Smoothing algorithms are used to reduce noise in the recorded Raman spectra. They rely on the fact that spectral data are assumed to vary somewhat gradually when going from one spectral data point to the next, whereas associated noise typically changes very quickly. Different algorithms can be chosen (e.g. Savitzky-Golay [123], wavelet transformation [124], maximum entropy filter [122]) and especially before multivariate data analysis smoothing might become necessary.

Baseline correction and background subtraction can be performed based on linear models or on more complex mathematical functions. For removing background coming from the measured material (fluorescence) or signal from the substrate different methods have been developed that are capable of handling irregularly shaped baselines [125-128]. Baseline correction of Raman spectra is especially important prior to multivariate methods and different solutions to improve baseline correction methods have been developed [125, 129, 130].

Additional pre-treatments can be carried out to enhance certain properties of the image data set. The choice depends on the spectral structure and the goal of the data analysis. Derivatives can be carried out to stress subtle differences in spectral features among spectra. For pixel classification purposes, when the focus is on comparing the shapes of the pixel spectra independently from their global intensity, spectra normalization represents a useful option [126].

#### *2.5.2. Univariate and multivariate image generation*

166 Cellulose – Biomass Conversion

embedding [118, 119].

methods to generate images.

*2.5.1. Spectra pre-processing*

might become necessary.

129, 130].

single dimension, which is afterwards dispersed fibre by fibre onto the CCD camera [114]. To characterize a sample´s chemical heterogeneity often only a few global Raman images need to be recorded at well-defined wavenumber positions, which are known either a priori or from

As a non-destructive technique in general minimal or no sample preparation is necessary. Nevertheless to refer intensity changes in imaging approaches directly to changes in content or composition the same Raman scattering volume has to be probed at any position and this requires a flat surface. Otherwise a reference band for normalisation or the use of band ratios becomes necessary. Depending on the biological material to be probed microcutting or polishing might be the method of choice to achieve such a flat surface, with or without

To take the advantage of the scanning (mapping) method to have a molecular fingerprint (whole spectrum) at every pixel sophisticated data analysis has to be applied. Typically in each scanning experiment thousands of spectra are acquired and extracting the relevant information needs usually pre-processing of the spectra (e.g. cosmic ray removal, background subtraction, smoothing…) followed by univariate or multivariate data analysis

Raman instruments utilizing CCD detectors suffer from occasional spikes caused by cosmic rays. Cosmic rays are high energy particles from outer space which interact with atoms and molecules in the earth´s atmosphere and may generate a false signal in the shape of a very sharp peak in the spectrum. Various mathematical methods can be used to filter the cosmic rays from the spectra [120-122]. As the spikes are usually quite high and may overlay with

Smoothing algorithms are used to reduce noise in the recorded Raman spectra. They rely on the fact that spectral data are assumed to vary somewhat gradually when going from one spectral data point to the next, whereas associated noise typically changes very quickly. Different algorithms can be chosen (e.g. Savitzky-Golay [123], wavelet transformation [124], maximum entropy filter [122]) and especially before multivariate data analysis smoothing

Baseline correction and background subtraction can be performed based on linear models or on more complex mathematical functions. For removing background coming from the measured material (fluorescence) or signal from the substrate different methods have been developed that are capable of handling irregularly shaped baselines [125-128]. Baseline correction of Raman spectra is especially important prior to multivariate methods and different solutions to improve baseline correction methods have been developed [125,

bands of interest they have to be removed to avoid influences on the final results.

spectral analysis of data obtained in point or line scanning [116].

**2.5. Processing of Raman spectra and image generation**

In univariate data analysis each spectrum determines one value of the corresponding pixel in the image. The value of each pixel is determined by simple filters or by fitting procedures [122]. The most important of the simple filters is the integrated intensity (sum) filter evaluating the integrated intensity of various specific peaks found in the spectra of the image scan. The amount and scattering strength of a certain band attributed to a specific component is displayed and gives information on its spatial distribution. Filters can also plot changes in peak width, which can give a measure of crystallinity and structural orientation or changes in peak position (i.e., centre of mass position) as a measure for the strain within the material [131].

Many different multivariate methods exist and are described in detail elsewhere [126, 132- 136]. Here only the very basics of the most commonly used ones, principal component analysis (PCA) and cluster analysis, are introduced.

PCA is the underlying method for many other multivariate methods since it is very effective for data reduction. It may be used to reduce the data set to 5–15 principal components (PC) and the residual error. Principal components are new, uncorrelated, and approximately normally distributed variables that provide faithful representations of the image, which can be used later as input information for exploration, segmentation, classification and other purposes. Compression by using principal components keeps all the relevant (image) information and, at the same time, allows understanding the relationship among the variables used to build the model by analysing the internal correlation structures provided by the loadings [132].

Cluster analysis applied to Raman images is essentially the sorting of the tens of thousands of spectra in a data set according to their similarities [122]. There are various ways of clustering, e.g. distance calculation (Euclidean, Manhattan), hierarchical cluster analysis, Kmeans Cluster Analysis, Fuzzy Clustering and each has its advantages and disadvantages [136].

In section 4 and 5 exemplary results for univariate analysis (band integration) and cluster analysis are shown.

#### **3. Raman spectra of plant cell walls: What information can we gain?**

Plant cell walls are nanocomposites based on cellulose microfibrils embedded in different matrix polymers (hemicelluloses, pectin and lignin) [137]. Besides water plays an essential role in the native plant cell walls and as water has relatively low polarizabilities weak Raman intensities are observed. Consequently, water saturated samples can be measured without problems. Raman investigations of cellulosic feedstock started in the 80s with the acquisition of spectra of cellulose fibres and wood [138-140].

 Position and assignment [138, 141] of the cellulose bands (Ramie fibre, Figure 2) and their preferred molecular orientation parallel (II) or perpendicular (╪) to the fibre axis direction (=laser polarisation direction). The sensitivity of the bands to intensity changes due to orientation is referred to

Because of the orientation-dependence of the cellulose band intensities, the fibre direction (plant axis) and the laser polarization have to be adjusted in a known and defined way in every plant cell wall Raman experiment. As the intensity of multiple bands change in a characteristic way (up and down, Figure 2), it is possible to distinguish between intensity changes due to alterations in fibre orientation from those resulting from different cellulose content (all bands increase or decrease). To eliminate intensity changes due to different focal plane during rotating the polarizer or drift of the scan stage band height ratios or band area ratios can be calculated for a more detailed analysis (Figure 3A). These ratios also allow the comparison of Raman measurements with different integration times and thus intensity. The ratios (2897/1095, 378/1095 and 1377/1095) reveal a clear dependency of the cellulose band intensities and the angle of the incident laser polarization. The strong relationship can be described by a cosine function and a quadratic regression (R2>0.99). Based on the band height ratios (Figure 3B) or partial least square regression models the angle of the cellulose molecule with respect to the laser polarisation direction and consequently the microfibril angle can be calculated [142]. Noteworthy, changes in fibre orientation often correspond to alterations in cellulose crystallinity. The effect of changes in crystallinity on the shape of the cellulose Raman bands was also investigated in detail: amorphous cellulose results in a

as no changes (◦) and strong (s) or medium (m). (def: deformation, sym: symmetric, asym:

significant decline in band heights accompanied by band broadening [143].

antisymmetric)

#### **3.1. Cellulose microfibrils: The structural elements of the cell wall**

The cellulose microfibrils give a Raman signature comprising about 15 different significant bands (Figure 2, ramie fibre: almost pure cellulose). If these microfibrils are aligned with a preferred orientation, the Raman intensity of the cellulose bands depends on the angle between the orientation of the cellulose microfibrils and the laser polarisation direction [138]. The investigated Ramie fibres have almost perfect alignment of the cellulose microfibril parallel to the fibre axis and high crystallinity (X-ray results, not shown). Changing the laser polarization from parallel with respect to the fibre axis (0°) to perpendicular to the fibre (90°) results in severe changes of the Raman intensity of almost all characteristic bands (Figure 2) except the two bands at 1377 and 437 cm-1. The bands at 1457 cm-1 (HCH and HOC bending), 517 cm-1, 499 cm-1 and 378 cm-1 (heavy atom stretching) show higher intensity in 90° arrangement and thus a more perpendicular alignment of these groups (Table 1). The β–(1→4)-glycosidic linkages in cellulose molecules, the methine groups of the glucopyranose rings and the methylene groups of the glucopyranose side are heavily orientation-dependent and reflect the cellulose molecule orientation along the fibre axis.

**Figure 2.** Raman spectra (532 nm excitation, 0.25 s integration time, 10 accumulations, baseline corrected) acquired from a Ramie fibre (>95% cellulose, high crystallinity, microfibrils aligned parallel to the fibre axis) with changing the polarization direction of the incident laser from 0° (parallel to the fibre axis and cellulose fibrils) to 90° (perpendicular to the fibre axis).

