molecules

Review
Analysis of Cellulose and Lignocellulose Materials by
Raman Spectroscopy: A Review of the Current Status
Umesh P. Agarwal
USDA, Forest Service, Forest Products Laboratory, Madison, WI 53726, USA; uagarwal@fs.fed.us

Academic Editor: Andrzej Grzechnik




Received: 4 March 2019; Accepted: 26 April 2019; Published: 27 April 2019
Abstract: This review is a summary of the Raman spectroscopy applications made over the last
10 years in the field of cellulose and lignocellulose materials. This paper functions as a status report
on the kinds of information that can be generated by applying Raman spectroscopy. The information
in the review is taken from the published papers and author’s own research—most of which is
in print. Although, at the molecular level, focus of the investigations has been on cellulose and
lignin, hemicelluloses have also received some attention. The progress over the last decade in
applying Raman spectroscopy is a direct consequence of the technical advances in the field of Raman
spectroscopy, in particular, the application of new Raman techniques (e.g., Raman imaging and
coherent anti-Stokes Raman or CARS), novel ways of spectral analysis, and quantum chemical
calculations. On the basis of this analysis, it is clear that Raman spectroscopy continues to play
an important role in the field of cellulose and lignocellulose research across a wide range of areas and
applications, and thereby provides useful information at the molecular level.

Keywords: polymorphy; crystallinity; supramolecular structure; nanocellulose; nanocomposite;

lignin; syringyl; hemicellulose; non-linear; density functional theory

1. Introduction
Raman spectroscopy, a label free spectroscopic method, was first applied to cellulose materials in
the early 1970s [1–4] and subsequently, to lignin containing materials in mid 1980s [5–7]. Although at
that time laser-excitation of the samples was limited to the visible region, fluorescence was a significant
problem. Whereas for the latter, the presence of impurities in a cellulose sample was responsible,
for lignin containing materials the sample itself was to be blamed. Additionally, due to single channel
detection, the spectral acquisition period was too long. This situation improved gradually, over a span
of 15 years, as improved instrumentation and longer wavelength lasers for sample excitation became
available. For instance, in the 1990s new technologies like the holographic notch filter and the
availability of charge-coupled devices (CCD) that acted as multichannel detectors decreased acquisition
time by more than an order of magnitude. Rugged, air-cooled lasers (e.g., He–Ne 633 nm) simplified
utility requirements and provided more beam-pointing stability compared with that of water-cooled
lasers. Furthermore, sampling in confocal mode reduced sample fluorescence by physically blocking
the signal originating from the volume of the sample not in focus. The detected Raman signal came
from the illuminated spot. These capabilities permitted compositional mapping of the woody tissue
(a lignocellulose material) with chosen lateral spatial resolutions. Thousands of spectra could now be
obtained in a practical way.
Similarly, especially for milled-wood lignin and lignocelluloses, the problem of sample fluorescence
could be largely avoided with the availability of the 785 and 1064 nm lasers as excitation sources.
Although, at such excitation wavelengths, native lignin generally does not fluoresce, industrial
lignins still do. Consequently, investigations of certain lignins remain limited in linear/traditional
Raman spectroscopy.

Molecules 2019, 24, 1659; doi:10.3390/molecules24091659 www.mdpi.com/journal/molecules

Molecules 2019, 24, 1659 2 of 16

There are still other factors that continue to play a role in the growth of Raman spectroscopy
applications. In the present context, these have to do with (1) continued investigations of plant tissues,
(2) applications to materials based on nanocelluloses, (3) development of superior high-resolution
techniques for Raman imaging including linear and nonlinear Raman microscopy, (4) advances in
the design and control of ultrafast lasers for applications exploiting nonlinear Raman processes,
and (5) developments in chemometric analysis of Raman data. All these advances have made Raman
spectroscopy an essential analytical technique that permits probing of cellulose and lignocellulose
materials at the molecular level in a variety of matrices and sampling environments.
Because this review is limited to last 10 years or so, for those interested in the earlier work, there are
a number of reviews available [8–15]. It is hoped that this review focused on the field of celluloses and
lignocelluloses will serve as a status report on the use of Raman spectroscopy and simultaneously,
highlight of some of the many applications in the field.

2. Cellulose Materials
Cellulose, known as nature’s polymer, is the most abundant biomaterial on earth. Cellulose
materials are largely used in food, materials, medical, and pharmaceutical industries. In the field of
cellulose and lignocelluloses, cellulose and cellulose products (e.g., cotton, pulp, paper, and cellulose
nanomaterials) along with cellulose derivatives are the materials most often investigated using Raman
spectroscopy due to the fact that generally speaking these materials are not inherently fluorescent and
therefore, spectra with good signal-to-noise ratio can be obtained. In the spectra of cellulose materials,
most of the Raman features of cellulose have been identified and assigned [16]. However, although
most of the vibrational modes are highly coupled due to cellulose chain consisting of C–C and C–O
bonds, better band assignment still needs to take place for some of the bands [17].

2.1. Characteristic Bands

Characteristic Raman bands of cellulose in various materials have been reported. A few of these
studies consisted of investigations of Valonia ventricosa [1], Valonia macrophysa and Ramie [16],
cotton [18], and plant cell wall cellulose [19]. While for a large number of bands, the band positions
between such substrates were similar, band intensities and profiles (e.g., band widths) differed
significantly. Such differences are largely due to variances in the supramolecular structures (mostly,
crystallinity) of aggregated states of cellulose (more later). Such basic information is essential in the
interpretation of spectral data from cellulose materials.

2.2. Allomorphs of Cellulose

Cellulose molecules can aggregate in a wide variety of secondary and tertiary structures. This is
brought about by variations in the intra- and inter-molecular hydrogen bonding and the organization
of the cellulose chains (e.g., parallel vs. antiparallel or chain polarity). These aspects influence the
crystal parameters of the various allomorphs (crystalline phases) which in turn dictate the overall
structural accessibility (e.g., during mechanical, chemical and enzymatic treatments), and the reactivity
of cellulose. There are at least six cellulose polymorphs reported in the literature—cellulose I, cellulose
II, cellulose IIII , cellulose IIIII cellulose IVI and cellulose IVII . However, some researchers think that
cellulose IV is not a pure allomorph but rather a mixture of cellulose I and cellulose II. Moreover, it was
reported that cellulose I is a composite of cellulose Iα and Iβ crystalline forms. Raman spectroscopy has
been used to study these allomorphs and also to differentiate between them when present in a material
together. Specific Raman bands associated with allomorphs cellulose I, cellulose II, and cellulose III
have been reported [4,13] and in the cases of a mixture of cellulose I and cellulose II and cellulose
I and cellulose III it was reported that amounts of the allomorphs can be quantified by Raman
spectroscopy [20,21]. In Figure 1 below, Raman spectra of cellulose I, cellulose II, and cellulose III are
compared [13].
Molecules 2019, 24, x FOR PEER REVIEW 3 of 16

quantified
Molecules 2019,by
24,Raman
1659 spectroscopy [20,21]. In Figure 1 below, Raman spectra of cellulose I, cellulose
3 of 16
II, and cellulose III are compared [13].

