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)
The main structural plant cell wall polymers - cellulose, hemicelluloses, and lignin - rank amongst the most abundant biopolymers in Earth’s carbon cycle. These three polymers form the lignocellulose complex and constitute the bulk of the cell wall with 40-50%, 10-40% and 5-30% of biomass by weight, respectively [1, 2]. Its highly ordered structure of cellulose microfibril aggregates, embedded in a matrix of hemicelluloses and lignin, provides the basis for its mechanical strength  and for the resistance to microbial attack , to which also low molecular mass extractives contribute . Lignified cell walls are therefore a remarkably durable material. In nature, only higher fungi have developed biochemical systems to degrade the lignocellulose complex and perform the conversion and mineralisation of wood to carbon dioxide and water. Extensive reviews on decay pattern, chemistry and biochemistry of microbial wood degradation are available [4, 6, 7]. The natural processes occurring during fungal wood degradation may be utilised for industrial purposes and have a great potential for cellulose-producing and wood-processing industries as well as for high value-added conversion of lignocellulosic waste materials in
Non-food biomass crops such as switchgrass (
Both, breeding of lignocellulosic biomass and the production of transgenic plants, places huge demands on the analyst in terms of methods that can cope with the differences in polymer composition and linkages between them and large sample numbers. Wet-laboratory methods are destructive and time demanding and do not allow handling large sample numbers. Nuclear magnetic resonance (NMR) spectroscopy [46-49], analytical pyrolysis [50, 51], thioacidolysis , and thermogravimetry [53, 54] allow to get information about the composition and linkages between the wood polymers, and ultra-violet (UV) microscopy allows to follow the distribution of e.g. lignin within the cell wall [55, 56]. The requirements on a well suited method are: 1) fast and cheap to allow high-throughput screening 2) non-destructive to probe the native cell wall 3) to be able to analyse the content of each component (cellulose, hemicellulose, lignin) 4) to analyse their distribution within the plant tissues down to the cell wall level and 5) linkages as well as the interdependencies within and between the wood components. Vibrational spectroscopic methods such as infrared  and Raman  spectroscopy have shown potential to fulfil these requirements and can contribute to understand the actual lignocellulosic substrate and what kind of chemical and microstructural alterations take place during breeding, genetic engineering, decay or processing.
Near infrared (NIR) spectroscopy, that enables analyses of high number of samples on a day basis, was used for the prediction of the content of wood components and mechanical properties [59, 60] and the assignment of bands in the near infrared region have been reviewed recently . Moreover it was shown that NIR spectroscopy can be used for the examination of the biodegradation of spruce wood by the white-rot fungi
Mid infrared (MIR) spectroscopy allows similar investigations  as NIR spectroscopy with the advantage of better separated bands in the fingerprint region and the possibility of revealing the orientation of polymers and their interactions, which is of utmost importance in lignocellulose feedstock utilization. Dynamic Fourier transform infrared (FT-IR) spectroscopy has been shown to be appropriate for studying interactions among wood polymers and their ultrastructural organization [69-72]. In spruce wood fibres a close cooperation between cellulose and glucomannan in the fibre wall was suggested, whereas xylan showed no mechanical interaction with cellulose . In primary cell walls investigations indicated a strong interaction among lignin, protein, pectin, xyloglucan, and cellulose . Furthermore the orientation of cell wall polymers can be elucidated by polarised FT-IR measurements. In spruce glucomannan and xylan appear to have a parallel orientation with regard to the orientation of cellulose and, in all probability, an almost parallel orientation with regard to the fibre axis . The first evidence for lignin orientation within native wood cell walls was revealed by Raman microprobe studies  and later confirmed in the secondary wall of tracheids fibres of thermomechanical pulp by FT-IR . Very recently H-2 NMR spectroscopy was used to quantify lignocellulose matrix orientation with the ability to separately investigate oriented and unoriented amorphous domains in intact natural plant tissue .
Mid-infrared spectrometers can like Raman spectrometers be coupled to a microscope to reveal spatial resolution on the micron-level. Polarised FT-IR microscopy confirmed the preferential alignment of lignin in the direction of the fiber axis within the cell wall, but no orientation was found for the lignin in the middle lamella . In combination with a fluid cell FT-IR microscopy was used to monitor
2. Basic principles, instrumentation, techniques and data analysis
2.1. Basic principles and Instrumentation
Raman and infrared spectroscopy monitor molecular vibrations, but are based on different principles. Raman spectroscopy involves inelastic scattering with a photon from a laser light source while IR spectroscopy involves photon absorption, with the molecule excited to a higher vibrational energy level. Thus, unlike infrared absorption, Raman scattering does not require matching of the incident radiation to the energy difference between the ground and excited states. In Raman scattering, the light interacts with the molecule and distorts (polarizes) the cloud of electrons round the nuclei to form a short-lived ‘virtual state’ before re-radiation. If only electron cloud distortion is involved in scattering, the photons will be scattered with very small frequency changes, as the electron mass is comparatively low. This elastic scattering process is the dominant process and called Rayleigh scattering. However, if nuclear motion is involved energy will be transferred either from the incident photon to the molecule (Stokes) or from the molecule to the scattered photon (Anti‐Stokes) . In the Raman scattering process the energy of the scattered photon is different from that of the incident photon (Raman-shift). Raman scattering therefore depends on changes in the polarizability due to molecular vibrations. On contrast IR absorption is based on changes in the dipole moments. Raman and IR spectroscopy thus provide “complementary” information about the molecular vibrations of a given sample. While water gives a strong absorption band in the IR (dipole), only weak Raman scattering is observed making this technique very suitable for
The Raman scattering process is inherently a very weak process and only one of every 106–108 photons is affected. It was experimentally the first time proven in 1928 and the first Raman spectra had to be recorded with very long acquisition times [86, 87]. The development of lasers in the 60’s brought the method a big step forward as the Raman signal is proportional to the excitation power. Today the excitation laser power has to be adjusted well below the point where absorption leads to thermal decomposition of the sample, especially when biological materials are investigated. Furthermore, the Raman scattering intensity is proportional to υ4, where υ is the frequency of the exciting laser radiation . Excitation at 400 nm (=7.5*10-14 Hz) therefore leads to about 16 times higher Raman signal than excitation at 800 nm (=3.75*10-14 Hz). But when measuring biological materials several components absorb the light in the lower wavelength range and therefore sample fluorescence can become problematic and swamp the Raman signal or even thermal sample decomposition may occur. Moving from the visible to the near-infrared (NIR) range, fluorescence virtually disappears as electronic absorption bands are unlikely. The use of Nd:YAG (neodymium-doped yttrium aluminum garnet) laser radiation at 1064 nm coupled with interferometers (involving Fourier transformations) led to so-called near infrared Fourier Transform (NIR-FT) Raman spectrometers . Laser with wavelength in the visible range (e.g. Ar+, He–Ne, Kr+, doubled Nd:YAG lasers) are usually coupled with a dispersive spectrometer and a charge coupled device detector (CCD) for detection (Figure 1). These classical dispersive multichannel Raman spectrometers are nowadays often used in confocal microscope configurations with the advantage of superior rejection of fluorescence and depth resolution due to the pinhole .
