemical and
pharmacological review. Molecules 2012, 17, 9697–9715. [CrossRef] [PubMed]
5. Liu, J.; Wang, C.; Wang, Z.; Zhang, C.; Lu, S.; Liu, J. The antioxidant and free-radical scavenging activities
of extract and fractions from corn silk (Zea mays L.) and related flavone glycosides. Food Chem. 2011, 126,
261–269. [CrossRef]
6. Sarepoua, E.; Tangwongchai, T.; Suriharn, B.; Lertrat, K. Relationships between phytochemicals and
antioxidant activity in corn silk. Int. Food Res. J. 2013, 20, 2073–2079.
7. Reddy, N.; Yang, Y. Properties and potential applications of natural cellulose fibers from cornhusks. Green
Chem. 2005, 7, 190–195. [CrossRef]
Processes 2019, 7, 804 12 of 14

8. Barl, B.; Biliaderis, C.G.; Murray, E.D.; Macgregor, A.W. Combined chemical and enzymic treatments of corn
husk lignocellulosics. J. Sci. Food Agric. 1991, 56, 195–214. [CrossRef]
9. Yoon, K.Y.; Woodams, E.E.; Hang, Y.D. Enzymatic production of pentoses from the hemicellulose fraction of
corn residues. LWT Food Sci. Technol. 2006, 39, 388–392. [CrossRef]
10. Hang, Y.D.; Woodams, E.E. Corn husks: A potential substrate for production of citric acid by Aspergillus
niger. LWT Food Sci. Technol. 2000, 33, 520–521. [CrossRef]
11. Mahalaxmi, Y.; Sathish, T.; Chaganti, S.R.; Prakasham, R.S. Corn husk as a novel substrate for the production
of rifamycin B by isolated Amycolatopsis sp. RSP 3 under SSF. Process. Biochem. 2010, 45, 47–53. [CrossRef]
12. Huda, S.; Yang, Y. Chemically extracted cornhusk fibers as reinforcement in light-weight poly(propylene)
composites. Macromol. Mater. Eng. 2008, 293, 235–243. [CrossRef]
13. Xiao, S.; Gao, R.; Gao, L.; Li, J. Poly(vinyl alcohol) films reinforced with nanofibrillated cellulose (NFC)
isolated from corn husk by high intensity ultrasonication. Carbohydr. Polym. 2015, 136, 1027–1034. [CrossRef]
[PubMed]
14. Li, C.Y.; Kim, H.W.; Won, S.R.; Min, H.K.; Park, K.J.; Park, J.Y.; Ahn, M.S.; Rhee, H.I. Corn husk as a potential
source of anthocyanins. J. Agric. Food Chem. 2008, 56, 11413–11416. [CrossRef]
15. Khamphasan, P.; Lomthaisong, K.; Harakotr, B.; Ketthaisong, D.; Scott, M.P.; Lertrat, K.; Suriharn, B.
Genotypic variation in anthocyanins, phenolic compounds, and antioxidant activity in cob and husk of
purple field corn. Agronomy 2018, 8, 271. [CrossRef]
16. Vijayalaxmi, S.; Jayalakshmi, S.K.; Sreeramulu, K. Polyphenols from different agricultural residues: Extraction,
identification and their antioxidant properties. J. Food Sci. Technol. 2015, 52, 2761–2769. [CrossRef]
17. Kupski, L.; Telles, A.C.; Gonçalves, L.M.; Nora, N.S.; Furlong, E.B. Recovery of functional compounds from
lignocellulosic material: An innovative enzymatic approach. Food Biosci. 2018, 22, 26–31. [CrossRef]
18. Khalid, M.; Saeed-ur-Rahman; Bilal, M.; Huang, D.-F. Role of flavonoids in plant interactions with the
environment and against human pathogens—A review. J. Integr. Agric. 2019, 18, 211–230. [CrossRef]
19. Shashirekha, M.N.; Mallikarjuna, S.E.; Rajarathnam, S. Status of bioactive compounds in foods, with focus
on fruits and vegetables. Crit. Rev. Food Sci. Nutr. 2015, 55, 1324–1339. [CrossRef]
20. Leong, H.Y.; Show, P.L.; Lim, M.H.; Ooi, C.W.; Ling, T.C. Natural red pigments from plants and their health
benefits: A review. Food Rev. Int. 2018, 34, 463–482. [CrossRef]
21. Mojzer, E.B.; Hrnčič, M.K.; Škerget, M.; Knez, Ž.; Bren, U. Polyphenols: Extraction methods, antioxidative
action, bioavailability and anticarcinogenic effects. Molecules 2016, 21, 901. [CrossRef] [PubMed]
22. Rodríguez-García, C.; Sánchez-Quesada, C.; Gaforio, J.J. Dietary flavonoids as cancer chemopreventive
agents: An updated review of human studies. Antioxidants 2019, 8, 137. [CrossRef] [PubMed]
23. Hrnčič, M.K.; Španinger, E.; Košir, I.J.; Knez, Ž.; Bren, U. Hop compounds: Extraction techniques, chemical
analyses, antioxidative, antimicrobial, and anticarcinogenic effects. Nutrients 2019, 11, 257. [CrossRef]
[PubMed]
24. Lavecchia, R.; Medici, F.; Piga, L.; Zuorro, A. Factorial design analysis of the recovery of flavonoids from
bilberry fruit by-products. Int. J. Appl. Eng. Res. 2015, 23, 43555–43559.