Figure 1.
Figure 1. Comparison
Comparison of
of Raman
Raman spectra
spectra of
of cellulose
cellulose I,
I, cellulose
cellulose II,
II, and
and cellulose
cellulose III
III in
in various
various spectral
spectral
regions; (A) 50–750 cm−1 , (B) 850–1550 cm−1
−1, (B) 850–1550 cm−1 , (C)
, (C)2600–3600
2600–3600cm cm−1−1
. Reproduced
. Reproduced from
fromRef. [13].
Ref. [13].

From the polymorphic sensitive information in Raman, materials that contain more than one
type of crystal/aggregated state can be characterized and the various
various forms
forms present
present quantified.
quantified.
Therefore, physicochemical properties of cellulose materials can be understood and improved upon
for applications.

2.3. Cellulose Crystallinity

2.3. Cellulose Crystallinity Measurements
Measurements
Although
Although Raman
Raman spectroscopy
spectroscopy has
has been
been used
used for
for aa long time for
long time for qualitative
qualitative analysis
analysis of
of celluloses
celluloses
and
and cellulose materials, it was not until 2005 that a method was proposed to measure cellulose
cellulose materials, it was not until 2005 that a method was proposed to measure cellulose
crystallinity [22]. The method was based on the CH2 deformation modes (1450–1480 cm−1 ). However,
the method requires separation of the contributions of amorphous and crystalline forms of cellulose,
Molecules 2019, 24, x FOR PEER REVIEW 4 of 16
Molecules 2019, 24, 1659 4 of 16
crystallinity [22]. The method was based on the CH2 deformation modes (1450–1480 cm−1). However,
the method requires separation of the contributions of amorphous and crystalline forms of cellulose,
and for
and for that,
that, aa deconvolution
deconvolution process
process is is used.
used. However,
However, the
the deconvolution
deconvolution methodology
methodology has has its
its own
own
limitations. Subsequently, two more Raman methods, one in 2010 [23] and the
limitations. Subsequently, two more Raman methods, one in 2010 [23] and the other in 2018 [24] were other in 2018 [24] were
published. Both
published. Both the
the methods
methods are are free
free ofof the
the deconvolution requirement and
deconvolution requirement and use
use low
low wavenumber
wavenumber
Raman bands (380 and 93 cm −1 ) to estimate cellulose crystallinity. Compared to the 380 cm−1 based
Raman bands (380 and 93 cm ) to estimate cellulose crystallinity. Compared to the 380 cm−1 based
−1

method (380-Raman),
(380-Raman), the −1 based method (93-Raman) is that it differentiates
method the advantage
advantage of of the
the 93
93 cm
cm−1 based method (93-Raman) is that it differentiates
between the crystalline and organized cellulose, the latter beingananaggregated
between the crystalline and organized cellulose, the latter being aggregatedform form ofof
cellulose
cellulose that
thatis
oriented and aligned but at the same time, not-crystalline. Nevertheless, for the 93 cm −1 peak based
is oriented and aligned but at the same time, not-crystalline. Nevertheless, for the 93 cm peak based −1

method, considering
method, considering the the difficulty
difficulty of of performing
performing lowlow frequency
frequency RamanRaman duedue to
to both
both scattering
scattering and and
sample fluorescence issues, an FT-Raman instrument with 1064 nm excitation
sample fluorescence issues, an FT-Raman instrument with 1064 nm excitation is needed. In non-FT is needed. In non-FT
spectrometers, aa strong
spectrometers, strong contribution
contribution from from Rayleigh
Rayleigh scattering
scattering masks
masks the
the low
low frequency
frequency (LF) (LF) Raman
Raman
scattering from
scattering fromthe
thesamples.
samples.InIn Figures
Figure 2 and2 and 3 the
Figure Raman
3 the Raman spectra
spectraofofthe
thecalibration
calibration set set samples
samples
are shown,
are shown, respectively
respectively forfor 380-Raman
380-Raman (23) (23) and
and 93-Raman
93-Raman (24)(24) methods.
methods. The The samples,
samples, mixtures
mixtures of of
the cotton microcrystalline cellulose and completely amorphous cellulose, were
the cotton microcrystalline cellulose and completely amorphous cellulose, were used to develop the used to develop the
crystallinity methods.
crystallinity methods. Figure
Figure 22 below
below shows
shows Raman
Raman spectra
spectra ofof aa set
set of
of cellulose
cellulose samples
samples that that were
were
used to develop the calibration for the two Raman methods in our laboratory,
used to develop the calibration for the two Raman methods in our laboratory, namely, 380-Raman namely, 380-Raman
(Figure
(Figure 2)2) [23]
[23] and
and 93-Raman
93-Raman (Figure
(Figure 3) 3) [24].
[24].

Figure
Figure 2. 2.Calibration
Calibrationset set
Raman
Ramanspectra after after
spectra subtracting amorphous
subtracting spectrum
amorphous in the region
spectrum in the250–700
region
250–700 cm ; (a) control, cotton microcrystalline cellulose, and plots (b) to (h) are spectra of1,
cm −1; (a) control,
−1 cotton microcrystalline cellulose, and plots (b) to (h) are spectra of mixture mixture
mixture 1,
2, mixture
mixture 2, 3, mixture
mixture 3,4, mixture4,5,mixture
mixture mixture5,6,mixture
and 120-min
6, andball milledball
120-min cellulose,
milled respectively. Note that
cellulose, respectively.
in the that
Note case in
of the
120-min
case spectrum
of 120-min thespectrum
intensities
thebelow
intensities
−1 are all zero because
700 cmbelow 700 cm−1 are all of zero
the subtraction.
because of
Spectra were offset on the intensity scale for display purposes. Reproduced with
the subtraction. Spectra were offset on the intensity scale for display purposes. Reproduced permission fromwith
Ref.
[23]. Copyright Springer Nature 2010.
permission from Ref. [23]. Copyright Springer Nature 2010.
Molecules 2019, 24, 1659 5 of 16
Molecules 2019, 24, x FOR PEER REVIEW 5 of 16

Figure 3. Low3.frequency
Figure Raman
Low frequency Raman spectra
spectraofofcalibration setsamples,
calibration set samples, calculated
calculated crystallinities
crystallinities of the of the
samplessamples
are listed on the
are listed onleft hand
the left side
hand ininthe
side theFigure. Reproduced
Figure. Reproduced withwith permission
permission from
from Ref. Ref. [24].
[24].
Copyright Springer
Copyright Nature
Springer 2010.
Nature 2010.