For Raman microscopy, and especially for the imaging approach, the throughput of the radiation in the system has to be optimised in every part to acquire spectra fast and of high quality (high signal to noise (S/N) ratio). If a single spectrum is acquired, it is usually not important whether the necessary integration time is 0.1 s or 10 s. However this becomes an issue in scanning (mapping) experiments (imaging), when it becomes 15 min or 25 h. Therefore perfect coupling of the laser radiation into the microscope and out to the spectrometer is important as well as high throughput in the spectrometer and high detection efficiency of the CCD camera . Optical fibres serve as ideal light pipes for connecting the elements and as a pinhole for the outgoing scattered radiation (Figure 1). Furthermore using a spectrometer optimized for the used wavelength range (“blazed” gratings) can increase the throughput as well as CCD cameras most sensitive for the chosen excitation wavelength [90, 91].
2.2. Resonance raman spectroscopy, surface enhanced Raman spectroscopy (SERS) and Coherent Anti-Stokes Scattering (CARS)
When a powerful beam of radiation is used some atoms and molecules of a sample absorb radiation at particular wavelengths and the e.g. coloured molecules become excited. Subsequently radiation of longer wavelength - termed fluorescence - is emitted. This fluorescence can be strong (intensive) and prevent the detection of the (weak) Raman signal . But when the frequency of the laser beam is close to the frequency of an electronic transition, scattering enhancements of up to 106 have been observed. In this resonance condition (Resonance Raman spectroscopy) the method becomes much more sensitive and since only the chromophore gives the more efficient scattering, it will also be selective for the part of the molecule involving the chromophore [85, 92, 93]. Furthermore fluorescence suppression can be achieved by using Kerr gating [93-95].
Another way of enhancing Raman intensity is to disperse the sample on metallic surfaces (either roughened wafers or colloidal solutions). The photon – plasmon interaction results in a huge signal enhancement and the technique, called surface-enhanced Raman spectroscopy (SERS), has progressed from studies of model systems on roughened electrodes to highly sophisticated studies, such as single molecule spectroscopy and molecular imaging [96-98]. The advantage is to enhance the Raman signal and besides the SERS effect leads to fluorescence quenching .
Another way of circumventing fluorescence is coherent anti-Stokes Raman scattering (CARS). This technique allows vibrational imaging with high sensitivity, high spectral resolution and three-dimensional sectioning capabilities. It is a nonlinear diagnostic technique that relies on inducing Raman coherence in the target molecule using two lasers, probed by a third laser which generates a coherent signal in the phase-matching direction at a blue-shifted frequency. Because of this nonlinear intensity dependence the photo-damage of the sample is reduced and the efficient background rejection improves the quality of the spectra . CARS microscopy has already been used for imaging a number of delicate biological samples and processes, e.g. imaging of C–H stretching vibration present in the lipid bilayer of the cell membranes [101-103]. Two other Raman imaging techniques with great potential have evolved recently: Stimulated Raman scattering spectroscopy and Ultrafast Raman loss spectroscopy [104-107].
2.3. Spatial resolution and Tip-Enhanced Raman Spectroscopy (TERS)
The spatial resolution in Confocal Raman microscopy is limited by the diffraction of radiation and defined by the distance between the central maximum and the first minimum of the diffraction pattern, which is given by r = 0.61 λ / NA (λ = wavelength of the radiation, NA = numerical aperture of the objective) . If high spatial resolution is sought-after, a laser in the visible range (e.g. 532 nm versus 1085 nm) and a microscope objective with a high numerical aperture (NA>1) have to be chosen. NA is defined by the refractive index of the medium (n) in which the optics are immersed (e.g. 1.0 for air and up to 1.56 for oils) and the half-angle of the maximum cone of radiation that enters or exits the condenser or objective (θ) (NA = n.sinθ). Two objects are completely resolved if they are separated by 2r and barely if they are separated by r (Rayleigh criterion of resolution) . Therefore, the highest spatial resolution can be achieved with oil immersion objectives with high NA. Also if depth resolution is important, immersion objectives (oil, water) give better results . Generally the axial resolution is around twice the lateral resolution .
Tip-enhanced Raman spectroscopy (TERS), which is based on the surface plasmonic enhancement and confinement of light near a metallic nanostructure, can overcome the so-called diffraction limit and produce optical images far beyond. It has been demonstrated that a spatial resolution as high as 4 nm could be achieved . Consequently, nucleobases, proteins, lipids, and carbohydrates can be identified and localized in a single measurement. This has been shown in the last few years for different biological samples ranging from single DNA strand investigations to cell membrane studies [111-113].
2.4. Raman approaches for imaging
In Point-by-Point scanning the sample is scanned with a laser beam using X, Y, Z scanning stages. At each position of the raster a Raman spectrum is acquired and out of these spectra an image generated. The laser and the scattered light are often focused through so-called pinholes in order to know the exact position of the excitation and the collection volumes from the samples. The limiting factor of the Point-by-Point scanning approach is the fact that quite long measuring times are necessary because the duration is proportional to the number of pixels. Nevertheless the main advantage is that the whole Raman spectrum is acquired at each point and available for detailed analysis .
In the Line scanning approach the laser is elongated (1 dimension) to form a line with the help of a moving mirror or cylindrical optic devices. As a result the sample is illuminated with a laser line, which is parallel oriented to an entrance slit of a dispersive spectrograph. Scanning of the sample is still required, but only in the direction perpendicular to the laser line. This leads to a reduced experiment time . It is the most efficient method if the spectral information from areas with perimeters of typically a few millimetre is required .
In Wide-field Raman imaging the whole sample field is illuminated with laser light. The experimental time depend primarily on the number of spectral channels or wavenumber positions at which an entire image is recorded . There are numerous Wide-field Raman imaging approaches, such as liquid crystal tuneable filters (LCTFS) or the Fibre Array Spectral Translator (FAST). In FAST the received Raman light from a globally illuminated sample field is focused on a 2-dimensional array of optical fibres, which is followed/reduced to a one dimensional array on the distal end. This end is imaged through a dispersive spectrometer with a CCD detector. This method makes it possible to reduce two spatial dimensions data to a single dimension, which is afterwards dispersed fibre by fibre onto the CCD camera . 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 spectral analysis of data obtained in point or line scanning .
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 embedding [118, 119].
2.5. Processing of Raman spectra and image generation
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 methods to generate images.
2.5.1. Spectra pre-processing
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 bands of interest they have to be removed to avoid influences on the final results.
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 , wavelet transformation , maximum entropy filter ) 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 .
2.5.2. Univariate and multivariate 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 . 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 .
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 .
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 . There are various ways of clustering, e.g. distance calculation (Euclidean, Manhattan), hierarchical cluster analysis, K-means Cluster Analysis, Fuzzy Clustering and each has its advantages and disadvantages .
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) . 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].