168 Cellulose – Biomass Conversion

axis.

role in the native plant cell walls and as water has relatively low polarizabilities weak Raman intensities are observed. Consequently, water saturated samples can be measured without problems. Raman investigations of cellulosic feedstock started in the 80s with the

The cellulose microfibrils give a Raman signature comprising about 15 different significant bands (Figure 2, ramie fibre: almost pure cellulose). If these microfibrils are aligned with a preferred orientation, the Raman intensity of the cellulose bands depends on the angle between the orientation of the cellulose microfibrils and the laser polarisation direction [138]. The investigated Ramie fibres have almost perfect alignment of the cellulose microfibril parallel to the fibre axis and high crystallinity (X-ray results, not shown). Changing the laser polarization from parallel with respect to the fibre axis (0°) to perpendicular to the fibre (90°) results in severe changes of the Raman intensity of almost all characteristic bands (Figure 2) except the two bands at 1377 and 437 cm-1. The bands at 1457 cm-1 (HCH and HOC bending), 517 cm-1, 499 cm-1 and 378 cm-1 (heavy atom stretching) show higher intensity in 90° arrangement and thus a more perpendicular alignment of these groups (Table 1). The β–(1→4)-glycosidic linkages in cellulose molecules, the methine groups of the glucopyranose rings and the methylene groups of the glucopyranose side are heavily orientation-dependent and reflect the cellulose molecule orientation along the fibre

**Figure 2.** Raman spectra (532 nm excitation, 0.25 s integration time, 10 accumulations, baseline corrected) acquired from a Ramie fibre (>95% cellulose, high crystallinity, microfibrils aligned parallel to the fibre axis) with changing the polarization direction of the incident laser from 0° (parallel to the

2897

wavenumber [cm-1] 3500 3250 3000 2750 1750 1500 1250 1000 750 500 250

 0° 30° 60° 90°

1098

fibre axis and cellulose fibrils) to 90° (perpendicular to the fibre axis).

Raman Intensity [CCDcts]

0

100

200

300

400

acquisition of spectra of cellulose fibres and wood [138-140].

**3.1. Cellulose microfibrils: The structural elements of the cell wall**

**Table 1.** Position and assignment [138, 141] of the cellulose bands (Ramie fibre, Figure 2) and their preferred molecular orientation parallel (II) or perpendicular (╪) to the fibre axis direction (=laser polarisation direction). The sensitivity of the bands to intensity changes due to orientation is referred to as no changes (◦) and strong (s) or medium (m). (def: deformation, sym: symmetric, asym: antisymmetric)

Because of the orientation-dependence of the cellulose band intensities, the fibre direction (plant axis) and the laser polarization have to be adjusted in a known and defined way in every plant cell wall Raman experiment. As the intensity of multiple bands change in a characteristic way (up and down, Figure 2), it is possible to distinguish between intensity changes due to alterations in fibre orientation from those resulting from different cellulose content (all bands increase or decrease). To eliminate intensity changes due to different focal plane during rotating the polarizer or drift of the scan stage band height ratios or band area ratios can be calculated for a more detailed analysis (Figure 3A). These ratios also allow the comparison of Raman measurements with different integration times and thus intensity. The ratios (2897/1095, 378/1095 and 1377/1095) reveal a clear dependency of the cellulose band intensities and the angle of the incident laser polarization. The strong relationship can be described by a cosine function and a quadratic regression (R2>0.99). Based on the band height ratios (Figure 3B) or partial least square regression models the angle of the cellulose molecule with respect to the laser polarisation direction and consequently the microfibril angle can be calculated [142]. Noteworthy, changes in fibre orientation often correspond to alterations in cellulose crystallinity. The effect of changes in crystallinity on the shape of the cellulose Raman bands was also investigated in detail: amorphous cellulose results in a significant decline in band heights accompanied by band broadening [143].

Raman Imaging of Lignocellulosic Feedstock 171

coniferyl, and sinapyl alcohols [151, 152]. The structural organisation of lignin is a subject of much debate, both in terms of chemical structure (H (p-hydroxyphenyl), G (guaiacyl) and S (syringyl) units/monomers and the bondings) and in terms of the degree to which lignin is ordered within its cell wall environment. Beside NMR and IR spectroscopy also Raman microscopy has shown high potential for non-invasive investigation of *in-situ* cell wall lignin

Laser-induced autofluorescence from lignin can be the major hindrance to acquire reasonably good Raman spectra because the fluorescence intensity can be several orders of magnitude larger than the Raman scattering intensity. Traditionally, two sampling procedures were used to effectively reduce the autofluorescence: water immersion technique (usable for woody tissues) [140] and oxygen flushing technique [155]. Fluorescence problems can be reduced by choosing the near-IR Fourier transform (NIR-FT) Raman technique, using a NIR laser source with the photon energy well below troublesome low energy electronic transitions of lignin. Good quality spectra, relatively free of fluorescence interference, have been acquired from various lignin-containing materials [156-161]. Today, also more sophisticated spectroscopic methods can overcome this problem. UV resonance Raman spectroscopy exploits the combined benefit of the resonantly enhanced Raman signal and the usually relatively much longer wavelengths of fluorescence emission compared to Raman photons [153, 162-164]. By Kerr-gated Raman spectroscopy the different time-domain characteristics of fluorescence and Raman emission allow the detector only to see a narrow time-domain window centred on the excitation laser pulse [93, 164]. Also CARS gives spectra free of background from onephoton-excited fluorescence and has been used to study lignin modification in alfalfa [165]. All the significant instrumental developments opened up new fields for

Improvements in Confocal Raman mapping/imaging approaches have provided insights into lignin distribution on the microscale. Due to the high spatial resolution it is possible to acquire spectra comprising only the chemistry of the cell corner, which is in lignocellulosics dominated by lignin contribution (Figure 4). The imaging approach requires short integration times (e.g. 0.1-0.4 s) and therefore not all lignin bands are resolved and the spectra are dominated by the strong band around 1600 cm-1 (Figure 4A), which is assigned to aryl stretching vibrations [166]. As this band has no overlap with the carbohydrate bands it can be used as a marker to image lignin distribution on the micron level [167, 168]. Depending on laser excitation and lignin structure, more or less background or resonance enhancement is observed. Due to the different chemical structure of softwood and hardwood lignin particular laser excitation (e.g. 532 nm) results in a higher fluorescence background and stronger 1607 cm-1 band intensity in spruce than in poplar. In softwood species, the most abundant precursor is coniferyl alcohol, which leads to an aromatic substitution by one methoxyl group, known as a guaiacyl structure (G lignin). In hardwood additionally sinapyl alcohol leads to syringyl structures (S lignin) with two methoxyl groups attached to the aromatic ring. Additionally, during the formation of the middle lamella

structure during the last years e.g. [153, 154].

investigating lignified samples.

**Figure 3. A-B.** Relationship between the intensity ratios of cellulose bands and the angle of laser polarization α (A, B: accounting for a cosine function with a quadratic regression)

#### **3.2. Carbohydrate matrix polymers: Hemicelluloses and pectin**

Hemicelluloses and cellulose have similar functional groups and chemical bonds and therefore the Raman contributions are overlapping. Due to the more amorphous nature of hemicelluloses the Raman signal intensity is less and the bands are usually broader [144]. According to Himmelsbach et al. [145] the weak bands between 870–800 and 515–475 cm-1 offer the potential to distinguish between cellulose and xylan in flax fibres. In *Miscanthus* a characteristic band at 478 cm-1, assigned to HCC and HCO bending at C6 of hemicelluloses was used to present the hemicellulose distribution [146]. Nevertheless within wood and pulp samples the bands typical for glucomannan and xylan were hardly detected [144]. Distinguishing and probing the hemicelluloses in cell walls with the Raman imaging approach is therefore not that straightforward and needs sometimes more sophisticated data analysis tools to resolve the overlapping and less intense characteristic bands.

While cellulose and hemicelluloses have β-glycosidic bonds, pectins are composed of αglycosidic linkages. In the Raman spectrum the region between 860-825 cm-1 corresponds to equatorial anomeric H (α-anomers and α-glycosides), whereas the band at about 900–880 cm-1 corresponds to axial anomeric H (β-anomers and β-glycosides) [147]. The sharp Raman band between 860 and 854 cm-1 is characteristic for pectin and shows no overlap with the other plant cell wall polymers and can therefore be used as a marker band in the imaging approach [148, 149]. Furthermore the exact position of this band is sensitive to the state of uronic carboxyls and to *O*-acetylation thus providing insight into pectin structure; decreasing with methylation (min. 850 cm-1) and increasing with acetylation (max. 862 cm-1) [150].

#### **3.3. The aromatic lignin polymer: Fluorescence and diversity**

The structure of lignin is comprised of a variety of different types of covalent bonds derived from oxidative coupling of three different types of phenolic precursor units, p-coumaryl, coniferyl, and sinapyl alcohols [151, 152]. The structural organisation of lignin is a subject of much debate, both in terms of chemical structure (H (p-hydroxyphenyl), G (guaiacyl) and S (syringyl) units/monomers and the bondings) and in terms of the degree to which lignin is ordered within its cell wall environment. Beside NMR and IR spectroscopy also Raman microscopy has shown high potential for non-invasive investigation of *in-situ* cell wall lignin structure during the last years e.g. [153, 154].