BecauseBecause crystallinity
crystallinity has anhas an important
important effect effect
on theonphysical,
the physical, mechanical,
mechanical, and chemical
and chemical properties
properties of cellulose (e.g., with increasing crystallinity, tensile strength, dimensional stability, and
of cellulose (e.g., with increasing crystallinity, tensile strength, dimensional stability, and density
density increase, while properties such as chemical reactivity and swelling decrease), its accurate
increase,determination
while properties such as chemical reactivity and swelling decrease), its accurate determination
is very important. Compared to some cellulose crystallinity methods, the 380-Raman
is very [23]
important.
and 93-Raman Compared to some
[24] methods cellulose
have unique crystallinity
advantages methods,
and produce reliablethe
data.380-Raman [23] and
Availability of
93-Ramansuch[24] methods
reliable have unique
information is usefuladvantages
in predictingand produce reliable
the applicability of the data. Availability
cellulose materials. of such reliable
information is useful in predicting the applicability of the cellulose materials.
2.4. Characteristics of Supramolecular Structures
2.4. Characteristics of Supramolecular
Crystalline Structures of how cellulose molecules are organized at the tertiary
cellulose is one manifestation
or 3-dimensional level and is measured by estimating cellulose crystallinity. Yet other measures,
Crystalline cellulose is one manifestation of how cellulose molecules are organized at the tertiary or
some of them based on Raman spectroscopy, exist to characterize the other aggregated states of
3-dimensional
cellulose.level
For and is measured
instance, by estimating
supramolecular structure cellulose
of cellulosecrystallinity.
in native stateYet
in other
woods,measures,
which wassome of
them based
foundon Raman
to be spectroscopy,
non-crystalline, has beenexist to characterize
defined thetoother
by its accessibility wateraggregated states
(based on Raman of cellulose.
intensity
For instance,
increasesupramolecular
at 1380 cm−1 upon structure
samplingof incellulose
D2O vs. Hin 2O)native state
[25]. This in woods,
Raman whichdetermined
spectroscopy was found to be
parameter,has
non-crystalline, wasbeen
found to be related
defined by itstoaccessibility
be correlated to with degree
water of lateral
(based order (DOLO)
on Raman which
intensity is
increase at
based
−1 on FWHM (full-width at half maximum) of [200] peak in X-ray diffraction [25]. A second
1380 cm upon sampling in D2 O vs. H2 O) [25]. This Raman spectroscopy determined parameter,
Raman spectroscopic measure, is the ratio of the peak heights of bands at 1460 and 1480 cm−1 [25]. In
was found to be related to be correlated with degree of lateral order (DOLO) which is based on FWHM
the aggregated state, this indicates the extent of disorder in cellulose that exists at C6 at the intra-
(full-width at half
molecular maximum)
level. of [200]
The two Raman peak
bands in X-ray
at 1460 diffraction
and 1480 [25].the
cm−1 represent A deformation
second Raman modes spectroscopic
of the
measure, is the ratio of the peak heightsCH of2OHbands at 1460 andof1480 −1 [25]. In the aggregated state,
methylene groups of the exocyclic group. In light thesecmtwo Raman parameters, non-
crystalline
this indicates the aggregated states of cellulose
extent of disorder that are
in cellulose notexists
that easily described
at C6 at thecan intra-molecular
now be characterized. ThisThe two
level.
is an unparalleled capability of
−1Raman spectroscopy and is expected
Raman bands at 1460 and 1480 cm represent the deformation modes of the methylene groups to play an important role in of the
characterizing the supramolecular structures of cellulose in various materials.
exocyclic CH2 OH group. In light of these two Raman parameters, non-crystalline aggregated states of
cellulose2.5.
that are not easily described can now be characterized. This is an unparalleled capability of
Nanocelluloses
Raman spectroscopy and is expected to play an important role in characterizing the supramolecular
When it comes to analyzing cellulose nanomaterials (CNs—cellulose nanofibrils and cellulose
structures of cellulose
nanocrystals), in various
Raman materials.
spectroscopy is uniquely suited because, these materials can be analyzed in
their native hydrated states without any special considerations. In our work on CNs, we have used
2.5. Nanocelluloses
Raman spectroscopy for estimation of crystallinity (in suspensions and freeze-dried states) [20,26–
28], measurement of accessibility of the nanomaterials by water [20,26], detection and quantitation of
When it comes to analyzing cellulose nanomaterials (CNs—cellulose nanofibrils and cellulose
nanocrystals), Raman spectroscopy is uniquely suited because, these materials can be analyzed in
their native hydrated states without any special considerations. In our work on CNs, we have used
Raman spectroscopy for estimation of crystallinity (in suspensions and freeze-dried states) [20,26–28],
measurement of accessibility of the nanomaterials by water [20,26], detection and quantitation of
cellulose II polymorph in the nanomaterials [20], and effect of drying on the structure of CNs [20].
Moreover, because Raman spectra of the nanomaterials contain bands that are associated with
Molecules 2019, 24, 1659 6 of 16