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 . 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.
|330||δ (CCC) ring||║ s|
|380||δ (CCC) ring||╪ m|
|436||Γ (COC) def||╪ m|
|497||ν (COC) glycosidic||╪ m|
|520||δ (COC) glycosidic||╪ s|
|902||υ (COC) in plane, sym||║m|
|970||ρ (CH2)||╪ m|
|998||ρ (CH2)||║ s|
|1098||υ (COC) glycosidic, asym||║ s|
|1121||υ (COC) glycosidic, sym||╪ m|
|1472||δ (CH2); δ (COH);||╪ m|
|2897||υ (CH)||╪ s|
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 . 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 .
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 . According to Himmelsbach et al.  the weak bands between 870–800 and 515–475 cm-1 offer the potential to distinguish between cellulose and xylan in flax fibres. In
While cellulose and hemicelluloses have β-glycosidic bonds, pectins are composed of α-glycosidic 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) . 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
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
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)  and oxygen flushing technique . 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 one-photon-excited fluorescence and has been used to study lignin modification in alfalfa . 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 . 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 . 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 . For the S units the intense band at 1328 cm-1 is characteristic, while in spruce the band is found at 1334 cm-1  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).
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 . 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
|2936||2944||2941||antisymmetric C-H str. in OCH3 (SW)  and (HW) ; symmetric C-H str. in CH3 in FA |
|1657||1657||ring-conjugated C=C str. of coniferyl alcohol plus C=O str. of coniferaldehyde [178, 179]|
|1632||str. of C=C from propenoic acid side chain of FA |
|1599||1600||1607||symmetric aryl ring str. [144, 166-168]|
|1503||1498||1505||antisymmetric aryl ring str. , in FA|
|1458||1458||C-H3 def. in O-CH3 ; C-H2 scissoring; guaiacyl ring vibration (SW) [178, 179] and to C-H3 def. in O-CH3 (HW) [166, 179]|
|1334||1328||aliphatic O-H bend. (SW) [144, 178], and S-lignin (HW) , possibly contribution from cellulose|
|1271||1274||1271||aryl-O str. of aryl-OH and aryl-O-CH3; guaiacyl ring (with C=O group) mode (SW) [144, 178, 179], HW [177, 179]|
|1176||aryl-H def. |
|1150||eventually O-CH3 def. ; possibly contribution from carbohydrate |
|1139||a mode of coniferaldehyde unit (SW) ; aromatic C-H in plane def. (guaiacyl type) |
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 . 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
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 (
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 . Furthermore changes due to environmental growth conditions or genetic engineering can be evaluated. By comparing lignification in wild-type and lignin-reduced 4CL transgenic
5. Heterogeneity of lignin in the internode of the model grass
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.
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 . It was shown that ferulate esters act as initiation or nucleation sites of lignin deposition in grasses . Ferulate molecules connect lignin to arabinoxylans primarily through ester-ether bonds and form dimeric structures cross-linking arabinoxylan chains to polysaccharides .
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
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 . 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.
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.
The examples of poplar tension wood and
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).
Mc Kendry P. 2002 Energy production from biomass (part 1): overview of biomassBioresour. Technol. 83 1 37 46
Fengel D. Wegener G. 1989 Wood: chemistry, ultrastructure, reactions.Berlin: Walter de Gruyter & Co., Berlin. 613 p.
Salmén L. Burgert I. 2009 Cell wall features with regard to mechanical performance. A review
Daniel G. 2003Microview of Wood under Degradation by Bacteria and Fungi. In: Goodell B, Nicholas DD, Schultz TP, editors. Wood Deterioration and Degradation. Advances in Our Changing World:
Zabel RA, Morrell JJ 1992Wood Microbiology- Decay and its Prevention. Academic Press I, editor. San Diego: Academic Press, Inc. 476 p.
Eriksson-E K. Blanchette L. Ander R. A. P. 1990 Microbial and Enzymatic Degradation of Wood and Wood ComponentsTimell TE, editor. Berlin: Springer. 313 p.
Goodell B. 2003Brown-Rot Fungal Degradation of Wood: Our Evolving View. In: Goodell B, Nicholas DD, Schultz TP, editors. Wood Deterioration and Degradation. Advances in Our Changing World:
Bajpai P. 2012 Biotechnology for Pulp and Paper ProcessingNew York: Springer. 414 p.
Mizrachi E. Mansfield S. D. AA Myburg 2012 Cellulose factories: advancing bioenergy production from forest trees.New Phytol. 194 1 54 62
Sims R. E. H. Hastings A. Schlamadinger B. Taylor G. Smith P. 2006Energy crops: current status and future prospects. Global Change Biol 12 11 2054 2076
Gordon GA 2011 Application of Fourier transform mid-infrared spectroscopy (FTIR) for research into biomass feed-stocksIn: Nikolic GS, editor. Fourier Transforms- New Analytical Approaches and FTIR Strategies. Rijeka, Croatia: lnTech; 71 88
Allison G. G. Robbins M. P. Carli J. Clifton-Brown J. Donnison I. 2010 Designing biomass crops with improved calorific content and attributes for burning: a UK perspectiveIn: P. Mascia, Schefrran J, Widhalm JM, editors. Plant Biotechnology for Sustainable Production of Energy and CoProducts. Heidelberg, Germany: Springer; 25 56
Mc Carthy J. L. Islam A. 2000Lignin chemistry, technology, and utilization: A brief history. In: Glasser WG, Northey RA, Schultz TP, editors. Lignin : Historical, Biological, and Materials Perspectives: American Chemical Society; 2 99
Ko JH, Kim HT, Han KH 2011 Biotechnological improvement of lignocellulosic feedstock for enhanced biofuel productivity and processingPlant Biotechnol. Rep. 5 1 1 7
Kishimoto T. 2009 Synthesis of lignin model compounds and their application to wood researchMokuzai Gakkaishi 55 4 187 197
Holmgren A. Norgren M. Zhang L. Henriksson G. 2009 On the role of the monolignol gamma-carbon functionality in lignin biopolymerization.
Hafren J. Westermark U. Lennholm H. Terashima N. 2002Formation of C-13-enriched cell-wall DHP using isolated soft xylem from Picea abies. Holzforschung 56 6 585 591
Monties B. 2005Biological variability of lignin. Cell. Chem. Technol. 39(5-6): 341-367.
Zhong RQ, Morrison WH, Himmelsbach DS, Poole FL, Ye Z-H 2000 Essential role of caffeoyl coenzyme A O-methyltransferase in lignin biosynthesis in woody poplar plantsPlant Physiol. 124 2 563 577
Liu CJ 2012 Deciphering the enigma of lignification: Precursor transport, oxidation, and the topochemistry of lignin assemblyMol Plant 5 2 304 317
Marjamaa K. Kukkola E. M. Fagerstedt K. V. 2009 The role of xylem class III peroxidases in lignificationJ. Exp. Bot. 60 2 367 376
Chen Y. R. Sarkanen S. 2010 Macromolecular replication during lignin biosynthesis
Baucher M. Monties B. Van Montagu M. Boerjan W. 1998 Biosynthesis and genetic engineering of lignin.Crit. Rev. Plant Sci. 17 2 125 197
Whetten R. Sederoff R. 1995 Lignin biosynthesis.Plant Cell 7 1001 1013
Umezawa T. 2010 The cinnamate/monolignol pathwayPhytochem. Rev. 9 1 1 17
Vanholme R. Demedts B. Morreel K. Ralph J. Boerjan W. 2010Lignin biosynthesis and structure. Plant Physiol. 153 3 895 905
Wang C. Wang Y. C. Diao G. P. Jiang J. CP Yang 2010 Isolation and characterization of expressed sequence tags (ESTs) from cambium tissue of birch (Betula platyphylla Suk)Plant Mol. Biol. Rep. 28 3 438 449
Samuels AL, Rensing KH, Douglas CJ, Mansfield SD, Dharmawardhana DP, Ellis BE 2002 Cellular machinery of wood production: differentiation of secondary xylem in Pinus contorta var. latifolia.