25. Lavecchia, R.; Zuorro, A. Recovery of flavonoids from three-phase olive pomace by aqueous ethanol
extraction. ARPN J. Eng. Appl. Sci. 2016, 11, 13802–13809.
26. Ko, M.-J.; Kwon, H.-L.; Chung, M.-S. Pilot-scale subcritical water extraction of flavonoids from satsuma
mandarin (Citrus unshiu Markovich) peel. Innov. Food Sci. Emerg. Technol. 2016, 38, 175–181. [CrossRef]
27. Liau, B.-C.; Ponnusamy, V.K.; Lee, M.-R.; Jong, T.-T.; Chen, J.-H. Development of pressurized hot water
extraction for five flavonoid glycosides from defatted Camellia oleifera seeds (byproducts). Ind. Crop. Prod.
2017, 95, 296–304. [CrossRef]
28. Sharma, K.; Mahato, N.; Lee, Y.R. Extraction, characterization and biological activity of citrus flavonoids.
Rev. Chem. Eng. 2019, 35, 265–284. [CrossRef]
29. Puri, M.; Sharma, D.; Barrow, C.J. Enzyme-assisted extraction of bioactives from plants. Trends Biotechnol.
2012, 30, 37–44. [CrossRef]
30. Khorasani, E.A.; Mat, T.R.; Mohajer, S.; Banisalam, B. Antioxidant activity and total phenolic and flavonoid
content of various solvent extracts from in vivo and in vitro grown Trifolium pratense L. (Red Clover). Biomed.
Res. Int. 2015, 2015, 643285. [CrossRef]
31. Agati, G.; Azzarello, E.; Pollastri, S.; Tattini, M. Flavonoids as antioxidants in plants: Location and functional
significance. Plant. Sci. 2012, 196, 67–76. [CrossRef] [PubMed]
Processes 2019, 7, 804 13 of 14

32. Zhao, S.; Baik, O.D.; Choi, Y.J.; Kim, S.M. Pretreatments for the efficient extraction of bioactive compounds
from plant-based biomaterials. Crit. Rev. Food Sci. Nutr. 2014, 54, 1283–1297. [CrossRef] [PubMed]
33. Baiano, A. Recovery of biomolecules from food wastes—A review. Molecules 2014, 19, 14821–14842. [CrossRef]
[PubMed]
34. Gligor, O.; Mocan, A.; Moldovan, C.; Locatelli, M.; Cris, an, G.; Ferreira, I.C.F.R. Enzyme-assisted extractions
of polyphenols—A comprehensive review. Trends Food Sci. Technol. 2019, 88, 302–315. [CrossRef]
35. Doblin, M.S.; Pettolino, F.; Bacic, A. Plant cell walls: The skeleton of the plant world. Funct. Plant. Biol. 2010,
37, 357–381. [CrossRef]
36. Scheller, H.V.; Ulvskov, P. Hemicelluloses. Annu. Rev. Plant. Biol. 2008, 61, 263–289. [CrossRef]
37. Kuhad, R.C.; Gupta, R.; Singh, A. Microbial cellulases and their industrial applications. Enzyme Res. 2011, 1,
280696. [CrossRef]
38. Lavecchia, R.; Zuorro, A. Cellulase Applications in Pigment and Bioactive Compound Extraction. In New
and Future Developments in Microbial Biotechnology and Bioengineering; Gupta, V.K., Ed.; Elsevier: Amsterdam,
The Netherlands, 2016; pp. 209–222.