Molecules 2019, 24, x FOR PEER REVIEW 6 of 16

chemical functionalities usually present on the surfaces of prepared/modified materials (for example,
cellulose II polymorph in the nanomaterials [20], and effect of drying on the structure of CNs [20].
sulfate Moreover,
esters present on the surfaces of the sulfuric acid produced CNCs) they can be detected
because Raman spectra of the nanomaterials contain bands that are associated with
and quantified. For instance,
chemical functionalities trans
usually esterified
present on theCN, where
surfaces surface hydroxyl
of prepared/modified groups
materials (forwere esterified,
example,
was characterized
sulfate estersby Raman
present on spectroscopy
the surfaces of the[29]. Yet another
sulfuric application
acid produced CNCs)was
theyincanthe
be area of crystallinity
detected and
quantified.
determination For cellulose
of the instance, trans esterifiedthat
nanofibrils CN,were
wheredisintegrated
surface hydroxylby groups
various were esterified,approaches
processing was
characterized by Raman spectroscopy [29]. Yet another application was in
(refining and microfluidization) [28]. In another application, Raman spectroscopy was used to the area of crystallinity
determination of the cellulose nanofibrils that were disintegrated by various processing approaches
characterize supramolecular structure of molecularly thin cellulose I nanoparticles [30]. Raman spectra
(refining and microfluidization) [28]. In another application, Raman spectroscopy was used to
from molecularly
characterize thin cellulose nanoparticles
supramolecular (WTS30, thin
structure of molecularly WTS60, WTS120),
cellulose control [30].
I nanoparticles wood-pulp
Raman (WP),
and TEMPO
spectra((2,2,6,6-Tetramethylpiperidin-1-yl)oxyl)
from molecularly thin cellulose nanoparticles (WTS30, treated WTS60,
wood pulp (WT)
WTS120), are shown
control in Figure 4.
wood-pulp
In the nanofibrils,
(WP), and TEMPOclear changes in the cellulose’s supramolecular
((2,2,6,6-Tetramethylpiperidin-1-yl)oxyl) treatedstructures
wood pulpwere(WT) noted based
are shown in on the
Figure 4.
spectral analysis. In the nanofibrils, clear changes in the cellulose’s supramolecular structures were noted
based
CNs are on the spectral
derived from analysis.
natural resources and Raman spectroscopy plays a significant role in various
CNs are derived from natural resources and Raman spectroscopy plays a significant role in
aspects of their R & D (production, properties and applications), so that novel and advanced materials
various aspects of their R & D (production, properties and applications), so that novel and advanced
from thematerials
CNs can be the
from developed.
CNs can be developed.

Figure
Figure 4. Raman4. Raman spectra
spectra of molecularly
of molecularly thin(single
thin (single digit
digitangstrom
angstromthickness) cellulose
thickness) nanoparticles
cellulose nanoparticles
thatobtained
that were were obtained by intensive
by intensive sonicationofofTEMPO-oxidized
sonication TEMPO-oxidized cellulose fibers.
cellulose Spectra
fibers. of TEMPO
Spectra of TEMPO
treated (WT) and control wood pulp (WP) are also shown. Reproduced with permission from Ref.
treated (WT) and control wood pulp (WP) are also shown. Reproduced with permission from Ref. [30].
[30]. Copyright American Chemical Society 2011.
Copyright American Chemical Society 2011.
2.6. Cellulose Nanocomposites
2.6. Cellulose Nanocomposites
Confocal Raman microscopy is being increasing applied to study composites of CNs [31–36].
Typically, the nanocomposites consist of thermoplastics and CNs. Additionally, composites of cellulose
nanofibrils and cellulose nanocrystals have also been investigated (author’s unpublished work).
In most instances, confocal Raman microscopy is used to investigate how well CNs are distributed
in a composite because it has been reported that aggregation of the CNs, which are the load bearing
component in a thermoplastic composite, inhibits the stress-transfer process. Because CNs are
Molecules 2019, 24, 1659 7 of 16

hydrophilic, they tend to aggregate and are not evenly distributed. One of the earlier investigations
on this topic focused on composites cellulose nanocrystals (CNC)-polypropylene [31]. Such analysis
showed that CNCs were aggregated to varying degrees in the composites and remained poorly
dispersed in the polypropylene matrix. A recent study was performed to evaluate quantitatively
distribution of CNCs in high-density polyethylene (HDPE) composites [33]. The Composites were
prepared using maleic anhydride modified polyethylene (MAPE) and poly(ethylene oxide) (PEO) as
a compatibilizer. The researchers reported that it was possible to quantify the distribution and mixing
of cellulose nanocrystals (CNCs) in a polyethylene-matrix composite.
To understand how stress is transferred from matrix to CNs in a composite, Raman spectroscopy
has been used. It has been reported that under mechanical tension the cellulose band at 1095 cm−1 shifts
to lower frequencies [34]. This characteristic of the Raman band has been used by researchers to study
what influences the stress transfer in various composites. For example, Rusli et al. [35] investigated
tunicate CNC and poly(vinyl acetate) nanocomposites by polarized Raman spectroscopy and showed
that the stress transfer was influenced by local orientation of the nanocrystals. Shifts of the 1095 cm−1
band as a result of uniaxial deformation of nanocomposite films were used to determine the degrees of
stress experienced by the CNCs, not only due to stress transfer from the matrix to the tunicate CNCs
but also between the CNCs within the composite. In another case, short cellulose nanofibrils (SCNF)
were used as reinforcement in polyvinyl alcohol (PVA) fiber and the system was analyzed by Raman
spectroscopy [36]. The researchers reported that the strength and modulus of PVA/SCNF composite
fiber with a SCNF weight ratio of 6 were nearly 60 and 220% higher, respectively than that of PVA by
itself. Shifts in the Raman peaks at 1095 cm−1 indicated good stress transfer between the SCNF and the
PVA matrix.
Therefore, both at the macro and sub-micron levels, Raman analysis gives beneficial information,
on the topics in nanocomposites research where further development needs to take place to improve the
compatibility between CNs and the matrix. Once such problems are successfully addressed, practical
applications of the nanocomposites will ensue.

3. Lignocellulose Materials
Cellulose materials that also contain hemicellulose and lignin are called lignocelluloses.
Such materials are abundantly available for multiple uses including biofuels production. Lignin is
an aromatic polymer whereas both cellulose and hemicellulose are carbohydrate polymers. Lignin
is found in the cell walls of vascular plants. Raman spectroscopic applications to lignin containing
materials had a slow start largely due to the fact that significant fluorescence from lignin was generated
upon excitation by visible lasers. Quenching of this fluorescence was difficult because it was caused by
one of the components of the material and not by an external impurity, as is the case with cellulose.
Although with the advent of confocal Raman microscopy this situation improved somewhat because
the technique limited fluorescence by physically blocking the signal originating from the locations other
than the sample in focus. Moreover, sampling in water and in an environment of molecular oxygen
helped—most likely, due to degradation/quenching of the fluorescence caused by chromophores in
lignin. Later, in mid 1990s, with the availability of the 1064 nm near-IR laser for sample excitation,
the fluorescence problem was mostly eliminated since not many lignocellulose materials fluoresced at
such a long wavelength of excitation [8,9,19]. Nevertheless, analysis of industrial lignins continues to
be a challenge in linear/ordinary Raman spectroscopy due to the fact that such dark colored lignins
generate extensive amount of fluorescence even at 1064 nm excitation.
Early work on lignocelluloses started with studying the ultrastructure of wood, especially
orientation of lignin in wood cell walls. Subsequently, role of lignin in high yield pulp yellowing
(thermal and photo) was investigated. Later on, efforts to quantitate lignin in woods were made
but failed due to the fact that aromatic ring conjugated substructures in lignin disproportionately
contributed to the intensity of 1600 cm−1 band (pre-resonance Raman and conjugation effects). However,
once such structures were removed, for instance in unbleached kraft pulps, residual lignin could be
Molecules 2019, 24, 1659 8 of 16