Studer M. H. JD De Martini Davis. M. F. Sykes R. W. Davison B. Keller M. Tuskan G. A. CE Wyman 2011 Lignin content in natural Populus variants affects sugar releaseP. Natl. Acad. Sci. USA 108 15 6300 6305
Grabber JH 2005 How do lignin composition, structure, and cross-linking affect degradability? A review of cell wall model studiesCrop Sci. 45 3 820 831
Carroll A. Somerville C. 2009Cellulosic biofuels. Annu. Rev. Plant Biol. 60 165 182
Chang MCY 2007Harnessing energy from plant biomass. Curr. Opin. Chem. Biol. 11 6 677 684
Clifton-Brown J. Robson P. Allison G. Lister S. Sanderson R. Hodgson E. Farrar K. Hawkins S. Jensen E. Jones S. Huang L. Roberts P. Youell S. Jones B. Wright A. Valantine J. Donnison I. 2008 Miscanthus: breeding our way to a better futureIn: E. Booth, M. Green, A. Karp, I. Shield, D. Stock, Turley D, editors. Biomass and Energy Crops III. Warwick, UK: Association of Applied Biologists; 199 206
Hodgson E. M. Fahmi R. Yates N. Barraclough T. Shield I. Allison G. Bridgwater A. V. Donnison I. S. 2010Miscanthus as a feedstock for fast-pyrolysis: Does agronomic treatment affect quality? Bioresour. Technol. 101 15 6185 6191
Hodgson E. M. Lister S. J. Bridgwater A. V. Clifton-Brown J. Donnison I. S. 2010 Genotypic and environmentally derived variation in the cell wall composition of Miscanthus in relation to its use as a biomass feedstockBiomass. Bioenerg. 34 5 652 660
Boudet A. M. Grima-Pettenati J. 1996 Lignin genetic engineeringMol. Breeding 2 1 25 39
Boudet A. M. Lapierre C. Grima-Pettenati J. 1995Tansley review No-80- Biochemistry and molecular-biology of lignification. New Phytol. 129 2 203 236
Chiang VL 2006 Monolignol biosynthesis and genetic engineering of lignin in trees, a reviewEnviron. Chem. Lett. 4 3 143 146
Harris D. De Bolt S. 2010Synthesis, regulation and utilization of lignocellulosic biomass. Plant Biotechnol. J. 8 3 244 262
Higuchi T. 2000 The present state and problems in lignin biosynthesisCell. Chem. Technol. 34(1-2): 79 EOF 100 EOF
Horvath B. Peszlen I. Peralta P. Kasal B. Li L. G. 2010 Effect of lignin genetic modification on wood anatomy of Aspen treesIawa J. 31 1 29 38
Koehler L. Telewski F. W. 2006 Biomechanics and transgenic wood.Am. J. Bot. 93 10 1433 1438
Leple J. C. Dauwe R. Morreel K. Storme V. Lapierre C. Pollet B. Naumann A. Kang K. Y. Kim H. Ruel K. Lefebvre A. Joseleau J. P. Grima-Pettenati J. De Rycke R. Andersson-Gunneras S. Erban A. Fehrle I. Petit-Conil M. Kopka J. Polle A. Messens E. Sundberg B. Mansfield S. D. Ralph J. Pilate G. Boerjan W. 2007Downregulation of cinnamoyl-coenzyme a reductase in poplar: Multiple-level phenotyping reveals effects on cell wall polymer metabolism and structure. Plant Cell 19 11 3669 3691
Pu Y. Q. Chen F. Ziebell A. Davison B. H. Ragauskas A. J. 2009 NMR characterization of C3H and HCT down-regulated alfalfa ligninBioenerg. Res. 2 4 198 208
Chen F. Dixon R. A. 2007 Lignin modification improves fermentable sugar yields for biofuel production.Nat Biotechnol. 25 7 759 761
Maunu SL 2002 NMR studies of wood and wood productsProg. Nucl. Magn. Reson. Spectrosc. 40 151 174
Lu F. Ralph J. 2003 Non-degradative dissolution and acetylation of ball-milled plant cell walls: high-resolution solution-state NMRPlant J. 35 535 544
Yelle D. J. Ralph J. Li F. Hammel K. E. 2008 Evidence for cleavage of lignin by a brown rot basidiomyceteEnviron. Microbiol. 10 7 1844 1849
Yelle D. J. Wei D. Ralph J. Hammel K. E. 2011 Multidimensional NMR analysis reveals truncated lignin structures in wood decayed by the brown rot basidiomycete Postia placenta.Environ. Microbiol. 13 4 1091 1100
Alves A. Schwanninger M. Pereira H. Rodrigues J. 2006Analytical pyrolysis as a direct method to determine the lignin content in wood- Part 1: Comparison of pyrolysis lignin with Klason lignin. J. Anal. Appl. Pyrol. 76(1-2): 209-213.
Alves A. Gierlinger N. Schwanninger M. Rodrigues J. 2009 Analytical pyrolysis as a direct method to determine the lignin content in wood Part 3. Evaluation of species-specific and tissue-specific differences in softwood lignin composition using principal component analysis.J. Anal. Appl. Pyrol. 85(1-2): 30 EOF 37 EOF
Anterola AM, Lewis NG 2002Review: Trends in lignin modification: a comprehensive analysis of the effects of genetic manipulations/mutations on lignification and vascular integrity. Phytochemistry 61 221 294
Korosec R. C. Lavric B. Rep G. Pohleven F. Bukovec P. 2009 Thermogravimetry as a possible tool for determining modification degree of thermally treated Norway spruce woodJ. Therm. Anal. Calorim. 98 1 189 195
Taneda K. Nishiyama Y. Uparivong S. 1995An evaluation of kinetic-parameters by derivative thermogravimetry and Its application to oood and other bioresources. Mokuzai Gakkaishi 41 4 414 424
Gindl W. Grabner M. 2000 Characteristics of spruce [Picea abies (L.) Karst] latewood formed under abnormally low temperatures
Gindl W. Grabner M. Wimmer R. 2000 The influence of temperature on latewood lignin content in treeline Norway spruce compared with maximum density and ring widthTrees-Struct. Funct. 14 7 409 414
Griffiths PR, Haseth JAD 2007 Fourier Transform Infrared Spectrometrynd ed. New York: Wiley. 529 p.
Schrader B. 1995 Infrared and Raman Spectroscopy: Methods and Applications.Weinheim: Wiley-VCH Verlag GmbH 788 p.