39. Meini, M.-R.; Cabezudo, I.; Boschetti, C.E.; Romanini, D. Recovery of phenolic antioxidants from Syrah
grape pomace through the optimization of an enzymatic extraction process. Food Chem. 2019, 283, 257–264.
[CrossRef]
40. Chen, S.; Xing, X.H.; Huang, J.J.; Xu, M.S. Enzyme-assisted extraction of flavonoids from Ginkgo biloba leaves:
Improvement effect of flavonol transglycosylation catalyzed by Penicillium decumbens cellulase. Enzyme
Microb. Technol. 2011, 48, 100–105. [CrossRef]
41. Wang, Y.; Zu, Y.; Long, J.; Fu, Y.; Li, S.; Zhang, D.; Li, J.; Wink, M.; Efferth, T. Enzymatic water extraction of
taxifolin from wood sawdust of Larix gmelini (Rupr.) Rupr. and evaluation of its antioxidant activity. Food
Chem. 2011, 126, 1178–1185. [CrossRef]
42. Huang, D.; Zhou, X.; Si, J.; Gong, X.; Wang, S. Studies on cellulase-ultrasonic assisted extraction technology
for flavonoids from Illicium verum residues. Chem. Cent. J. 2016, 10, 56. [CrossRef] [PubMed]
43. Nema, N.; Alamir, L.; Mohammad, M. Production of cellulase from Bacillus cereus by submerged fermentation
using corn husks as substrates. Int. Food Res. J. 2015, 22, 1831–1836.
44. Yilmaz, N.D.; Sulak, M.; Yilmaz, K.; Kalin, F. Physical and chemical properties of water-retted fibers extracted
from different locations in corn husks. J. Nat. Fibers 2016, 13, 397–409. [CrossRef]
45. Zuorro, A.; Lavecchia, R.; Medici, F.; Piga, L. Use of cell wall degrading enzymes for the production of
high-quality functional products from tomato processing waste. Chem. Eng. Trans. 2014, 38, 355–360.
46. Zuorro, A.; Maffei, G.; Lavecchia, R. Optimization of enzyme-assisted lipid extraction from Nannochloropsis
microalgae. J. Taiwan Inst. Chem. Eng. 2016, 67, 106–114. [CrossRef]
47. Donohoe, B.S.; Resch, M.G. Mechanisms employed by cellulase systems to gain access through the complex
architecture of lignocellulosic substrates. Curr. Opin. Chem. Biol. 2015, 29, 100–107. [CrossRef]
48. Lu, X.; Zheng, X.; Li, X.; Zhao, J. Adsorption and mechanism of cellulase enzymes onto lignin isolated from
corn stover pretreated with liquid hot water. Biotechnol. Biofuels 2016, 9, 118. [CrossRef]
49. Siqueira, G.; Arantes, V.; Saddler, J.N.; Ferraz, A.; Milagres, A.M.F. Limitation of cellulose accessibility and
unproductive binding of cellulases by pretreated sugarcane bagasse lignin. Biotechnol. Biofuels 2017, 10, 176.
[CrossRef]
50. Vermaas, J.V.; Petridis, L.; Qi, X.; Schulz, R.; Lindner, B.; Smith, J.C. Mechanism of lignin inhibition of
enzymatic biomass deconstruction. Biotechnol. Biofuels 2015, 8, 217. [CrossRef]
51. Rahikainen, J.; Mikander, S.; Marjamaa, K.; Tamminen, T.; Lappas, A.; Viikari, L.; Kruus, K. Inhibition
of enzymatic hydrolysis by residual lignins from softwood-study of enzyme binding and inactivation on
lignin-rich surface. Biotechnol. Bioeng. 2011, 108, 2823–2834. [CrossRef]
52. Dos Santos, A.C.; Ximenes, E.; Kim, Y.; Ladisch, M.R. Lignin–enzyme interactions in the hydrolysis of
lignocellulosic biomass. Trends Biotechnol. 2019, 37, 518–531. [CrossRef] [PubMed]
53. Rahikainen, J.L.; Evans, J.D.; Mikander, S.; Kalliola, A.; Puranen, T.; Tamminen, T.; Marjamaa, K.; Kruus, K.
Cellulase-lignin interactions—The role of carbohydrate-binding module and pH in non-productive binding.