quantified [37]. Other wood-pulp studies made use of resonance Raman spectroscopy to avoid the
problematic fluorescence. FT-Raman spectroscopy was applied for classifying woods into hardwoods
and softwoods. For more information on past work related to lignin, a recent review nicely covered
the applications of Raman spectroscopy to understand the lignin containing biomass [14] and its
processing. Similarly, a review of Raman microscopy applications to understand plant cell walls and
their structure-function relationships has been provided [15]. Some other confocal Raman microscopy
work focused on the altered lignin structure of CAD (cinnamyl alcohol dehydrogenase) deficient
transgenic poplar [38], study of lignin distribution in Eucalyptus cell walls during GVL/water/acid
treatment [39], identification of hemicellulose features in poplar cell wall (in conjunction with
multivariate analysis) [40], effects of weathering on wood surfaces [41], and characterization of ionic
liquid swelled cell walls [42–44].

3.1. Lignin Bands

All plant lignins are composed of three types of aromatic nuclei—guaiacyl (G), syringyl (S)
and p-hydroxyphenyl (H). In Raman spectra of lignocellulose materials, lignin bands have been
identified in woods (both softwoods and hardwoods) and sugarcane bagasse (pith)—a grass. Because
there are many structural differences not only between lignins of the plants but also between lignins’
compositions, lignin Raman spectra vary between lignocellulosics and also within a particular type of
lignocellulose (e.g., woods; angiosperms vs. gymnosperms). Lignins can be classified as G-lignins
(from softwoods), GS-lignins (from hardwoods), GSH-lignins (from grasses) or GH-lignins (from
compression wood). Wood lignins have been studied the most either in the native state [15,19] or upon
isolation as milled-wood lignins [19,45]. The Raman bands of softwood and hardwood lignins have
been assigned in literature although some of the assignments continue to be a topic of ongoing research.
The assignment work is largely based on the studies of lignin models [46,47] and the refinements that
have been made using DFT (density functional theory) calculations [48]. From the models used in
the work (4-methylphenol, 2-methoxy-4-methylphenol, and 2,6-dimethoxy-4-methylphenol), the DFT
refinements were made on characteristic H, G, and S bands and related to specific vibrations based on
the DFT calculations.

3.2. Spectral Domination by the Conjugated Structures in Lignins

Early on, when the lignin quantitation work based on Raman spectroscopy did not succeed, it was
recognized that some of the subunits in its structure contributed disproportionately to the observed
scattering at 1600 cm−1 . Subsequent work involving 19 lignin model compounds established that
in conventional Raman spectroscopy the intensity enhancement in the spectrum was due to both
pre-resonance Raman and conjugation effects [8,9]. Comparatively, in near-IR Raman using forty
models, it was established that only conjugation effect contributed to the increased intensity [46].
Therefore, quantitation is not straightforward and only possible if same lignin structure is assumed
in all samples [13]. This is exemplified by the Raman study of a number of lignocellulose materials
where the contributions to 1600 cm−1 band by the lignin conjugated structures were removed/reduced
by treatment with alkaline hydrogen peroxide (H2 O2 ), diimide (N2 H2 ), and sodium borohydride
(NaBH4 ) treatments [13,45]. This meant that in cases where lignin structure is non-homogeneous,
lignin quantitation is achievable using Raman spectroscopy. The advantage of Raman spectroscopy in
being sensitive to the conjugated lignin substructures is that such structures can be easily detected
when present in small quantities. An example is the detection of pinosylvins (a kind of wood extractive)
in Scots pine wood by Raman spectroscopy [49]. In Table 1 below, for black spruce milled-wood lignin
(MWL), data is shown that shows the manner in which Raman band positions/intensities change upon
various chemical treatments [45].
Molecules 2019, 24, 1659 9 of 16

Table 1. Treated black spruce milled-wood lignins (MWLs)—Raman frequencies (cm–1 ) and change in
peak-intensity of most intense untreated MWL bands. Reproduced with permission from Ref. [45].
Copyright TAYLOR & FRANCIS 2011.

H2 O2 bl. Hydro. Acet. Methy.

Untreated
(% ∆ Int.) (% ∆ Int.) (% ∆ Int.) (% ∆ Int.)
3071 m a 3070 (sc b ) 3065 (sc) 3073 (na) 3071 (na)
3008 sh 3008 (sc b ) 3002 (sc) 3010 (nc) 3008 (nc)
2940 m 2940 (na c ) 2937 (na) 2939 (–36) 2940 (–307)
2890 sh 2883 (sc b ) 2886 (sc) - f (100) 2890 (–298)
2845 m 2846 (nc d ) 2843 (nc) 2846 (sc) 2845 (nc)
1662 s 1656 e (86) 1650 e (100) 1674 e (53) 1662 (94)
1621 sh - f (100) - f (100) 1622 (61) 1621 (100)
1597 vs 1602 (67) 1605 e (73) 1600 d (59) 1597 (65)
1508 vw 1508 -f 1508 1508
1453 m 1453 (35) 1453 (51) 1454 (49) 1453 (–111)
1430 w 1429 1435 e 1426 1430
1392 sh -f -f 1391 1392
1363 sh 1360 1351 e 1367 1363
1334 m 1334 (69) 1334 (57) 1335 (52) 1334 (50)
1298 sh -f -f 1300 1298
1272 m 1271 (79) 1273 (64) 1274 (58) 1272 (58)
1226 vw 1222 1224 1222 1226
1192 w 1193 (33) 1190 (50) 1195 (36) 1192 (38)
1136 m 1135 (88) - f (92) 1129 d,e (58) 1136 (78)
1089 w 1079 d,e -f 1089 1089
1033 w 1032 (35) 1032 (47) 1034 (54) 1033 (–84)
975 vw -f -f 972 975
928 vw 927 929 936 d,e 928
895 vw 884 d,e 895 907 d,e 895
811 sh -f 811 -f 811
787 w 781 e (46) 789 (34) 792 e (70) 787 (54)
731 w 731 (41) 731 (14) 730 (44) 731 (–67)
637 vw 638 641 641 637
588 vw -f -f 607 e 588
557 vw 562 e 561 548 e 557
534 vw 535 -f 531 534
491 vw 484 e vw -f 490 491
457 vw 458 465 e 461 457
361 w 368 e (42) 370 e (51) 366 (60) 361 e (73)
aNote: vs. is very strong; s is strong; m is medium; w is weak; vw is very weak; and sh is shoulder. Band intensities
are relative to other peaks in spectrum. b sc = Small change in intensity. c na = Not applicable because this band is
expected to be minimally impacted and was used in spectra normalization. d nc = No change in intensity. e Band
shifted. f Not detected.