170 Cellulose – Biomass Conversion

0.0 0.2

0.4

Intensity Ratio [ ]

0.6 0.8

1.0

**Figure 3. A-B.** Relationship between the intensity ratios of cellulose bands and the angle of laser

Hemicelluloses and cellulose have similar functional groups and chemical bonds and therefore the Raman contributions are overlapping. Due to the more amorphous nature of hemicelluloses the Raman signal intensity is less and the bands are usually broader [144]. According to Himmelsbach et al. [145] the weak bands between 870–800 and 515–475 cm-1 offer the potential to distinguish between cellulose and xylan in flax fibres. In *Miscanthus* a characteristic band at 478 cm-1, assigned to HCC and HCO bending at C6 of hemicelluloses was used to present the hemicellulose distribution [146]. Nevertheless within wood and pulp samples the bands typical for glucomannan and xylan were hardly detected [144]. Distinguishing and probing the hemicelluloses in cell walls with the Raman imaging approach is therefore not that straightforward and needs sometimes more sophisticated data

A B

0.0

0.2

0.4

Intensity Ratio [ ]

0.6

0.8

0.0 0.2 0.4 0.6 0.8 1.0

1.0 2897/1095

cos2 () [ ]

 378/1095 1377/1095

While cellulose and hemicelluloses have β-glycosidic bonds, pectins are composed of αglycosidic linkages. In the Raman spectrum the region between 860-825 cm-1 corresponds to equatorial anomeric H (α-anomers and α-glycosides), whereas the band at about 900–880 cm-1 corresponds to axial anomeric H (β-anomers and β-glycosides) [147]. The sharp Raman band between 860 and 854 cm-1 is characteristic for pectin and shows no overlap with the other plant cell wall polymers and can therefore be used as a marker band in the imaging approach [148, 149]. Furthermore the exact position of this band is sensitive to the state of uronic carboxyls and to *O*-acetylation thus providing insight into pectin structure; decreasing with methylation

The structure of lignin is comprised of a variety of different types of covalent bonds derived from oxidative coupling of three different types of phenolic precursor units, p-coumaryl,

polarization α (A, B: accounting for a cosine function with a quadratic regression)

 2897/1095 378/1095 1377/1095

**3.2. Carbohydrate matrix polymers: Hemicelluloses and pectin**

analysis tools to resolve the overlapping and less intense characteristic bands.

(min. 850 cm-1) and increasing with acetylation (max. 862 cm-1) [150].

**3.3. The aromatic lignin polymer: Fluorescence and diversity**

Laser-induced autofluorescence from lignin can be the major hindrance to acquire reasonably good Raman spectra because the fluorescence intensity can be several orders of magnitude larger than the Raman scattering intensity. Traditionally, two sampling procedures were used to effectively reduce the autofluorescence: water immersion technique (usable for woody tissues) [140] and oxygen flushing technique [155]. Fluorescence problems can be reduced by choosing the near-IR Fourier transform (NIR-FT) Raman technique, using a NIR laser source with the photon energy well below troublesome low energy electronic transitions of lignin. Good quality spectra, relatively free of fluorescence interference, have been acquired from various lignin-containing materials [156-161]. Today, also more sophisticated spectroscopic methods can overcome this problem. UV resonance Raman spectroscopy exploits the combined benefit of the resonantly enhanced Raman signal and the usually relatively much longer wavelengths of fluorescence emission compared to Raman photons [153, 162-164]. By Kerr-gated Raman spectroscopy the different time-domain characteristics of fluorescence and Raman emission allow the detector only to see a narrow time-domain window centred on the excitation laser pulse [93, 164]. Also CARS gives spectra free of background from onephoton-excited fluorescence and has been used to study lignin modification in alfalfa [165]. All the significant instrumental developments opened up new fields for investigating lignified samples.

Improvements in Confocal Raman mapping/imaging approaches have provided insights into lignin distribution on the microscale. Due to the high spatial resolution it is possible to acquire spectra comprising only the chemistry of the cell corner, which is in lignocellulosics dominated by lignin contribution (Figure 4). The imaging approach requires short integration times (e.g. 0.1-0.4 s) and therefore not all lignin bands are resolved and the spectra are dominated by the strong band around 1600 cm-1 (Figure 4A), which is assigned to aryl stretching vibrations [166]. As this band has no overlap with the carbohydrate bands it can be used as a marker to image lignin distribution on the micron level [167, 168]. Depending on laser excitation and lignin structure, more or less background or resonance enhancement is observed. Due to the different chemical structure of softwood and hardwood lignin particular laser excitation (e.g. 532 nm) results in a higher fluorescence background and stronger 1607 cm-1 band intensity in spruce than in poplar. In softwood species, the most abundant precursor is coniferyl alcohol, which leads to an aromatic substitution by one methoxyl group, known as a guaiacyl structure (G lignin). In hardwood additionally sinapyl alcohol leads to syringyl structures (S lignin) with two methoxyl groups attached to the aromatic ring. Additionally, during the formation of the middle lamella p-coumaryl alcohol precursors are present and p-hydroxphenyl lignin without methoxyl groups is formed. The differences in lignin structure in the cell corner of spruce and poplar are reflected by the different intensity and band shape in the region at about 1600 cm-1 as well as in the other bands (Figure 4A). Contribution from coniferaldehyde units is expected at 1623 and 1660 cm-1, whereas coniferyl alcohol contributes at 1654 cm-1 as well as other chromophores [169]. Using these bands the amount of coniferyl alcohol and aldehyde groups compared to the total amount of lignin was imaged in pine and spruce wood samples [170]. For the S units the intense band at 1328 cm-1 is characteristic, while in spruce the band is found at 1334 cm-1 [144] and accompanied by bands (shoulders) below and above (Figure 4A, Table 2). The relatively intensive band at about 1150 cm-1 in poplar wood was tentatively assigned (Table 2). On the contrary, in spruce the band at 1139 cm-1 is stronger (Figure 4A).

Raman Imaging of Lignocellulosic Feedstock 173

antisymmetric C-H str. in OCH3 (SW) [178] and (HW) [179]; symmetric C-H str. in CH3 in

plus C=O str. of coniferaldehyde [178, 179]

C-H3 def. in O-CH3 [179]; C-H2 scissoring; guaiacyl ring vibration (SW) [178, 179] and to

aliphatic O-H bend. (SW) [144, 178], and Slignin (HW) [177], possibly contribution from

aryl-O str. of aryl-OH and aryl-O-CH3; guaiacyl ring (with C=O group) mode (SW)

contribution from carbohydrate [177]

a mode of coniferaldehyde unit (SW) [178]; aromatic C-H in plane def. (guaiacyl type)

[144, 178, 179], HW [177, 179]

C-H3 def. in O-CH3 (HW) [166, 179]

Spruce Poplar *Brachypodium* Assignment [Reference]

FA [180]

1657 1657 ring-conjugated C=C str. of coniferyl alcohol

1599 1600 1607 symmetric aryl ring str. [144, 166-168] 1503 1498 1505 antisymmetric aryl ring str. [178], in FA

<sup>1632</sup>str. of C=C from propenoic acid side chain of FA [180]

cellulose

1176 aryl-H def. [180] <sup>1150</sup>eventually O-CH3 def. [166]; possibly

[181]

**Table 2.** Position and assignment of lignin bands in spectra acquired from the cell corners of spruce, poplar, and *Brachypodium*; FA: ferulic acid; SW: softwood; HW: hardwood; str: stretching vibration;

**4. Raman imaging of wood: Revealing lignification on the micron level**

In the future, wood will play a crucial role in carbon capture and is a fundamental feedstock for bio-based fuels, chemicals, materials, and power. Currently, the greatest processing challenge is to develop efficient deconstruction and separation technologies that enable the release of sugar and aromatic compounds 'locked in' the intricacy of wood cell wall macromolecular structures [182]. To tackle this challenge detailed knowledge on the molecular composition of the cell walls within the different cell wall tissues and layers is of importance and Raman microscopy may contribute to make progress. As a non-destructive method, characterisation of the native cell walls is possible as well as the *in-situ* monitoring

By calculating the integral of the bands in the Raman spectra of plant cell walls the distribution of different molecular structures can be imaged on the micron-level [148, 149,

Wavenumber [cm-1]

2936 2944 2941

1271 1274 1271

bend: bending vibration; def: deformation vibration

of the deconstruction during different treatments.

1458 1458

1334 1328

1139

**Figure 4. A-B**. Raman spectra (532 nm excitation, 0.1-0.4 s integration time) acquired from the cell corner of wood (poplar and spruce, A) and *Brachypodium* (B), reflecting the different lignin structures in softwood, hardwood and grasses. Additionally a reference spectrum of ferulic acid is plotted (B, black line)

Grasses have Type II cell walls, which in addition to other cell wall polymers, typically contain arabinoxylans and phenolics [171-173]. Noteworthy, grass xylans play an important role in the cell wall by helping to facilitate the assembly of cellulose microfibrils or/and the cross-linking of lignin to polysaccharides with the aid of hydroxycinnamic acids [174]. When compared to dicots, a high amount of hydroxycinnamic acids (ferulic and p-coumaric acid) is characteristic for grass cell walls. Therefore the cell corner spectrum of *Brachypodium* (Figure 4B) differs remarkably from the wood spectra (Figure 4A). The very strong aryl stretching vibration at 1607 cm-1 is a clear doublet with a second band at 1632 cm-1, accompanied by a weaker band at 1701 cm-1. This doublet peak as well as the bands at 1176 cm-1 and 1276 cm-1 are typical for ferulic acid [175, 176] (Figure 4B). So the cell corner spectra of *Brachypodium* clearly reflect contributions from ferulic acid. Similar bands have been found in corn stover, although not recognized to be due to ferulic acid [177].