Tsuchikawa S. 2007 A review of recent near infrared research for wood and paperAppl. Spectrosc. Rev. 42 43 71
Tsuchikawa S. Schwanninger M. 2011A review of recent near infrared research for wood and paper (Part 2). Appl. Spectrosc. Rev. (in print): DOI:
Schwanninger M. Rodrigues J. Fackler K. 2011 A review of band assignments in near infrared spectra of wood and wood componentsJ. Near Infrared Spectrosc. 19( 287 EOF 308 EOF
Schwanninger M. Hinterstoisser B. Gradinger C. Messner K. Fackler K. 2004 Examination of spruce wood biodegraded by Ceriporiopsis subvermispora using near and mid infrared spectroscopyJ. Near Infrared Spectrosc. 12 6 397 409
Fackler K. Schmutzer M. Manoch L. Schwanninger M. Hinterstoisser B. Ters T. Messner K. Gradinger C. 2007 Evaluation of the selectivity of white rot isolates using near infrared spectroscopic techniquesEnzyme Microb. Technol. 41 881 887
Fackler K. Schwanninger M. Gradinger C. Srebotnik E. Hinterstoisser B. Messner K. 2007 Fungal decay of spruce and beech wood assessed by near infrared spectroscopy in combination with uni- and multivariate data analysis
Fackler K. Gradinger C. Hinterstoisser B. Messner K. Schwanninger M. 2006 Lignin degradation by white rot fungi on spruce wood shavings during short-time solid-state fermentations monitored by near infrared spectroscopyEnzyme Microb. Technol. 39 7 1476 1483
Fackler K. Schwanninger M. 2010Polysaccharide degradation and lignin modification during brown-rot of spruce wood: a polarised Fourier transfrom near infrared study. J. Near Infrared Spectrosc. 18 403 416
Fackler K. Schwanninger M. 2011 Accessibility of hydroxyl groups of brown-rot degraded spruce wood to heavy waterJ. Near Infrared Spectrosc. 19 359 368
Fackler K. Schwanninger M. Gradinger C. Hinterstoisser B. Messner K. 2007 Qualitative and quantitative changes of beech wood degraded by wood rotting basidiomycetes monitored by Fourier transform infrared spectroscopic methods and multivariate data analysisFEMS Microbiol. Lett. 271 162 169
Åkerholm M. Salmén L. 2001 Interactions between wood polymers studied by dynamic FT-IR spectroscopy
Åkerholm M. Salmén L. 2003 The oriented structure of lignin and its viscoelastic properties studied by static and dynamic FT-IR spectroscopy
Hinterstoisser B. Åkerholm M. Salmén L. 2001 Effect of fiber orientation in dynamic FTIR study on native cellulose.Carbohydr. Res. 334 27 37
Hinterstoisser B. Salmén L. 2000 Application of dynamic 2D FTIR to celluloseVib. Spectrosc. 22(1-2): 111 EOF 118 EOF
Stevanic J. S. Salmén L. 2008 Characterizing wood polymers in the primary cell wall of Norway spruce (Picea abies (L.) Karst.) using dynamic FT-IR spectroscopy
Stevanic J. S. Salmén L. 2009 Orientation of the wood polymers in the cell wall of spruce wood fibres
Atalla RH, Agarwal UP 1985 Raman microprobe evidence for lignin orientation in the cell walls of native woody tissue.Science 227 636 638
Chowdhury S. Madsen L. A. CE Frazier 2012Probing alignment and phase behavior in intact wood cell walls using H-2 NMR spectroscopy. Biomacromolecules 13 4 1043 1050
Salmén L. Olsson-M A. Stevanic J. S. Simonovic J. Radotic K. 2012Structural organization of the wood polymers in the wood fibre structure Bioresources 7 1 521 532
Gierlinger N. Goswami L. Schmidt M. Burgert I. Coutand C. Rogge T. Schwanninger M. 2008 In situ FT-IR microscopic study on enzymatic treatment of poplar wood cross-sections
Chen L. M. Carpita N. C. Reiter W. D. Wilson R. H. Jeffries C. Mc Cann M. C. 1998 A rapid method to screen for cell-wall mutants using discriminant analysis of Fourier transform infrared spectra.Plant J. 16 3 385 392
Mouille G. Robin S. Lecomte M. Pagant S. Hofte H. 2003 Classification and identification of Arabidopsis cell wall mutants using Fourier-Transform InfraRed (FT-IR) microspectroscopyPlant J. 35 3 393 404
Robin S. Lecomte M. Hofte H. Mouille G. 2003 A procedure for the clustering of cell wall mutants in the model plant Arabidopsis based on Fourier-transform infrared (FT-IR) spectroscopyJ. Appl. Stat. 30 6 669 681
Mc Cann M. C. Defernez M. Urbanowicz B. R. Tewari J. C. Langewisch T. Olek A. Wells B. Wilson R. H. Carpita N. C. 2007Neural network analyses of infrared spectra for classifying cell wall architectures. Plant Physiol. 143 3 1314 1326
Fackler K. Stevanic J. S. Ters T. Hinterstoisser B. Schwanninger M. Salmén L. 2010 Localisation and characterisation of incipient brown-rot decay within spruce wood cell walls using FT-IR imaging microscopyEnzyme Microb. Technol. 47 257 267
Fackler K. Stevanic J. S. Ters T. Hinterstoisser B. Schwanninger M. Salmén L. 2011 FT-IR imaging microscopy to localise and characterise simultaneous and selective white-rot decay within spruce wood cells
Smith E. Dent G. 2005Modern Raman Spectroscopy- A practical approach. Manchester: John Wiley & Sons Ltd. 210 p.
Landsberg G. Mandelstam L. 1928Light scattering in crystals. Zeitschrift Für Physik 50(11-12): 769-780.
Raman CV, Krishnan KS 1928 A new type of secondary radiation
Hirschfeld T. Chase B. 1986FT-Raman spectroscopy- development and justification. Appl. Spectrosc. 40 2 133 137
Das RS, Agrawal YK 2011 Raman spectroscopy: Recent advancements, techniques and applicationsVib. Spectrosc. 57 2 163 176
Hollricher O. 2010 Raman Instrumentation for Confocal Raman MicroscopyIn: Diening T, Hollricher O, Toporski J, editors. Confocal Raman microscopy. Berlin Heidelberg: Springer-Verlag; 43 60
Dieing T. Hollricher O. 2008 High-resolution, high-speed confocal Raman imagingVib. Spectrosc. 48 1 22 27
Withnall R. Chowdhry B. Z. Silver J. Edwards H. G. M. de Oliveira L. F. C. 2003 Raman spectra of carotenoids in natural products.Spectroc. Acta Pt. A-Molec. Biomolec. Spectr. 59 2207 2212
Saariaho A. M. AS Jääskeläinen Matousek. P. Towrie M. Parker A. W. Vuorinen T. 2004 Resonance Raman spectroscopy of highly fluorescing lignin containing chemical pulps: Suppression of fluorescence with an optical Kerr gateHolzforschung 58 1 82 90
Matousek P. Towrie M. Stanley A. Parker A. W. 1999 Efficient rejection of fluorescence from Raman spectra using picosecond Kerr gatingAppl. Spectrosc. 53 12 1485 1489
Matousek P. Towrie M. Parker A. W. 2002 Fluorescence background suppression in Raman spectroscopy using combined Kerr gated and shifted excitation Raman difference techniquesJ. Raman Spectrosc. 33 238 242
Sharma B. Frontiera R. R. Henry A. I. Ringe E. Van Duyne R. P. 2012SERS: Materials, applications, and the future. Mater. Today 15(1-2): 16-25.