3.3. Differentiation between G-, S-, and H-Lignins

When one considers the Raman spectra of the G, S, and H lignins, certain Raman spectral
differences stand out. These have been described in various publications. In the case of S-lignin
spectrum, bands at 2940, 1455, 1331, 1156, 1037, 597, 531, 522, 503, 472, 447, 431, 417, and 369 cm−1
either are more intense compared to G-lignin bands or are only present in its spectrum [45]. Similarly,
in G-lignin’s case, such bands are present at 1298, 557, and 384 cm−1 [45]. Such differences are important
in further developing Raman spectroscopy for applications to investigate lignin and lignocellulosics.
For instance, % syringyl groups in lignin (%S) have been quantified using Raman spectroscopy
(see below). Although lignins dominant in H units seem to have not been investigated by Raman
spectroscopy, H Dehydrogenation Polymer (DHP) lignin has been investigated [50]. Based on this
work, compared to the syringyl and guaiacyl DHPs, the H-DHP showed weak bands in the aliphatic
C-H stretch region. This is likely to be due to the fact that in these DHPs there were not many lignin
Molecules 2019, 24, 1659 10 of 16

units with -OCH3 groups. Additionally, compared to the spectra of S and G DHPs, the bands at 370,
600, 730, 820, 904, 1035, 1100, 1150, 1366, 1456, 1508 cm−1 were missing. However, new bands were
detected at 831, 1173, 1257, 1393 cm−1 .

3.4. %S Content and S/G Ratio

Lignin plays a significant role in the growth and development of plants and at the same time,
has to be dealt with in the industrial utilization of lignocellulose biomass. The lignin monomer
composition and, therefore, its %S content is an important parameter for lignocellulose biomass
characterization and utilization. Various techniques, both wet-chemical and spectroscopic methods
have been used to estimate the H, G, and S monomer composition of lignins. Previously, Raman
spectroscopy in conjunction with thioacidolysis has been used to develop PLS (partial least squares)
models for predicting lignin S/G ratios [51]. However, this multivariate approach lacks simplicity
and specificity and involves first developing a predictive model based on a large number of samples.
In another FT-Raman approach [52] to determine lignin S/G, first, S and G bands in the spectra of lignin
models were identified. To separate S and G contributions in the spectra of lignocellulosic substrates,
spectral deconvolution between 1220 and 1530 cm−1 was carried out. By integrating the intensities of
the deconvoluted Raman bands that represented the S and G units the Raman S/G ratio was calculated.
Next, a regression model was constructed between the FT-Raman and pyrolysis-GC/MS S/G ratio
results and was used to estimate final S/G ratios [52]. However, sample spectral regions assigned to
S, G, and H lignin units had significant interference not only due to sample fluorescence, but also
from overlap with cellulose and hemicellulose contributions. Moreover, it is well known that the
deconvolution approach used is a subjective method, and the obtained values may not be reliable.
More recently, the author’s laboratory has developed a technique that uses no such method [53].
This Raman method is based on quantifying the intensity of the 370 cm−1 peak in wood lignins. This
band in the Raman spectra of hardwoods is known to be due to syringyl units in lignin [45].

4. Hemicellulose Contributions
The molecular structures of hemicelluloses (branched amorphous polysaccharides composed
of different carbohydrate monomers, a mixed polymer) are similar to that of cellulose and therefore,
in the Raman spectra, the two types of contributions overlap [19]. This was borne out by the earlier
investigations where the Raman spectra of cellulose, glucomannan, and xylan were compared [19].
Nevertheless, because usually cellulose exists in an organized/crystalline state, its Raman features are
much sharper and stronger compared to that of the hemicelluloses. More recently, Zhang et al. [40]
reported Raman spectrum of hemicellulose in poplar cell wall that was based on multivariate analysis.
As expected, in the simulated components spectra of hemicellulose and cellulose, there was significant
overlap of Raman bands [40].

5. New Methods of Spectral (Data) Analysis

Most methods of analyzing complex set of Raman spectra fall in the category of Chemometrics.
Chemometrics can be described as the application of mathematical and statistical methods to extract
more useful information from chemical and physical measurement data. Within this field, subtopics of
data preprocessing, genetic algorithms, spectral image processing, data compression, and calibration
transfer are generally included. Using this approach, the spectral changes or differences are visualized
with the help of multivariate statistical analysis (linear discriminant analysis, partial least squares
regressions etc.). In such approaches, using baseline corrected spectra gives superior results. Moreover,
in the case of irregularly shaped fluorescence background, truncating spectra first then BG correction
gives better results compared to correction the whole spectrum at once. The multivariate methods are
used in quantitative measurements because they can detect unanticipated sources of variance in the
data. In the earlier days, Raman analysis was carried out in combination with the chemometric methods
partial least squares (PLS) and principal component analysis (PCA). For example, PLS was used to build
Molecules 2019, 24, 1659 11 of 16

a predictive model for estimating cellulose crystallinity for cellulose materials [23]. Other methods
were subsequently applied to plant cell walls [15,54–56]; complex multicomponent lignocellulose
samples, that produced complex Raman spectra so that the identification and quantitation of individual
constituents can be carried out. To automatically identify Raman spectra of different cell wall layers
(cell corner—CC, compound middle lamella—CML, secondary wall—SW, gelatinous layer—G-layer,
and cell lumen), Zhang et al. [54] proposed a new chemometric method on the basis of PCA and cluster
analysis. Later on, Zhang et al. [40] applied a chemometrics technique called self-modeling curve
resolution (SMCR) to poplar cell wall Raman imaging data to discriminate the spectral contributions of
cellulose and hemicellulose to demonstrate how the two components were distributed in various cell
types of the woody plant. Similarly, Prats-Mateu et al. [56] applied vertex component analysis (VCA),
non-negative matrix factorization (NNF) and multivariate curve resolution–alternating least squares
(MCR-ALS) to gain insights into the spectra obtained as a result of Raman imaging of the plant cell
walls. Vertex component analysis was recommended as a good preliminary approach whereas the
other two tools had some negative aspects to them. It was reported [56] that while investigating spruce
wood and Arabidopsis, although NMF, MCR-ALS, and VCA were used to find the purest components
within the confocal Raman microscopy datasets, the endmember spectra obtained using VCA were
more correlated and mixed than those retrieved by NMF and MCR-ALS methods. Moreover, it was
stated that the former two methods can also lead to artificial band shapes due to peak splitting or
inverted bands.