172 Cellulose – Biomass Conversion

stronger (Figure 4A).

line)

p-coumaryl alcohol precursors are present and p-hydroxphenyl lignin without methoxyl groups is formed. The differences in lignin structure in the cell corner of spruce and poplar are reflected by the different intensity and band shape in the region at about 1600 cm-1 as well as in the other bands (Figure 4A). Contribution from coniferaldehyde units is expected at 1623 and 1660 cm-1, whereas coniferyl alcohol contributes at 1654 cm-1 as well as other chromophores [169]. Using these bands the amount of coniferyl alcohol and aldehyde groups compared to the total amount of lignin was imaged in pine and spruce wood samples [170]. For the S units the intense band at 1328 cm-1 is characteristic, while in spruce the band is found at 1334 cm-1 [144] and accompanied by bands (shoulders) below and above (Figure 4A, Table 2). The relatively intensive band at about 1150 cm-1 in poplar wood was tentatively assigned (Table 2). On the contrary, in spruce the band at 1139 cm-1 is

**Figure 4. A-B**. Raman spectra (532 nm excitation, 0.1-0.4 s integration time) acquired from the cell corner of wood (poplar and spruce, A) and *Brachypodium* (B), reflecting the different lignin structures in softwood, hardwood and grasses. Additionally a reference spectrum of ferulic acid is plotted (B, black

found in corn stover, although not recognized to be due to ferulic acid [177].

Grasses have Type II cell walls, which in addition to other cell wall polymers, typically contain arabinoxylans and phenolics [171-173]. Noteworthy, grass xylans play an important role in the cell wall by helping to facilitate the assembly of cellulose microfibrils or/and the cross-linking of lignin to polysaccharides with the aid of hydroxycinnamic acids [174]. When compared to dicots, a high amount of hydroxycinnamic acids (ferulic and p-coumaric acid) is characteristic for grass cell walls. Therefore the cell corner spectrum of *Brachypodium* (Figure 4B) differs remarkably from the wood spectra (Figure 4A). The very strong aryl stretching vibration at 1607 cm-1 is a clear doublet with a second band at 1632 cm-1, accompanied by a weaker band at 1701 cm-1. This doublet peak as well as the bands at 1176 cm-1 and 1276 cm-1 are typical for ferulic acid [175, 176] (Figure 4B). So the cell corner spectra of *Brachypodium* clearly reflect contributions from ferulic acid. Similar bands have been

A B

**Table 2.** Position and assignment of lignin bands in spectra acquired from the cell corners of spruce, poplar, and *Brachypodium*; FA: ferulic acid; SW: softwood; HW: hardwood; str: stretching vibration; bend: bending vibration; def: deformation vibration

#### **4. Raman imaging of wood: Revealing lignification on the micron level**

In the future, wood will play a crucial role in carbon capture and is a fundamental feedstock for bio-based fuels, chemicals, materials, and power. Currently, the greatest processing challenge is to develop efficient deconstruction and separation technologies that enable the release of sugar and aromatic compounds 'locked in' the intricacy of wood cell wall macromolecular structures [182]. To tackle this challenge detailed knowledge on the molecular composition of the cell walls within the different cell wall tissues and layers is of importance and Raman microscopy may contribute to make progress. As a non-destructive method, characterisation of the native cell walls is possible as well as the *in-situ* monitoring of the deconstruction during different treatments.

By calculating the integral of the bands in the Raman spectra of plant cell walls the distribution of different molecular structures can be imaged on the micron-level [148, 149, 167, 183]. Figure 5A shows an example of imaging water uptake of cell walls in young poplar wood (*Populus nigra x Populus deltoids*) by plotting the integral of the OH stretching vibration. As the sample was water saturated, highest intensity (blue colour) is observed in the water filled lumen of the cells. The border to the cell wall is slightly visible as less blue, followed by remarkable high intensity of the inner cell wall layer. Clear distinct layers separating the cells are visualised black and thus less hydrated areas. Integrating the strong aromatic aryl stretching vibration gives the opposite picture (Figure 5B): high intensity (red colour) in the cell corner (CC) and compound middle lamella (CML) and the rest of the cell walls are displayed black as intensity is less than to be seen with this scaling. In contrast the inner layer (gelatinous (G-) layer) of the poplar tension wood can be visualised selectively by integrating the 1380 cm-1 cellulose band. This band is not sensitive to changes in cellulose orientation (Figure 2A). In lignified samples the band becomes a shoulder the more the adjacent lignin band rises and therefore only in cellulosic cell wall regions without (or very minor) lignin the band is clearly resolved and shows high intensity by integration. So the black regions of the images not necessarily represent non-cellulosic regions, but regions where cellulose is accompanied by higher lignin content. Based on the lignin and cellulose images three cell wall regions have been selected by using an intensity threshold (Figure 5D): 1) lignin intensity higher than 3000 cts (red), 2) lignin intensity between 1000 and 3000 cts (pink) and 3) cellulose intensity higher than 150 cts (green). The highest lignin content comprises the CC regions as well as part of the vessel walls (v) (Figure 5D, red). Medium lignin content displays the CML (and probably part of the S2 layer) as well as the ray cells (Figure 5D, pink) and the non-(or minor) lignified region is restricted to the inner cell wall (Figure 5D, green). For a detailed analysis of the cell wall regions, average spectra can be calculated from the defined cell wall regions of the sample (Figure 5E). For better comparison spectra have been baseline corrected and normalised to the aromatic lignin stretching vibrations. The spectra show that lignin is present in the green coloured cell wall region (G-layer) in minor amounts and the carbohydrate bands (C) are in relation much higher as well as the OH and CH stretching vibrations. Numerous OH-groups are present in the carbohydrates and show a contribution in the cellulose Raman spectra (Figure 2). But the height and form of the OH-bands in the G-layer are more comparable to spectra from the free water in the lumen and thus point to access of water uptake in this inner layer. In the medium lignified region (pink), similar cellulose bands are detected, but only one third of intensity compared to the inner layer. In the highly lignified region the lignin signature dominates (Figure 5E, red line). Yet carbohydrate bands are abundant, mainly because also the highly lignified vessel walls (v) are partly included in the average spectra calculation. Vessel spectra show higher amount of carbohydrates than spectra from CML (not shown). A zoom into the "lignin region" shows that not only the amount changes on the micron level, but also the lignin structure: a slight shift in band position of the aromatic stretching vibration is observed and the height of the shoulder at about 1657 cm-1 changes. As the sampled region is near the cambium and the higher shoulder points to the alcohol and aldehyde lignin precursors in the G-layer and S2, lignification process in these regions might be in progress. Interestingly also spectra derived from substances within the rays point to aromatic compounds. These deposits could be visualised selectively by integrating from Raman Imaging of Lignocellulosic Feedstock 175

785-486 cm-1. Detailed analysis of the spectra and band assignment will reveal the composition of these ray components. Several studies have shown that molecular structures,

**Figure 5. A-F**: Raman images of poplar tension wood based on integrating the OH stretching region (A), the lignin marker band at about 1600 cm-1 (B) and the cellulose band at about 1388 cm-1 (C). In a combined image (D), regions with lignin intensity higher 3000 cts (red) and between 1000 and 3000 cts (pink) and with cellulose intensity >150 cts (green) are displayed. From these defined regions average spectra (baseline corrected and normalised to the 1600 cm-1 band) have been calculated (E). The content of the rays is visualized by integrating from 785-486 cm-1. (Experimental: 532 nm excitation, 100x oil immersion objective, scan area: 120 µm x 100 µm, Pixels: 360x300, integration time: 0.4 s) (CC: cell

The example of poplar tension wood showed the potential of Raman imaging to get a detailed view on the molecular structure on the micron level. Distinguishing cell wall types based on their chemistry gives an overview of the tissue composition. Furthermore extracting the underlying Raman spectra for detailed analysis can elucidate specific insights into the molecular structure and composition. The position resolved micro-resolution opens up new ways for understanding biosynthesis (especially lignification) as gradients in developing tissues can be followed cell by cell and cellular components investigated together with the cell wall itself. Different performance and reactions upon treatment can be resolved on the cell wall level and help to understand recalcitrance of wood. Different species have different chemical composition and lignin structures and recently a clear distinction between pine and spruce in terms of the distribution of coniferyl alcohol and coniferyl aldehyde was recognized using the Raman imaging approach [170]. Furthermore changes due to environmental growth conditions or genetic engineering can be evaluated. By comparing lignification in wild-type and lignin-reduced 4CL transgenic *Populus trichocarpa* stem wood, it was shown that transgenic reduction of lignin is particularly pronounced in the S2 wall layer of fibres, suggesting that such transgenic approach may help overcome cell wall recalcitrance to wood saccharification [186]. A higher volume of

corner, CML: compound middle lamella, G: gelatinous layer, v: vessel)

e.g. lignin structure are reflected in the Raman signature [154, 161, 170, 184, 185].

785-486 cm-1. Detailed analysis of the spectra and band assignment will reveal the composition of these ray components. Several studies have shown that molecular structures, e.g. lignin structure are reflected in the Raman signature [154, 161, 170, 184, 185].