Zhang Y. Hong H. Myklejord D. V. Cai W. B. 2011 Molecular imaging with SERS-active nanoparticles.Small 7 23 3261 3269
Mc Nay G. Eustace D. Smith W. E. Faulds K. Graham D. 2011 Surface-Enhanced Raman Scattering (SERS) and Surface-Enhanced Resonance Raman Scattering (SERRS): A review of applicationsAppl. Spectrosc. 65 8 825 837
Rösch P. Popp J. Kiefer W. 1999 Raman and surface enhanced Raman spectroscopic investigation on Lamiaceae plantsJ. Mol. Struct. 481 121 124
Cheng JX, Xie XS 2004 Coherent anti-Stokes Raman scattering microscopy: Instrumentation, theory, and applicationsJ. Phys. Chem. B 108 3 827 840
Chen J. X. Volkmer A. Book L. D. Xie X. S. 2002Multiplex coherent anti-stokes Raman scattering microspectroscopy and study of lipid vesicles. J. Phys. Chem. B 106 34 8493 8498
Le TT, Yue SH, Cheng JX 2010 Shedding new light on lipid biology with coherent anti-Stokes Raman scattering microscopyJ. Lipid Res. 51 11 3091 3102
Pezacki J. P. Blake J. A. Danielson D. C. Kennedy D. C. Lyn R. K. Singaravelu R. 2011 Chemical contrast for imaging living systems: molecular vibrations drive CARS microscopyNat. Chem. Biol. 7 3 137 145
Fu D. Lu F. K. Zhang X. Freudiger C. Pernik D. R. Holtom G. Xie X. S. 2012 Quantitative chemical imaging with multiplex stimulated Raman scattering microscopyJ. Am. Chem. Soc. 134 8 3623 3626
Mallick B. Lakhsmanna A. Umapathy S. 2011 Ultrafast Raman loss spectroscopy (URLS): instrumentation and principleJ. Raman Spectrosc. 42 10 1883 1890
Freudiger C. W. Min W. Holtom G. R. Xu B. W. Dantus M. Xie X. S. 2011 Highly specific label-free molecular imaging with spectrally tailored excitation-stimulated Raman scattering (STE-SRS) microscopyNat. Photonics 5 2 103 109
Saar BG, Zeng YN, Freudiger CW, Liu YS, Himmel ME, Xie XS, Ding SY 2010Label-free, real-time monitoring of biomass processing with stimulated Raman scattering microscopy. Angew. Chem.-Int. Edit. 49 32 5476 5479
Griffith PR 2009Infrared and Raman Instrumentation for Mapping and Imaging. In: Salzer R, Siesler HW, editors. Infrared and Raman Spectroscopic Imaging. Weinheim: Wiley-VCH Verlag GmbH & Co. KGaA; 3 64
Everall N. Lapham J. Adar F. Whitley A. Lee E. Mamedov S. 2007 Optimizing depth resolution in Confocal Raman microscopy: A comparison of metallurgical, dry corrected, and oil immersion objectives.Appl. Spectrosc. 61 3 251 259
Bruneel J. L. Lassegues J. C. Sourisseau C. 2002 In-depth analyses by confocal Raman microspectrometry: experimental features and modeling of the refraction effectsJ. Raman Spectrosc. 33 10 815 828
Verma P. Ichimura T. Yano T. Saito Y. Kawata S. 2010 Nano-imaging through tip-enhanced Raman spectroscopy: Stepping beyond the classical limitsLaser Photon. Rev. 4 4 548 561
Deckert-Gaudig T. Deckert V. 2011 Nanoscale structural analysis using tip-enhanced Raman spectroscopyCurr. Opin. Chem. Biol. 15 5 719 724
Elfick A. P. D. Downes A. R. Mouras R. 2010 Development of tip-enhanced optical spectroscopy for biological applications: a reviewAnal. Bioanal. Chem. 396 1 45 52
Nelson MP, Treado PJ 2010Raman imaging instrumentation. In: Sasic S, Ozaki Y, editors. Raman, Infrared, and Near-Infrared Chemical ImagingHoboken, New Jersey: John Wiley & Sons, Inc.; 23 55
Toytman I. Simanovskii D. Palanker D. 2009 On illumination schemes for wide-field CARS microscopy.Opt. Express 17 9 7339 7347
Schlucker S. MD Schaeberle Huffman. S. W. Levin I. W. 2003 Raman microspectroscopy: A comparison of point, line, and wide-field imaging methodologies.Anal. Chem. 75 16 4312 4318
McCreery RL 2000 Raman microscopy and imaging. In: Winefordner JD, editor. Raman Spectroscopy for Chemical Analysis. New York: John Wiley & Sons, Inc.; 293 332
Gamsjaeger S. Kazanci M. Paschalis E. P. Fratzl P. 2009Raman application in bone imaging. In: Amer MS, editor. Raman Spectroscopy for soft matter applications. New Jersey: Wiley VCH; 227 267
Gierlinger N. Keplinger T. Harrington M. 2012 Imaging of plant cell walls by Confocal Raman microscopy.Nat. Protoc.: in review.