6. New Raman Techniques (Instrumentation)

As the instrumentation has evolved in the field of Raman spectroscopy, so has the ability of
Raman spectroscopy to deliver useful information in the field of cellulose and lignocelluloses. Early
on, the problem of fluorescence swamping the weaker Raman signal in the study of lignin containing
materials was overcome by using a Kerr gated Raman collection system [57]. In this study, chromophore
lignin structures in wood-pulp samples were analyzed. In the Kerr system, fluorescence is rejected in
the time domain with the Kerr gate acting as an optical shutter. In one investigation, lignin radicals in
the plant cell wall were probed by this technique [58].
Confocal Raman imaging [12] where analysis is carried out at diffraction-limited spatial resolution
level to obtain information on the chemical constituents and their distributions within the sample
has been used quite widely to investigate tissues of various plants (e.g., stem, leaves, and roots).
Additionally, different vascular cell types (e.g., xylem and phloem) in woody and herbaceous plants
have been studied by this spectroscopy technique [59]. Plant cell walls are highly complex structures
and during growth and development phase, undergo changes. For poplar wood xylem and phloem,
the kind of information that can be generated is shown in Figure 5 [59]. Another area where Raman
imaging has played a role is in the area of nanocellulose composites [31] where the technique has
allowed a better understanding of the cellulose/polymer interface.
To deal with the sample fluorescence and small amounts of lignin substructures (e.g., in commercial
and milled-wood lignins), surface-enhanced Raman spectroscopy (SERS) was used by Agarwal et al. [60].
In SERS, Raman scattering is enhanced of molecules adsorbed on rough metal surfaces (e.g., silver or
gold) or by nanostructures such as plasmonic-magnetic silica nanotubes. The enhancement factor can
be as much as 1010 to 1016 , which means the technique may detect single molecules.
In the context of producing biofuels (e.g., cellulosic ethanol), biomass processing has been
monitored by stimulated Raman scattering (SRS) microscopy (Figure 6 below) [61,62]. In SRS, two laser
beams at ωp and ωS coincide on the sample. When the difference frequency ∆ω = ωp − ωS (also
called the Raman shift), matches a specific vibrational frequency, amplification of the Raman signal is
achieved by virtue of stimulated excitation of molecular transition rate. Zeng et al. [62] reported that
this technique can be readily applied to understand the pretreatment reaction, and can also be useful
for studying the enzymatic breakdown of cellulose or for in vivo imaging of lignification and cellulose
biosynthesis in plant cell-wall development. In another application of SRS, maleic acid pretreatment
 chromophore lignin structures in wood-pulp samples were analyzed. In the Kerr system,
fluorescence is rejected in the time domain with the Kerr gate acting as an optical shutter. In one
investigation, lignin radicals in the plant cell wall were probed by this technique [58].
Confocal Raman imaging [12] where analysis is carried out at diffraction-limited spatial
Molecules
resolution2019,level
24, x FOR PEER REVIEW
to obtain information on the chemical constituents and their distributions 12 of 16 the
within
Molecules 2019, 24, 1659 12 of 16
sample has been used quite widely to investigate tissues of various plants (e.g., stem, leaves, and
Figure 5. Raman images of xylem for poplar wood calculated by integrating from 2800 to 3030 cm −1
roots). Additionally, different vascular cell types (e.g., xylem and phloem) in woody and herbaceous
(a, b overall morphology), from 2800 to 2918 cm−1 (c, d carbohydrates), from 1540 to 1700 cm−1 (e, f,
plants
resulted have been
in effectively studied by this spectroscopy technique [59]. Plant cell walls are highly complex
lignin), from 1255 to removing ofGthe
1290 cm−1 (g, h loosely
units), and from packed
1320 to lignin
1338 cm−1in(i,secondary walls
j, S units). Images which
a, c, e, in turn led
structures and during growth and development phase, undergo changes. For poplar wood xylem
to enzyme accessibility
g, i: xylem for digestion
near the cambium and imagesof b,the biomass
d, f, h, j xylem[62]. In yet
near the annualanother application,
ring (AR). Reproduced in conjunction
and phloem, the kind of information thatSPRINGER
can be generated is shown in Figure 5 [59]. Another area
with ofwith permissiondigestion,
enzymatic from Ref. [59].
SRSCopyright
technique was used NATUREby the 2018.
researchers to establish that xylan can be
where Raman imaging has played a role is in the area of nanocellulose composites [31] where the
resolved
technique
from cellulose
haswithallowed
and lignin [63].
a better fluorescence
understanding of small
the cellulose/polymer interface.
To deal the sample and amounts of lignin substructures (e.g., in
commercial and milled-wood lignins), surface-enhanced Raman spectroscopy (SERS) was used by
Agarwal et al. [60]. In SERS, Raman scattering is enhanced of molecules adsorbed on rough metal
surfaces (e.g., silver or gold) or by nanostructures such as plasmonic-magnetic silica nanotubes. The
enhancement factor can be as much as 101⁰ to 1016, which means the technique may detect single
molecules.
In the context of producing biofuels (e.g., cellulosic ethanol), biomass processing has been
monitored by stimulated Raman scattering (SRS) microscopy (Figure 6 below) [61,62]. In SRS, two
laser beams at ωp and ωS coincide on the sample. When the difference frequency Δω = ωp – ωS (also
called the Raman shift), matches a specific vibrational frequency, amplification of the Raman signal
is achieved by virtue of stimulated excitation of molecular transition rate. Zeng et al. [62] reported
that this technique can be readily applied to understand the pretreatment reaction, and can also be
useful for studying the enzymatic breakdown of cellulose or for in vivo imaging of lignification and
5. Raman images
Figurebiosynthesis
cellulose of xylem
in plant fordevelopment.
cell-wall poplar wood In calculated
another by integrating
application of from 3030 cm−1
2800 to acid
SRS, maleic
(a, b overall morphology), from 2800 to of 2918 −1 1700 cm−1
pretreatment resulted in effectively removing the cmloosely(c,packed
d carbohydrates), from 1540
lignin in secondary wallsto which
(e, f, led
lignin), from 1255 to 1290 cm −1 (g, h G units), −1
in turn to enzyme accessibility for digestion of theand from 1320
biomass [62]. to
In 1338 cm (i,application,
yet another j, S units). Images
in a,
conjunction with ofnear
c, e, g, i: xylem enzymatic digestion,
the cambium andSRS technique
images b, d, f,was
h, j used
xylem bynear
the researchers
the annual ring to establish that
(AR). Reproduced
xylan
withcan be resolved
permission from
from cellulose
Ref. and ligninSPRINGER
[59]. Copyright [63]. NATURE 2018.