174 Cellulose – Biomass Conversion

167, 183]. Figure 5A shows an example of imaging water uptake of cell walls in young poplar wood (*Populus nigra x Populus deltoids*) by plotting the integral of the OH stretching vibration. As the sample was water saturated, highest intensity (blue colour) is observed in the water filled lumen of the cells. The border to the cell wall is slightly visible as less blue, followed by remarkable high intensity of the inner cell wall layer. Clear distinct layers separating the cells are visualised black and thus less hydrated areas. Integrating the strong aromatic aryl stretching vibration gives the opposite picture (Figure 5B): high intensity (red colour) in the cell corner (CC) and compound middle lamella (CML) and the rest of the cell walls are displayed black as intensity is less than to be seen with this scaling. In contrast the inner layer (gelatinous (G-) layer) of the poplar tension wood can be visualised selectively by integrating the 1380 cm-1 cellulose band. This band is not sensitive to changes in cellulose orientation (Figure 2A). In lignified samples the band becomes a shoulder the more the adjacent lignin band rises and therefore only in cellulosic cell wall regions without (or very minor) lignin the band is clearly resolved and shows high intensity by integration. So the black regions of the images not necessarily represent non-cellulosic regions, but regions where cellulose is accompanied by higher lignin content. Based on the lignin and cellulose images three cell wall regions have been selected by using an intensity threshold (Figure 5D): 1) lignin intensity higher than 3000 cts (red), 2) lignin intensity between 1000 and 3000 cts (pink) and 3) cellulose intensity higher than 150 cts (green). The highest lignin content comprises the CC regions as well as part of the vessel walls (v) (Figure 5D, red). Medium lignin content displays the CML (and probably part of the S2 layer) as well as the ray cells (Figure 5D, pink) and the non-(or minor) lignified region is restricted to the inner cell wall (Figure 5D, green). For a detailed analysis of the cell wall regions, average spectra can be calculated from the defined cell wall regions of the sample (Figure 5E). For better comparison spectra have been baseline corrected and normalised to the aromatic lignin stretching vibrations. The spectra show that lignin is present in the green coloured cell wall region (G-layer) in minor amounts and the carbohydrate bands (C) are in relation much higher as well as the OH and CH stretching vibrations. Numerous OH-groups are present in the carbohydrates and show a contribution in the cellulose Raman spectra (Figure 2). But the height and form of the OH-bands in the G-layer are more comparable to spectra from the free water in the lumen and thus point to access of water uptake in this inner layer. In the medium lignified region (pink), similar cellulose bands are detected, but only one third of intensity compared to the inner layer. In the highly lignified region the lignin signature dominates (Figure 5E, red line). Yet carbohydrate bands are abundant, mainly because also the highly lignified vessel walls (v) are partly included in the average spectra calculation. Vessel spectra show higher amount of carbohydrates than spectra from CML (not shown). A zoom into the "lignin region" shows that not only the amount changes on the micron level, but also the lignin structure: a slight shift in band position of the aromatic stretching vibration is observed and the height of the shoulder at about 1657 cm-1 changes. As the sampled region is near the cambium and the higher shoulder points to the alcohol and aldehyde lignin precursors in the G-layer and S2, lignification process in these regions might be in progress. Interestingly also spectra derived from substances within the rays point to aromatic compounds. These deposits could be visualised selectively by integrating from

**Figure 5. A-F**: Raman images of poplar tension wood based on integrating the OH stretching region (A), the lignin marker band at about 1600 cm-1 (B) and the cellulose band at about 1388 cm-1 (C). In a combined image (D), regions with lignin intensity higher 3000 cts (red) and between 1000 and 3000 cts (pink) and with cellulose intensity >150 cts (green) are displayed. From these defined regions average spectra (baseline corrected and normalised to the 1600 cm-1 band) have been calculated (E). The content of the rays is visualized by integrating from 785-486 cm-1. (Experimental: 532 nm excitation, 100x oil immersion objective, scan area: 120 µm x 100 µm, Pixels: 360x300, integration time: 0.4 s) (CC: cell corner, CML: compound middle lamella, G: gelatinous layer, v: vessel)

The example of poplar tension wood showed the potential of Raman imaging to get a detailed view on the molecular structure on the micron level. Distinguishing cell wall types based on their chemistry gives an overview of the tissue composition. Furthermore extracting the underlying Raman spectra for detailed analysis can elucidate specific insights into the molecular structure and composition. The position resolved micro-resolution opens up new ways for understanding biosynthesis (especially lignification) as gradients in developing tissues can be followed cell by cell and cellular components investigated together with the cell wall itself. Different performance and reactions upon treatment can be resolved on the cell wall level and help to understand recalcitrance of wood. Different species have different chemical composition and lignin structures and recently a clear distinction between pine and spruce in terms of the distribution of coniferyl alcohol and coniferyl aldehyde was recognized using the Raman imaging approach [170]. Furthermore changes due to environmental growth conditions or genetic engineering can be evaluated. By comparing lignification in wild-type and lignin-reduced 4CL transgenic *Populus trichocarpa* stem wood, it was shown that transgenic reduction of lignin is particularly pronounced in the S2 wall layer of fibres, suggesting that such transgenic approach may help overcome cell wall recalcitrance to wood saccharification [186]. A higher volume of water was found in the cell wall of transgenic aspen compared with wild-type aspen, indicating an increase in the hydrophilicity of the cell wall [187].

Raman Imaging of Lignocellulosic Feedstock 177

The study of lignification in the monocot cell wall is of particular interest as several studies have demonstrated that lignin and phenolics bound to cell walls counter productively to saccharification yield and ruminant digestibility by reducing the accessibility of degrading/digestive enzymes to polysaccharides in the cell wall [45, 190]. Furthermore, a secondary, unintended effect of pre-treatments commonly used to reduce lignin content prior to saccharification for bioethanol production results in residual byproducts that inhibits growth of microorganisms used during fermentation. Therefore the natural resistance of lignocellulosic plant material to degradation serves as a major obstacle to efficient conversion of cellulose into fermentable sugars used for bioenergy [31, 191]. Within the monocot stems lignin is found in many tissues and cell types; the highest amount in the xylem tissue (Figure 6B). Phlorglucinol HCl staining gives insight into thevariability of lignification, but it is unspecific to different lignin and phenolic acid structures. As Raman images are based on underlying spectra, which represent a molecular fingerprint at every point within the acquired images, more detailed

Raman images of young (3 week old) basal internodes show point-wise accumulations of aromatic substances within the xylem tissues, while in the surrounding sclerenchyma fibres cell walls are visualized to be more homogenous (Figure 7A). In the lumen of the xylem cells remarkably high aromatic intensity is observed from deposits, which have not yet been further analysed. By integrating the marker band of ferulic acid at 1176 cm-1 (Figure 4B) again the point-wise accumulation within the xylem becomes visible, but less intensity is observed in the sclerenchyma fibres (Figure 7B). As stated previously grass cell walls contain high amounts of p-hydroxycinnamic acids, particularly ferulic (FA) and p-coumaric (pCA). Previous studies have demonstrated that both pCA and FA play an important functional role in the incorporation of lignin into the cell wall by aiding to establish ester or/and ether-linkages to cell wall polymers [192]. It was shown that ferulate esters act as initiation or nucleation sites of lignin deposition in grasses [193]. Ferulate molecules connect lignin to arabinoxylans primarily through ester-ether bonds and form dimeric structures cross-linking arabinoxylan chains to

By integrating the carbohydrate band at about 903 cm-1, slightly higher intensity in the sclerenchyma cells was observed (Figure 7C). On the contrary to the poplar wood cells (Figure 5A-F) no clear cell corners are seen within the young xylem and sclerenchyma fibres and differences in the distribution of aromatic and carbohydrate substances within the scanned cell wall area are less pronounced when applying the band integration approach

Nevertheless, a cluster analysis performed with derivatives of baseline corrected spectra reveals high heterogeneity of the spectra in the lumen and on the border of the cell walls (Figure 8A) as well as clear separation of the xylem cell wall and the sclerenchyma cell wall (Figure 8B). Based on the found clusters, average spectra can be calculated

information can be gained.

polysaccharides [194].

for image calculation.

### **5. Heterogeneity of lignin in the internode of the model grass**  *Brachypodium distachyon*

Grasses (or *Poaceae*, monocotyledon) are important plants on earth as they promote life and provide nutrients to both humans and animals. It is estimated that cereal grasses, i.e., corn, wheat, rice, oats, and barley, cover roughly 20% of the world's land surface and that the demand for these plants will remain high as the human population continues to increase [188]. Furthermore cereal grasses, including their grain and straw, are today not only used as sources of food but are also considered viable material for bioenergy. The grasses dedicated to biomass production, i.e., Switchgrass (*Panicum virgatum*) and Miscanthus (*Miscanthus sacchariflorus*, *M. sinensis* or *M. giganteus*) are mainly C4 plants that when grown in warm-environmental conditions, are more efficient in photosynthesis, nutrient-use and water-use [33]. The major limitations to the direct study of C4 grasses include the large size of the plants (requires land space), long generation times, and demanding growth requirements. Therefore several model plants, e.g., rice and more recently *Brachypdium distachyon*, were domesticated for laboratory studies (Figure 6A). *Brachypodium* now serves as an ideal experimental model for studying cereal grasses as it has many of the desirable traits for plant model systems including a small genome size, short generation time, the ability to self-pollinate, minimal input requirements and more importantly an amenability to forward and reverse genetic techniques [189]. Since 2001, *Brachypodium* has been established as a model however only recently have researchers begun to take advantage of this plant to study the monocot cell wall.