Zhang D. M. Jallad K. N. Ben-Amotz D. 2001 Stripping of cosmic spike spectral artifacts using a new upper-bound spectrum algorithmAppl. Spectrosc. 55 11 1523 1531
Katsumoto Y. Ozaki Y. 2003 Practical algorithm for reducing convex spike noises on a spectrumAppl. Spectrosc. 57 3 317 322
Diening T. Ibach W. 2010Software Requirements and Data Analysis in Confocal Raman Microscopy. In: Diening T, Hollricher O, Toporski J, editors. Confocal Raman microscopy. Berlin Heidelberg: Springer-Verlag; 61 89
Savitzky A. Golay M. J. E. 1964 Smoothing and differentiation of data by simplified least squares procedures.Anal. Chem. 36 1627 1639
Ramos P. M. Ruisanchez I. 2005 Noise and background removal in Raman spectra of ancient pigments using wavelet transformJ. Raman Spectrosc. 36 9 848 856
Liland K. H. Rukke E. O. Olsen E. F. Isaksson T. 2011 Customized baseline correctionChemometrics Intell. Lab. Syst. 109 1 51 56
de Juan A. Maeder M. Hancewicz T. Duponchel L. Tauler R. 2009Chemometric Tools for Image Analysis. In: Salzer R, W. SH, editors. Infrared and Raman Spectroscopic ImagingWeinheim: WILEY-VCH Verlag GmbH & Co. KGaA; 65 108
Schulze G. Jirasek A. Yu M. M. L. Lim A. Turner R. F. B. Blades M. W. 2005 Investigation of selected baseline removal techniques as candidates for automated implementationAppl. Spectrosc. 59 5 545 574
Prakash BD, Wei YC 2011 A fully automated iterative moving averaging (AIMA) technique for baseline correctionAnalyst 136 15 3130 3135
Schulze H. G. Foist R. B. Okuda K. Ivanov A. Turner R. F. B. 2011A model-free, fully automated baseline-removal method for Raman spectra. Appl. Spectrosc. 65 1 75 84
Zhang Z. M. Chen S. Liang Y. Z. 2010 Baseline correction using adaptive iteratively reweighted penalized least squares
Schmidt U. Ibach W. Muller J. Weishaupt K. Hollricher O. 2006Raman spectral imaging- A nondestructive, high resolution analysis technique for local stress measurements in silicon. Vib. Spectrosc. 42 1 93 97
Geladi P. Grahn H. Manley M. 2010Data analysis and chemometrics for hyperspectral Imaging. In: Sasic S, Ozaki Y, editors. Raman, Infrared, and Near-Infrared Chemical ImagingHoboken, New Jersey: John Wiley & Sons, Inc.; 93 109
Shinzawa H. Awa K. Kanematsu W. Ozaki Y. 2009 Multivariate data analysis for Raman spectroscopic imagingJ. Raman Spectrosc. 40 12 1720 1725
Næs T. Isaksson T. Fearn T. Davies T. 2002A User-Friendly Guide to Multivariate Calibration and Classification. first ed. Chichester: NIR Publications. 344 p.
Geladi P. 2003Chemometrics in spectroscopy. Part 1. Classical chemometrics. Spectroc. Acta Pt. B-Atom. Spectr. 58 767 782
( Hastie T. Tibshirani R. Friedman J. 2009) The Elements of Statistical Learning. New York: Springer. 739 p.
Cosgrove DJ 2005Growth of the plant cell wall. Nat. Rev. Mol. Cell Biol. 6 11 850 861
Wiley JH, Atalla RH 1987 Band assignments in the Raman spectra of cellulosesCarbohydr. Res. 160 113 129
Agarwal UP, Atalla RH 1986In-situ Raman microprobe studies of plant cell walls- Macromolecular organization and compositional variability in the secondary wall of Picea mariana (Mill) Bsp. Planta 169 3 325 332
Atalla RH, Agarwal UP 1986 Recording Raman-spectra from plant cell wallsJ. Raman Spectrosc. 17 2 229 231
Schenzel K. Fischer S. 2001NIR FT Raman spectroscopy- a rapid analytical tool for detecting the transformation of cellulose polymorphs. Cellulose 8 1 49 57
Gierlinger N. Luss S. Konig C. Konnerth J. Eder M. Fratzl P. 2010 Cellulose microfibril orientation of Picea abies and its variability at the micron-level determined by Raman imagingJ. Exp. Bot. 61 2 587 595
Agarwal UP, Reiner RS, Ralph SA 2010 Cellulose I crystallinity determination using FT-Raman spectroscopy: univariate and multivariate methods
Agarwal UP, Ralph SA 1997 FT-Raman spectroscopy of wood: Identifying contributions of lignin and carbohydrate polymers in the spectrum of black spruce (Picea mariana)Appl. Spectrosc. 51 11 1648 1655
DS Himmelsbach Khahili. S. Akin D. E. 1999 Near-infrared-Fourier-transform-Raman microspectroscopic imaging of flax stemsVib. Spectrosc. 19 361 367
Chu L. Q. Masyuko R. Sweedler J. V. Bohn P. W. 2009 Base-induced delignification of Miscanthus x giganteus studied by three-dimensional confocal Raman imagingBioresour. Technol. 101 13 4919 4925
Mathlouthi M. Koenig J. L. 1986 Vibrational Spectra of Carbohydrates.Adv. Carbohydr. Chem. Biochem. 44 7 89
Richter S. Mussig J. Gierlinger N. 2011 Functional plant cell wall design revealed by the Raman imaging approach
Gierlinger N. Sapei L. Paris O. 2008 Insights into the chemical composition of Equisetum hyemale by high resolution Raman imagingPlanta 227 5 969 980
Synytsya A. Copikova J. Matejka P. Machovic V. 2003 Fourier transform Raman and infrared spectroscopy of pectinsCarbohydr. Polym. 54 1 97 106
Arjyal B. P. Katerelos D. G. Filiou C. Galiotis C. 2000 Measurement and Modeling of Stress Concentration around a Circular NotchExp. Mech. 40 3 248 255
Lewis N. G. Yamamoto E. 1990Lignin: occurrence, biogenesis and biodegradation. Annu. Rev. Plant Physiol. Plant Mol. Biol. 41 455 496
Barsberg S. Matousek P. Towrie M. 2005 Structural analysis of lignin by resonance Raman spectroscopy.Macromol. Biosci. 5 8 743 752
Perera P. N. Schmidt M. Chiang V. L. Schuck P. J. Adams P. D. 2012 Raman-spectroscopy-based noninvasive microanalysis of native lignin structureAnal. Bioanal. Chem. 402 2 983 987
Agarwal UP, Atalla RH 1994 Raman spectral features associated with chromophores in high-yield pulpsJ. Wood Chem. Technol. 14 2 227 241
Agarwal UP 1999An Overview of Raman Spectroscopy as Applied to Lignocellulosic Materials. In: Argyropoulos DS, editor. Advances in Lignocellulosics Characterization. Atlanta, GA: TAPPI Press; 209 225
Agarwal UP, Landucci LL 2004FT-Raman investigation of bleaching of spruce thermornechanical pulp. J. Pulp Pap. Sci. 30 10 269 274
Stewart D. Yahiaoui N. Mc Dougall G. J. Myton K. Marque C. Boudet A. M. Haigh J. 1997 Fourier-transform infrared and Raman spectroscopic evidence for the incorporation of cinnamaldehydes into the lignin of transgenic tobacco (Nicotiana tabacum L) plants with reduced expression of cinnamyl alcohol dehydrogenase.