Enzyme Microb. Technol. 2013, 53, 315–321. [CrossRef] [PubMed]
54. Zuorro, A.; Lavecchia, R. Polyphenols and energy recovery from spent coffee grounds. Chem. Eng. Trans.
2011, 25, 285–290.
Processes 2019, 7, 804 14 of 14

55. Dorta, E.; Lobo, M.G.; Gonzalez, M. Reutilization of mango byproducts: Study of the effect of extraction
solvent and temperature on their antioxidant properties. J. Food Sci. 2012, 77, C80–C88. [CrossRef] [PubMed]
56. Zuorro, A.; Iannone, A.; Lavecchia, R. Water–organic solvent extraction of phenolic antioxidants from
brewers’ spent grain. Processes 2019, 7, 126. [CrossRef]
57. Fidaleo, M.; Lavecchia, R.; Zuorro, A. Extraction of bioactive polyphenols with high antioxidant activity
from bilberry (Vaccinium myrtillus L.) processing waste. Orient. J. Chem. 2016, 32, 759–767. [CrossRef]
58. Zuorro, A.; Maffei, G.; Lavecchia, R. Reuse potential of artichoke (Cynara scolimus L.) waste for the recovery
of phenolic compounds and bioenergy. J. Clean. Prod. 2016, 111, 279–284. [CrossRef]
59. Sun, R.C.; Sun, X.F.; Wang, S.Q.; Zhu, W.; Wang, X.Y. Ester and ether linkages between hydroxycinnamic
acids and lignin from wheat, rice rye, and barley straws, maize stems, and fast-growing poplar wood.
Ind. Crop. Prod. 2002, 15, 179–188. [CrossRef]
60. Damoderan, S. Protein: Danaturation. In Handbook of Food Science, Technology and Engineering; Hui, Y.K., Ed.;
CRC Press: Boca Raton, FL, USA, 2005.
61. Obataya, E.; Gril, J. Swelling of acetylated wood I: Swelling in organic liquids. J. Wood Sci. 2005, 51, 124–129.
[CrossRef]
62. El Seoud, O.A. Understanding solvation. Pure Appl. Chem. 2009, 81, 697–707. [CrossRef]
63. Fidale, L.C.; Ruiz, N.; Heinze, T.; El Seoud, O.A. Cellulose swelling by aprotic and protic solvents: What are
the similarities and differences? Macromol. Chem. Phys. 2008, 209, 1240–1254. [CrossRef]
64. Kachrimanidou, V.; Kopsahelis, N.; Chatzifragkou, A.; Papanikolaou, S.; Yanniotis, S.; Kookos, I.;
Koutinas, A.A. Utilisation of by-products from sunflower-based biodiesel production processes for the
production of fermentation feedstock. Waste Biomass Valoriz. 2013, 4, 529–537. [CrossRef]
65. Papadaki, A.; Androutsopoulos, N.; Patsalou, M.; Koutinas, M.; Kopsahelis, N.; de Castro, A.M.;
Papanikolaou, S.; Koutinas, A.A. Biotechnological production of fumaric acid: The effect of morphology of
Rhizopus arrhizus NRRL 2582. Fermentation 2017, 3, 33. [CrossRef]
66. Kachrimanidou, V.; Kopsahelis, N.; Vlysidis, A.; Papanikolaou, S.; Kookos, I.K.; Monje Martínez, B.; Escrig
Rondán, M.C.; Koutinas, A.A. Downstream separation of poly(hydroxyalkanoates) using crude enzyme
consortia produced via solid state fermentation integrated in a biorefinery concept. Food Bioprod. Process.
2016, 100, 323–334. [CrossRef]
67. Papadaki, A.; Kachrimanidou, V.; Papanikolaou, S.; Philippoussis, A.; Diamantopoulou, P. Upgrading grape
pomace through Pleurotus spp. cultivation for the production of enzymes and fruiting bodies. Microorganisms
2019, 7, 207. [CrossRef] [PubMed]
68. Papadaki, A.; Kopsahelis, N.; Mallouchos, A.; Mandala, I.; Koutinas, A.A. Bioprocess development for the
production of novel oleogels from soybean and microbial oils. Food Res. Int. 2019, 126, 108684. [CrossRef]

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).