**Figure 6. A-B**: *Brachypodium distachyon*: Lateral view of *Brachypodium* development in the growth chamber (A). Cross section of a seven week old basal internode labelled with phloroglucinol-HCl (B). Dark labelling shows lignified cell walls. (Cell type abbreviations: Ep: epidermis; Sc: sclerenchyma; Ph: phloem; mXy: metaxylum; pXy: protoxylum; Pa: parenchyma)

The study of lignification in the monocot cell wall is of particular interest as several studies have demonstrated that lignin and phenolics bound to cell walls counter productively to saccharification yield and ruminant digestibility by reducing the accessibility of degrading/digestive enzymes to polysaccharides in the cell wall [45, 190]. Furthermore, a secondary, unintended effect of pre-treatments commonly used to reduce lignin content prior to saccharification for bioethanol production results in residual byproducts that inhibits growth of microorganisms used during fermentation. Therefore the natural resistance of lignocellulosic plant material to degradation serves as a major obstacle to efficient conversion of cellulose into fermentable sugars used for bioenergy [31, 191]. Within the monocot stems lignin is found in many tissues and cell types; the highest amount in the xylem tissue (Figure 6B). Phlorglucinol HCl staining gives insight into thevariability of lignification, but it is unspecific to different lignin and phenolic acid structures. As Raman images are based on underlying spectra, which represent a molecular fingerprint at every point within the acquired images, more detailed information can be gained.

176 Cellulose – Biomass Conversion

*Brachypodium distachyon*

this plant to study the monocot cell wall.

water was found in the cell wall of transgenic aspen compared with wild-type aspen,

Grasses (or *Poaceae*, monocotyledon) are important plants on earth as they promote life and provide nutrients to both humans and animals. It is estimated that cereal grasses, i.e., corn, wheat, rice, oats, and barley, cover roughly 20% of the world's land surface and that the demand for these plants will remain high as the human population continues to increase [188]. Furthermore cereal grasses, including their grain and straw, are today not only used as sources of food but are also considered viable material for bioenergy. The grasses dedicated to biomass production, i.e., Switchgrass (*Panicum virgatum*) and Miscanthus (*Miscanthus sacchariflorus*, *M. sinensis* or *M. giganteus*) are mainly C4 plants that when grown in warm-environmental conditions, are more efficient in photosynthesis, nutrient-use and water-use [33]. The major limitations to the direct study of C4 grasses include the large size of the plants (requires land space), long generation times, and demanding growth requirements. Therefore several model plants, e.g., rice and more recently *Brachypdium distachyon*, were domesticated for laboratory studies (Figure 6A). *Brachypodium* now serves as an ideal experimental model for studying cereal grasses as it has many of the desirable traits for plant model systems including a small genome size, short generation time, the ability to self-pollinate, minimal input requirements and more importantly an amenability to forward and reverse genetic techniques [189]. Since 2001, *Brachypodium* has been established as a model however only recently have researchers begun to take advantage of

**Figure 6. A-B**: *Brachypodium distachyon*: Lateral view of *Brachypodium* development in the growth chamber (A). Cross section of a seven week old basal internode labelled with phloroglucinol-HCl (B). Dark labelling shows lignified cell walls. (Cell type abbreviations: Ep: epidermis; Sc: sclerenchyma; Ph:

phloem; mXy: metaxylum; pXy: protoxylum; Pa: parenchyma)

indicating an increase in the hydrophilicity of the cell wall [187].

**5. Heterogeneity of lignin in the internode of the model grass**

Raman images of young (3 week old) basal internodes show point-wise accumulations of aromatic substances within the xylem tissues, while in the surrounding sclerenchyma fibres cell walls are visualized to be more homogenous (Figure 7A). In the lumen of the xylem cells remarkably high aromatic intensity is observed from deposits, which have not yet been further analysed. By integrating the marker band of ferulic acid at 1176 cm-1 (Figure 4B) again the point-wise accumulation within the xylem becomes visible, but less intensity is observed in the sclerenchyma fibres (Figure 7B). As stated previously grass cell walls contain high amounts of p-hydroxycinnamic acids, particularly ferulic (FA) and p-coumaric (pCA). Previous studies have demonstrated that both pCA and FA play an important functional role in the incorporation of lignin into the cell wall by aiding to establish ester or/and ether-linkages to cell wall polymers [192]. It was shown that ferulate esters act as initiation or nucleation sites of lignin deposition in grasses [193]. Ferulate molecules connect lignin to arabinoxylans primarily through ester-ether bonds and form dimeric structures cross-linking arabinoxylan chains to polysaccharides [194].

By integrating the carbohydrate band at about 903 cm-1, slightly higher intensity in the sclerenchyma cells was observed (Figure 7C). On the contrary to the poplar wood cells (Figure 5A-F) no clear cell corners are seen within the young xylem and sclerenchyma fibres and differences in the distribution of aromatic and carbohydrate substances within the scanned cell wall area are less pronounced when applying the band integration approach for image calculation.

Nevertheless, a cluster analysis performed with derivatives of baseline corrected spectra reveals high heterogeneity of the spectra in the lumen and on the border of the cell walls (Figure 8A) as well as clear separation of the xylem cell wall and the sclerenchyma cell wall (Figure 8B). Based on the found clusters, average spectra can be calculated corresponding to the separated regions. By this (Figure 8C-D) spectral (molecular) characterization of each cell wall region is possible on the micron level. Characteristic bands are observed e.g. for the lumen deposits (yellow line, Figure 8C), which can give insights into the chemical nature of these deposits. Furthermore, the gradual change recognized by the cluster analysis from the lumen towards the xylem cell wall can be analysed in detail. Comparing the xylem and sclerenchyma spectra (Figure 8D) it becomes clear that ferulic acid is much more accumulated in the xylem cells than in the surrounding sclerenchyma cells at this developmental stage of *Brachypodium*. The aryl stretching vibration as well as the marker bands at 1271 and 1176 cm-1 are clearly reduced in the spectrum corresponding to the sclerenchyma cells, whereas the carbohydrate distribution appears comparable (Figure 8D). The spot-wise pattern in the xylem was observed clearly by integrating the area of the bands (Figure 7A-B) and not in the cluster analysis (Figure 8B). Therefore it can be concluded that the spots reflect more changes in intensity (amount) than compositional (structural) changes. By studying the different cell wall types as well as cell lumen ingredients during different developmental stages, insights into lignification will be gained.

Raman Imaging of Lignocellulosic Feedstock 179

**Figure 8. A-D**: Raman images of *Brachypodium distachyon* based on a Cluster analysis using derivatives of baseline corrected spectra of the same measurement as shown in Figure 7. For clarity reasons the 6 calculated clusters are not shown within one image, but in two: A: representing the clusters of the cell lumen and borders towards the cell wall, B: the inner border of the xylem wall (pink) and the xylem wall itself (red) clearly separated from the sclerenchyma cells (green) High variability of the spectra within the cell lumen and its borders is seen (C) as well as clear differences between the sclerenchyma

The demand for plant biomass feedstock will increase as renewable resources get more and more attractive and further fields of utilizations open up. The mechanical performance as well as the recalcitrance of plant biomass to degradation is a function of which cell wall polymers are abundant and how they are cross-linked and aggregated within the walls. For understanding of biomass resources and an optimized utilization these higher order structures have to be probed in their native state on the micro- and nano level. The amount of cellulose as well as its crystallinity, structural arrangement (orientation) and interaction with other wood polymers play a key role in any utilization aspect. The recalcitrance to saccharification is a major limitation for conversion of lignocellulosic biomass to ethanol,

(scl) and xylem (xyl, xyl in) spectra (D).

**6. Conclusion**

**Figure 7. A-C**: Raman images of a cross section of the basal internode of *Brachypodium distachyon* (three weeks old). High aromatic contribution is visualised by integrating from 1535 to 1674 cm-1 in the xylem (xyl) and surrounding sclerenchyma (scl) cells. When the typical band for ferulic acid at 1176 cm-1 is integrated (B) no lumen deposits are seen and less intensity in the sclerenchyma cells. The carbohydrate band at 903 cm-1 (925-887 cm-1) shows higher intensity in the sclerenchyma cells (C). (Experimental: 532 nm excitation, 100x oil immersion objective, scan area: 30 µm x 30 µm, Pixels: 90x90, integration time: 0.3 s)

Recent Raman study on corn stover revealed that lignin and cellulose abundance varies significantly among different cell types: 5-times higher in sclerenchymea cells, 3-times higher in epidermal cells than bundle sheaths and parenchyma cells [177]. They also noted characteristic bands at 1428, 1271, and 1175 cm-1 in corn stover and although not assigned to ferulic acid, it seems that also in corn stover spectral contributions of ferulic acid have been reflected.

**Figure 8. A-D**: Raman images of *Brachypodium distachyon* based on a Cluster analysis using derivatives of baseline corrected spectra of the same measurement as shown in Figure 7. For clarity reasons the 6 calculated clusters are not shown within one image, but in two: A: representing the clusters of the cell lumen and borders towards the cell wall, B: the inner border of the xylem wall (pink) and the xylem wall itself (red) clearly separated from the sclerenchyma cells (green) High variability of the spectra within the cell lumen and its borders is seen (C) as well as clear differences between the sclerenchyma (scl) and xylem (xyl, xyl in) spectra (D).

#### **6. Conclusion**

178 Cellulose – Biomass Conversion

insights into lignification will be gained.