Himmelsbach DS, Akin DE 1998 Near-infrared Fourier-transform Raman spectroscopy of flax (Linum usitatissimum L.) stemsJ. Agric. Food Chem. 46 3 991 998
Ona T. Sonoda T. Ito K. Shibata M. Katayama T. Kato T. Ootake Y. 1998 Non-destructive determination of lignin syringyl/guaiacyl monomeric composition in native wood by Fourier transform Raman spectroscopyJ. Wood Chem. Technol. 18 1 43 51
Sun L. Varanasi P. Yang F. Loque D. BA Simmons Singh. S. 2012 Rapid determination of syringyl:guaiacyl ratios using FT-Raman spectroscopy.Biotechnol. Bioeng. 109 3 647 656
Barsberg S. Matousek P. Towrie M. Jorgensen H. Felby C. 2006 Lignin radicals in the plant cell wall probed by Kerr-gated resonance Raman spectroscopy.Biophys. J. 90 8 2978 2986
Saariaho A. M. AS Jääskeläinen Nuopponen. M. Vuorinen T. 2003Ultra violet resonance Raman spectroscopy in lignin analysis: determination of characteristic vibrations of p-hydroxyphenyl, guaiacyl, and syringyl lignin structures. Appl. Spectrosc. 57 1 58 66
AS Jääskeläinen Saariaho. A. M. Vyorykka J. Vuorinen T. Matousek P. Parker A. W. 2006 Application of UV-Vis and resonance Raman spectroscopy to study bleaching and photoyellowing of thermomechanical pulps
Zeng Y. Saar B. G. Friedrich M. G. Chen F. Liu-S Y. Dixon R. A. ME Himmel Xie. X. S. Ding-Y S. 2010Imaging Lignin-Downregulated Alfalfa Using Coherent Anti-Stokes Raman Scattering Microscopy. Bioenerg. Res. 3 3 272 277
Larsen K. L. Barsberg S. 2010 Theoretical and Raman spectroscopic studies of phenolic lignin model monomersJ. Phys. Chem. B 114 23 8009 8021
Agarwal UP 2006 Raman imaging to investigate ultrastructure and composition of plant cell walls: distribution of lignin and cellulose in black spruce wood (Picea mariana)
Gierlinger N. Schwanninger M. 2007 The potential of Raman microscopy and Raman imaging in plant researchreview. Spectroscopy 21 69 89
Agarwal UP, Ralph SA 2008 Determination of ethylenic residues in wood and TMP of spruce by FT-Raman spectroscopy
Hanninen T. Kontturi E. Vuorinen T. 2011 Distribution of lignin and its coniferyl alcohol and coniferyl aldehyde groups in Picea abies and Pinus sylvestris as observed by Raman imaging
Carpita NC, Gibeaut DM 1993 Structural models of primary cell walls in flowering plants: consistency of molecular structure with the physical properties of the walls during growth.Plant J. 3 1 1 30
Carpita NC, McCann MC 2000The cell wall. In: Buchanan BB GW, Jones RL, editor. American Society of Plant BiologistsRockville, MD: Biochemistry and Molecular Biology of Plants.
Carpita NC 1996 Structure and biogenesis of the cell walls of grassesAnnu. Rev. Plant Physiol. Plant Mol. Biol. 47 445 476
Scalbert A. Monties B. Lallemand-Y J. Guittet E. Rolando C. 1985 Ether linkage between phenolic acids and lignin fractions from wheat straw
Piot O. Autran-C J. Manfait M. 2001 Investigation by confocal Raman microspectroscopy of the molecular factors responsible for grain cohesion in the Triticum aestivum bread wheat. Role of the cell walls in the starchy endospermJ. Cereal Sci. 34 2 191 205
Ram MS, Dowell FE, Seitz LM 2003 FT-Raman spectra of unsoaked and NaOH-soaked wheat kernels, bran, and ferulic acidCereal Chem. 80 2 188 192
Sun L. BA Simmons Singh. S. 2011 Understanding tissue specific compositions of bioenergy feedstocks through hyperspectral Raman imagingBiotechnol. Bioeng. 108 2 286 295
Agarwal UP 1999Chapter 9: An Overview of Raman Spectroscopy as Applied to Lignocellulosic Materials. In: Argyropoulos DS, editor. Advances in Lignocellulosics Characterization. Atlanta: TAPPI PRESS; 201 225
Agarwal UP, McSweeny JD, Ralph SA 2011 FT-Raman investigation of milled-wood lignins: Softwood, hardwood, and chemically modified black spruce ligninsJ. Wood Chem. Technol. 31 4 324 344
Sebastian S. Sundaraganesan N. Manoharan S. 2009Molecular structure, spectroscopic studies and first-order molecular hyperpolarizabilities of ferulic acid by density functional study. Spectroc. Acta Pt. A-Molec. Biomolec. Spectr. 74 2 312 323
Atalla RH, Agarwal UP, Bond JS 1992Raman Spectroscopy. In: Lin SY, Dence CW, editors. Methods in Lignin Chemistry. Heidelberg, Germany: Springer-Verlag; 162 176
Pu Y. Q. Kosa M. Kalluri U. C. Tuskan G. A. Ragauskas A. J. 2011Challenges of the utilization of wood polymers: how can they be overcome? Appl. Microbiol. Biotechnol. 91 6 1525 1536
Gierlinger N. Schwanninger M. 2006 Chemical imaging of poplar wood cell walls by confocal Raman microscopyPlant Physiol. 140 4 1246 1254
Takayama M. Johjima T. Yamanaka T. Wariishi H. Tanaka H. 1997 Fourier transform Raman assignment of guaiacyl and syringyl marker bands for lignin determinationSpectroc. Acta Pt. A-Molec. Biomolec. Spectr. 53 1621 1628
Larsen K. L. Barsberg S. 2011 Environmental Effects on the Lignin Model Monomer, Vanillyl Alcohol, Studied by Raman SpectroscopyJ. Phys. Chem. B 115 39 11470 11480
Schmidt M. Schwartzberg A. M. Perera P. N. Weber-Bargioni A. Carroll A. Sarkar P. Bosneaga E. Urban J. J. Song J. Balakshin M. Y. Capanema E. A. Auer M. Adams P. D. Chiang V. L. Schuck P. J. 2009Label-free in situ imaging of lignification in the cell wall of low lignin transgenic Populus trichocarpa. Planta 230 3 589 597
Horvath L. Peszlen I. Gierlinger N. Peralta P. Steve K. Csoka L. 2012 Distribution of wood polymers within the cell wall of transgenic aspen imaged by Raman microscopy
Jacobs BF, Kingston JD, Jacobs LL 1999 The origin of grass-dominated ecosystems
Draper J. Mur L. A. J. Jenkins G. Ghosh-Biswas G. C. Bablak P. Hasterok R. Routledge A. P. M. 2001 Brachypodium distachyon. A New Model System for Functional Genomics in Grasses. Plant Physiol. 127 4 1539 1555
Albert L. Németh Z. I. Halász G. Koloszár J. Varga S. Takács L. 1999 Radial variation of pH and buffer capacity in the red-heartwooded beech (Fagus silvatica L.) woodHolz als Roh- und Werkst. 57 75 76
Somerville C. Bauer S. Brininstool G. Facette M. Hamann T. Milne J. Osborne E. Alex P. Persson S. Raab T. Vorwerk S. Youngs H. 2004Toward a systems approach to understanding plant cell walls. Science 206 2206 2211
Grabber J. H. Ralph J. Lapierre C. Barriere Y. 2004 Genetic and molecular basis of grass cell-wall degradability. I. Lignin-cell wall matrix interactions.C. R. Biol. 327 5 455 465
Ralph J. Grabber J. H. Hatfield R. D. 1995 Lignin-ferulate cross-links in grasses: active incorporation of ferulate polysaccharide esters into ryegrass ligninsCarbohydr. Res. 275 1 167 178
Ralph J. Hatfield R. D. Sederoff R. R. Mac Kay. J. J. 1998Order and randomness in lignin and lignification: Is a new paradigm for lignification required? Research Summaries: 39 41