Figure6.6. Raman
Figure spectrum
Raman spectrum andand
SRSSRS imaging
imaging of stover.
of corn corn stover.
(a) Raman(a) spectrum
Raman spectrum
of raw cornofstover.
raw corn stover.
Thepeak
The peakat at 1600 cm−1−1(red
1600 cm arrow)
(red corresponds
arrow) to thetolignin
corresponds distribution,
the lignin and the peak
distribution, and at
the1100
peakcmat
−1
1100 cm−1
(green arrow) corresponds to cellulose. (b) SRS image of the vascular bundle including the
(green arrow) corresponds to cellulose. (b) SRS image of the vascular bundle including the edge of the edge of
the stem in raw corn stover at 1600 cm−1, showing the lignin distribution. Labeled structures are
stem in raw corn stover at 1600 cm−1 , showing the lignin distribution. Labeled structures are discussed
discussed in the text: parenchyma (PC), phloem (PH), vessel (VE), tracheid (TR), fiber (FI).
in the text: parenchyma (PC), phloem (PH), vessel (VE), tracheid (TR), fiber (FI). Reproduced with
Reproduced with permission from Ref. [61]. Copyright JOHN WILEY AND SONS 2010.
permission from Ref. [61]. Copyright JOHN WILEY AND SONS 2010.
In coherent anti-Stokes Raman spectroscopy (CARS) two beams of frequency ω1 and ω2 are
In coherent
mixed anti-Stokes
in the sample to generateRaman spectroscopy
a new frequency ωs = 2ω(CARS) two beams of frequency ω 1 −and ω2 are
1 − ω2. If there is a Raman resonance at ω1

mixed in the sample to generate a new frequency ω =

ω2 = Ω, an amplified signal is detected at the anti-Stokes frequency
s 2ω ω11 +−Ω.ωCompared
2 . If there to is a Raman resonance
conventional
at Raman, 2 = Ω,
ω1 − ωCARS offers greater sensitivity
an amplified signal isand rapid analytical
detected capability. CARS
at the anti-Stokes ω1 +aΩ.
microscopy,
frequency multi-
Compared to
photon technique,
conventional Raman, hasCARS
been applied
offers to wild-type
greater and lignin-downregulated
sensitivity and rapid analytical alfalfa lines to assess
capability. CARS themicroscopy,
impact of lignin modification on overall cell wall structure [64]. In another work reported on cellulose
a multi-photon technique, has been applied to wild-type and lignin-downregulated alfalfa lines to
fibers, CARS along with second harmonic generation (SHG) microscopy was used to investigate
assess the impact of lignin modification on overall cell wall structure [64]. In another work reported
molecular alignment in dry and hydrated cellulose [65].
on cellulose fibers, CARS along with second harmonic generation (SHG) microscopy was used to
investigate molecular alignment in dry and hydrated cellulose [65].
In the biofuels area, clearly from the examples cited above, Raman applications/analyses provided
information that when factored in and successfully worked on the remaining problems will advance
the field. Therefore, it contributes to generating solutions to the problems at hand.
Molecules 2019, 24, 1659 13 of 16

7. Quantum Chemical Calculations

Although, to interpret experimental results, the use of quantum computational methods (typically
using density functional theory, DFT) is standard practice in Raman spectroscopy, the methods have
been only occasionally used in the cellulose and lignocellulose field. This may be because all the
component biopolymers of plants (cellulose, hemicellulose, and lignin) give rise to many vibrational
bands that are combinations of several vibrational modes which involve various atoms in the molecules.
In addition to correlating the band positions with the molecular motions (normal modes), the DFT
methods are capable of predicting intensities and bandwidths of the spectral features. This capability
is therefore, adding to our ability to calculate and interpret Raman spectroscopic measurements.
In the following a few example s are described where the quantum chemical methods have aided the
spectral interpretation.
In 2006, Barsberg et al. [57] used DFT to correlate the resonance Raman spectroscopy results with
the predicted vibrational modes of lignin radicals, and indicated that the radicals were formed from
syringyl and guaiacyl moieties in beech and spruce, respectively. Similarly, in 2010, Raman bands of
lignin model monomeric structures were compared with density functional theory predictions [47].
For analyzing vibrational modes of cellulose, DFT calculations with the cellulose Iα and Iβ structures
were carried out [66,67]. Such calculations have resulted in not only the assignment of experimentally
detected bands (e.g., assignments of the 380 and 93 cm−1 bands in cellulose) but also questioning
of some of the earlier assignments (H-bonded OH modes) in cellulose. The assignments of the 380
and 93 cm−1 bands are particularly important because they have been identified as measures of
cellulose crystallinity.

8. Conclusions and Outlook

In conclusion, the future of applying Raman spectroscopy to investigate cellulose and
lignocellulosic materials is bright. The information provided by Raman spectroscopy is unique
and obtained in situ. Past applications have ranged from understanding cellulose crystallinity and
biomass recalcitrance to characterizing lignin and cellulose nanomaterials. Although it took quite
a while for the Raman applications to arrive at this juncture and even now, in most laboratories,
the technique has not become a regularly used analytical tool, going forward, it is likely that this
powerful spectroscopy with its multitude of sub-techniques will be implemented by many more
researchers. In the context of plant deconstruction and biofuels, Raman spectroscopy methods meet
the need to screen rapidly and accurately large collections of different plant materials. Various Raman
imaging techniques have allowed researchers to assess the biomass pretreatment processes in real-time,
providing insights into how specific treatments affect the constituents of the biomass. Finally, future
advancements in the field of Raman spectroscopy will continue to offer researchers an analytical
capability that is a versatile, non-destructive, and user-friendly. Nevertheless, challenges remain in
applying linear Raman spectroscopy to fluorescent materials (e.g., commercial lignins). However,
non-linear techniques (e.g., SRS and CARS) are free of this problem although spectra of commercial
lignins do not seem to have been reported in the literature.

Funding: This research received no external funding.

Conflicts of Interest: The authors declare no conflicts of interest.

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