0.3 s)

reflected.

corresponding to the separated regions. By this (Figure 8C-D) spectral (molecular) characterization of each cell wall region is possible on the micron level. Characteristic bands are observed e.g. for the lumen deposits (yellow line, Figure 8C), which can give insights into the chemical nature of these deposits. Furthermore, the gradual change recognized by the cluster analysis from the lumen towards the xylem cell wall can be analysed in detail. Comparing the xylem and sclerenchyma spectra (Figure 8D) it becomes clear that ferulic acid is much more accumulated in the xylem cells than in the surrounding sclerenchyma cells at this developmental stage of *Brachypodium*. The aryl stretching vibration as well as the marker bands at 1271 and 1176 cm-1 are clearly reduced in the spectrum corresponding to the sclerenchyma cells, whereas the carbohydrate distribution appears comparable (Figure 8D). The spot-wise pattern in the xylem was observed clearly by integrating the area of the bands (Figure 7A-B) and not in the cluster analysis (Figure 8B). Therefore it can be concluded that the spots reflect more changes in intensity (amount) than compositional (structural) changes. By studying the different cell wall types as well as cell lumen ingredients during different developmental stages,

**Figure 7. A-C**: Raman images of a cross section of the basal internode of *Brachypodium distachyon* (three weeks old). High aromatic contribution is visualised by integrating from 1535 to 1674 cm-1 in the xylem (xyl) and surrounding sclerenchyma (scl) cells. When the typical band for ferulic acid at 1176 cm-1 is integrated (B) no lumen deposits are seen and less intensity in the sclerenchyma cells. The carbohydrate band at 903 cm-1 (925-887 cm-1) shows higher intensity in the sclerenchyma cells (C). (Experimental: 532 nm excitation, 100x oil immersion objective, scan area: 30 µm x 30 µm, Pixels: 90x90, integration time:

Recent Raman study on corn stover revealed that lignin and cellulose abundance varies significantly among different cell types: 5-times higher in sclerenchymea cells, 3-times higher in epidermal cells than bundle sheaths and parenchyma cells [177]. They also noted characteristic bands at 1428, 1271, and 1175 cm-1 in corn stover and although not assigned to ferulic acid, it seems that also in corn stover spectral contributions of ferulic acid have been The demand for plant biomass feedstock will increase as renewable resources get more and more attractive and further fields of utilizations open up. The mechanical performance as well as the recalcitrance of plant biomass to degradation is a function of which cell wall polymers are abundant and how they are cross-linked and aggregated within the walls. For understanding of biomass resources and an optimized utilization these higher order structures have to be probed in their native state on the micro- and nano level. The amount of cellulose as well as its crystallinity, structural arrangement (orientation) and interaction with other wood polymers play a key role in any utilization aspect. The recalcitrance to saccharification is a major limitation for conversion of lignocellulosic biomass to ethanol, which is mainly due to the lignin content and composition. Therefore improving feedstocks for both animal consumption and for starting material for bioethanol production is proposed through breeding and genetic engineering of lignin. High throughput methods to characterize plant cell walls have become more and more important in order to follow the native variability as well as engineered changes. Both, FT-IR and Raman spectroscopy have given important insights into plant cell wall polymers during the last years. While Raman has the advantage of higher spatial resolution (<0.5 µm) to reveal changes on the cell wall layer level and the possibility of investigating the samples in the wet state, FT-IR is more sensitive to the functional group of hemicelluloses.

Raman Imaging of Lignocellulosic Feedstock 181

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The examples of poplar tension wood and *Brachypodium* have shown the potential of Raman imaging to get a detailed view on the molecular structure on the micron level. Distinguishing cell wall types based on their chemistry gives an overview of the tissue composition. Furthermore extracting the underlying Raman spectra for detailed analysis can elucidate specific insights into the molecular structure and composition. The position resolved micro-resolution opens up new ways for understanding biosynthesis (especially lignification) as gradients in developing tissues can be followed cell by cell and cellular components investigated together with the cell wall itself. Different performance and reactions upon treatment can be resolved on the cell wall level and so-called *in-situ* approaches, watching directly the effect of treatment, will help to understand the performance (e.g. recalcitrance) of plant cell walls.

#### **Author details**

Notburga Gierlinger *BOKU - University of Natural Resources and Life Sciences, Department of Material Sciences and Process Engineering, Vienna, Austria*

Notburga Gierlinger and Tobias Keplinger *Johannes Kepler University Linz, Institute of Polymer Science, Linz, Austria*

Michael Harrington *INRA, UMR 1318, Institut Jean Pierre Bourgin, France AgroParisTech, Institut Jean Pierre Bourgin, France*

Manfred Schwanninger *BOKU – University of Natural Resources and Life Sciences, Department of Chemistry, Vienna, Austria*

#### **Acknowledgement**

N.G. thanks Pierre Conchon and Catherine Coutand (INRA, Clermont Ferrand, France) for providing the poplar wood cross-section. MH is supported by the National Science Foundation (IRFP # 1002683).

#### **7. References**

180 Cellulose – Biomass Conversion

**Author details**

Notburga Gierlinger

Michael Harrington

Manfred Schwanninger

**Acknowledgement**

Foundation (IRFP # 1002683).

sensitive to the functional group of hemicelluloses.

performance (e.g. recalcitrance) of plant cell walls.

*BOKU - University of Natural Resources and Life Sciences,*

*INRA, UMR 1318, Institut Jean Pierre Bourgin, France AgroParisTech, Institut Jean Pierre Bourgin, France*

*BOKU – University of Natural Resources and Life Sciences,*

Notburga Gierlinger and Tobias Keplinger

*Department of Chemistry, Vienna, Austria*

*Department of Material Sciences and Process Engineering, Vienna, Austria*

*Johannes Kepler University Linz, Institute of Polymer Science, Linz, Austria*

N.G. thanks Pierre Conchon and Catherine Coutand (INRA, Clermont Ferrand, France) for providing the poplar wood cross-section. MH is supported by the National Science

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**Chapter 9**

© 2013 Shen et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2013 Shen et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**pyrolytic behavior**

**with the other components**

**3) The interactions among chemical components**

> **Optimization of the processes**

**Figure 1.** The fundamental issues and targets concerning the pyrolysis of cellulose

**Bio-fuels Bio-chemicals Bio-materials Biomass-system**

**The Overview of Thermal Decomposition**

Lignocellulosic biomass including wood, logging residue, crops and agricultural wastes) has been widely utilized to produce energy, fuels or chemicals, acting as the potential renewable source for taking place of fossil energies (such as coal, natural gas and petroleum) [1]. Pyrolysis is proved to be, one of the most promising methods to convert biomass into different products (syn-gas, bio-liquid, char and chemicals), which could essentially

> **1) Physico-chemical structure analysis of biomass**

**Configuration of cellulose Morphological relationship**

**On-line pyrolysis study & Off-line pyrolysis study**

**of Cellulose in Lignocellulosic Biomass**

Dekui Shen, Rui Xiao, Sai Gu and Huiyan Zhang

Additional information is available at the end of the chapter

diversify the energy-supply in many situations [2].

**2) Thermal behaviour of cellulose**

http://dx.doi.org/10.5772/51883

**1. Introduction**

### **The Overview of Thermal Decomposition of Cellulose in Lignocellulosic Biomass**

Dekui Shen, Rui Xiao, Sai Gu and Huiyan Zhang

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/51883

#### **1. Introduction**

192 Cellulose – Biomass Conversion

11480.

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[190] Albert L, Németh ZI, Halász G, Koloszár J, Varga S, Takács L (1999) Radial variation of pH and buffer capacity in the red-heartwooded beech (*Fagus silvatica* L.) wood. Holz als

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[194] Ralph J, Hatfield RD, Sederoff RR, MacKay JJ (1998) Order and randomness in lignin and lignification: Is a new paradigm for lignification required? Research Summaries: 39-

low lignin transgenic *Populus trichocarpa*. Planta 230(3): 589-597.

Holzforschung DOI 10.1515/hf-2011-0126.

in Grasses. Plant Physiol. 127(4): 1539-1555.

understanding plant cell walls. Science 206: 2206-2211.

Roh- und Werkst. 57: 75-76.

275(1): 167-178.

41.

Lignocellulosic biomass including wood, logging residue, crops and agricultural wastes) has been widely utilized to produce energy, fuels or chemicals, acting as the potential renewable source for taking place of fossil energies (such as coal, natural gas and petroleum) [1]. Pyrolysis is proved to be, one of the most promising methods to convert biomass into different products (syn-gas, bio-liquid, char and chemicals), which could essentially diversify the energy-supply in many situations [2].

**Figure 1.** The fundamental issues and targets concerning the pyrolysis of cellulose

© 2013 Shen et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2013 Shen et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Cellulose, the most principal chemical component in different lignocellulosic biomass (accounting for more than 50% by weight), has a linear homopolymer of glucopyranose residues linked by β-1, 4- glycosidic bond. The study on pyrolysis of cellulose would be particularly benificial for achieving the better understanding of the pyrolytic mechanism of biomass and facilitating its direct applications in terms of fuels, chemicals and bio-materials. This gives rise to substantial studies on pyrolysis of cellulose in lignocellulosic biomass during the past half-century (Fig. 1), which could be categorized into the three following fundamental issues (Fig. 1):

The Overview of Thermal Decomposition of Cellulose in Lignocellulosic Biomass 195

cellulose), and the configuration of cellulose would be discussed in the following sections, which would help understand the remarkable characteristics of cellulose pyrolysis and its

The model of the cell-wall of woody biomass, firstly proposed by Fengel and Wegener [3], is well-established and further developed by Dumitriu [5], involving cell-wall structure and

The cell wall could be morphologically divided into three distinct zones: middle lamella, primary cell wall and secondary wall [5]. The middle lamella is shared by two contiguous cells and is composed almost entirely of pectic substances. The primary cell walls are composed of cellulose microfibrils and interpenetrating matrix of hemicelluloses, pectins, and proteins. Cellulose forms the framework of the cell walls, hemicelluloses cross-link noncellulosic and cellulosic polymers, and pectins provide the structural support to the cell wall. The secondary cell walls are derived from the primary walls by thickening and inclusion of lignin into the cell wall matrix and occur inside the primary wall. The transition from primary to secondary cell wall synthesis is marked by the cessation of pectin deposition and a noted increase in the synthesis and deposition of cellulose/hemicellulose and lignin. The cellulose and non-cellulosic polysaccharides of the secondary cell wall are

The relevant study [6] evidenced that if cellulose is deposited actively between S1 and S3 developmental stages (especially in the middle part of S2 stage), hemicellulose (xylan) deposition occurs in the S1 to early S2 and again in the S3 developmental layers. Successive deposition of hemicellulose (xylan) onto the cell wall increases the microfibril diameter. The large amounts of hemicellulose (xylan) that accumulated on microfibrils appear globular but are covered with lignin after they are deposited. The information about the distribution of the main components (hemicellulose, cellulose and lignin) in the cell wall layers of

**Figure 2.** The schematic representation of the proposed cell wall along with the location of the main

interactions with the other two main components (hemicellulose and lignin).

the distribution of the chemical components in different cell wall layers.

qualitatively distinct from those found in the primary cell walls.

lignocellulosic biomass is quantitatively reported in the literature [7].

components in biomass

**2.1. The cell-wall structure of biomass**

The studies of pyrolysis of cellulose concerning the above four fundamental issues would be vigorously discussed in this work (especially for the works reported during the past 25 years), where the way-forward of this field would also be specified. This would supply the conceptual guide for the improvement of cellulose utilization and optimization of the thermal-conversion process of biomass.

#### **2. The cell-wall structure of biomass and the configuration of cellulose**

The morphological structure of lignocellulosic biomass has been studied regarding the distribution and inter-linkages of the chemical components, and their configuration [3, 4]. This facilitates not only the better understanding of the physico-chemical properties of biomass, but also the improvement of conversion processes (such as pyrolysis) of biomass to fuels or chemicals.

With the growing interest on lignocellulosic biomass as a potential substituent for fossil fuels, the pyrolysis of biomass should be dramatically examined. Consequently, the cell-wall model of lignocellulosic biomass, the distribution of the chemical components (especially cellulose), and the configuration of cellulose would be discussed in the following sections, which would help understand the remarkable characteristics of cellulose pyrolysis and its interactions with the other two main components (hemicellulose and lignin).

#### **2.1. The cell-wall structure of biomass**

194 Cellulose – Biomass Conversion

fundamental issues (Fig. 1):

for energy and/or chemicals production;

thermal-conversion process of biomass.

fuels or chemicals.

Cellulose, the most principal chemical component in different lignocellulosic biomass (accounting for more than 50% by weight), has a linear homopolymer of glucopyranose residues linked by β-1, 4- glycosidic bond. The study on pyrolysis of cellulose would be particularly benificial for achieving the better understanding of the pyrolytic mechanism of biomass and facilitating its direct applications in terms of fuels, chemicals and bio-materials. This gives rise to substantial studies on pyrolysis of cellulose in lignocellulosic biomass during the past half-century (Fig. 1), which could be categorized into the three following

1. The physico-chemical structure analysis of biomass is concerning the morphological analysis of the biomass cell-wall structure, the distribution and configuration of cellulose, which would facilitate not only the direct utilization of biomass as bio-material, but also the improvement of conversion processes of biomass to fuels or chemicals; 2. The thermal behavior of cellulose involving on-line pyrolysis and off-line pyrolysis study. The on-line pyrolysis is concentrated on the solid mass loss versus temperature or time (along with the evolution of the volatiles) and kinetic models, mostly employing isothermal and dynamic thermo-gravimetry analysis coupled with or without Fourier Transformation Infrared Spectrometry (FTIR) or Mass spectrometry (MS); The off-line pyrolysis study is to examine the yield of the main products (gas, liquid and solid), variation of the compositions in gaseous or liquid product influenced by the intrinsic characteristics and experimental conditions, in order to optimize the pyrolysis process

3. The interactions among the three main components under the pyrolytic condition is to introduce the possible interacting mechanism of the components in biomass, in terms of the mass loss process, the evolution of the volatiles and the yield of the specific products. This would help to improve the understanding of pyrolysis of whole biomass

The studies of pyrolysis of cellulose concerning the above four fundamental issues would be vigorously discussed in this work (especially for the works reported during the past 25 years), where the way-forward of this field would also be specified. This would supply the conceptual guide for the improvement of cellulose utilization and optimization of the

**2. The cell-wall structure of biomass and the configuration of cellulose**

The morphological structure of lignocellulosic biomass has been studied regarding the distribution and inter-linkages of the chemical components, and their configuration [3, 4]. This facilitates not only the better understanding of the physico-chemical properties of biomass, but also the improvement of conversion processes (such as pyrolysis) of biomass to

With the growing interest on lignocellulosic biomass as a potential substituent for fossil fuels, the pyrolysis of biomass should be dramatically examined. Consequently, the cell-wall model of lignocellulosic biomass, the distribution of the chemical components (especially

system from the pyrolytic behavior of the individual components.

The model of the cell-wall of woody biomass, firstly proposed by Fengel and Wegener [3], is well-established and further developed by Dumitriu [5], involving cell-wall structure and the distribution of the chemical components in different cell wall layers.

The cell wall could be morphologically divided into three distinct zones: middle lamella, primary cell wall and secondary wall [5]. The middle lamella is shared by two contiguous cells and is composed almost entirely of pectic substances. The primary cell walls are composed of cellulose microfibrils and interpenetrating matrix of hemicelluloses, pectins, and proteins. Cellulose forms the framework of the cell walls, hemicelluloses cross-link noncellulosic and cellulosic polymers, and pectins provide the structural support to the cell wall. The secondary cell walls are derived from the primary walls by thickening and inclusion of lignin into the cell wall matrix and occur inside the primary wall. The transition from primary to secondary cell wall synthesis is marked by the cessation of pectin deposition and a noted increase in the synthesis and deposition of cellulose/hemicellulose and lignin. The cellulose and non-cellulosic polysaccharides of the secondary cell wall are qualitatively distinct from those found in the primary cell walls.

The relevant study [6] evidenced that if cellulose is deposited actively between S1 and S3 developmental stages (especially in the middle part of S2 stage), hemicellulose (xylan) deposition occurs in the S1 to early S2 and again in the S3 developmental layers. Successive deposition of hemicellulose (xylan) onto the cell wall increases the microfibril diameter. The large amounts of hemicellulose (xylan) that accumulated on microfibrils appear globular but are covered with lignin after they are deposited. The information about the distribution of the main components (hemicellulose, cellulose and lignin) in the cell wall layers of lignocellulosic biomass is quantitatively reported in the literature [7].

**Figure 2.** The schematic representation of the proposed cell wall along with the location of the main components in biomass

According to the above discussion, a simplified schematics for the structure of plant cell wall is presented in Fig. 2, where the morphological relationship among the main components in biomass (cellulose, hemicellulose and lignin) is clearly specified. It still needs to be notified that the details concerning the inter-linking/bond relationship (such as the Hbond among the polysaccharide molecules and lignin-carbohydrate coalescence) between the chemical components in the cell walls of wood are not well examined in the literature.

The Overview of Thermal Decomposition of Cellulose in Lignocellulosic Biomass 197

The stabilization of the long cellulose molecular chains in order systems originates in the presence of functional groups which are able to interact with each other. The functional groups of the cellulose chains are the hydroxyl groups, three of which are linked to each glucopyranose unit. These OH-groups are not only responsible for the supramolecular structure by also for the chemical and physical behavior of the cellulose through the hydrogen bond (H-bond). The OH-groups of cellulose molecules are able to form two types of hydrogen bonds depending on their site at the glucose unit [3]. The hydrogen bonds between OH-groups of adjacent glucose units in the same cellulose chain are called intramolecular linkages, which give certain stiffness to the single chain. The hydrogen bonds between OH-groups of the adjacent cellulose chains are called intermolecular linkages, which are responsible for the formation of supramolecular structures. The primary structures, consisting of a number of cellulose chains through the hydrogen bonds in a superhelicoidal fashion, are the cellulose microfibrils, which build up the framework of the

Two chain ends of the cellulose chain are chemically different (Fig. 3). One end has a Dglucopyranose unit in which anomeric carbon atom is involved in a glycosidic linkage, whereas the other end has a D-glucopyranose unit in which the anomeric carbon atom is free. This cyclic hemiacetal function is in an equilibrium in which a small proportion is an aldehyde, which gives rise to reducing properties at this end of the chain, so that the cellulose chain has a chemical polarity, while the OH-group at the C4 end of the cellulose chain is an alcoholic hydroxyl and therefore non-reducing. The molecular weight of cellulose varies widely depending on the origin of the sample. As cellulose is a linear polymer with uniform units and bonds the size of the chain molecule is usually defined as degree of polymerization (DP). The degrees of polymerization of the plant-cellulose as well as the technical cellulose products are estimated from 15300 for capsules to 305 for rayon