Multivariate unmixing approaches on Raman images of plant cell walls: new insights or overinterpretation of results?

Unraveling Molecular Composition in Biological Samples-Systematic Evaluation of Statistical Methods for the Analysis of Hyperspectral Raman Data

Recently, confocal Raman microscopy has gained popularity in biomedical research for studying tissues in healthy and diseased state due to its ability to acquire chemically selective data in a noninvasive approach. However, biological samples, such as brain tissue, are inherently difficult to analyze due to the superposition of molecules in the Raman spectra and low variation of spectral features within the sample. The analysis is further impeded by pathological hallmarks, for example beta-amyloid (Aβ) plaques in Alzheimer's disease, which are often solely characterized by subtle shifts in the respective Raman peaks. To unravel the underlying molecular information, convoluted statistical procedures are inevitable. Unfortunately, such statistical methods are often inadequately described, and most natural scientists lack knowledge of their appropriate use, causing unreproducible results and stagnation in the application of hyperspectral Raman imaging. Therefore, we have set out to provide a comprehensive guide to address these challenges with the example of a complex hyperspectral data set of brain tissue samples with Aβ plaques. Our study encompasses established as well as novel statistical methods, including univariate analysis, principal component analysis, cluster analysis, spectral unmixing, and 2D correlation spectroscopy, and critically compares the outcomes of each analysis. Moreover, we transparently demonstrate the effect of preprocessing decisions like denoising and scaling techniques, providing valuable insights into implications of spectral quality for data evaluation. Thereby, this study provides a comprehensive evaluation of analysis approaches for complex hyperspectral Raman data, laying out a blueprint for elucidating meaningful information from biological samples in chemical imaging.

Strategies and Progress of Raman Technologies for Cellular Uptake Analysis of the Drug Delivery Systems

Nanoparticle (NP)-based drug delivery systems have the potential to significantly enhance the pharmacological and therapeutic properties of drugs. These systems enhance the bioavailability and biocompatibility of pharmaceutical agents via enabling targeted delivery to specific tissues or organs. However, the efficacy and safety of these systems are largely dependent on the cellular uptake and intracellular transport of NPs. Thus, it is crucial to monitor the intracellular behavior of NPs within a single cell. Yet, it is challenging due to the complexity and size of the cell. Recently, the development of the Raman instrumentation offers a versatile tool to allow noninvasive cellular measurements. The primary objective of this review is to highlight the most recent advancements in Raman techniques (spontaneous Raman scattering, bioorthogonal Raman scattering, coherence Raman scattering, and surface-enhanced Raman scattering) when it comes to assessing the internalization of NP-based drug delivery systems and their subsequent movement within cells.

3D (x-y-t) Raman imaging of tomato fruit cuticle: Microchemistry during development

The cuticle is a protective extracellular matrix that covers the above-ground epidermis of land plants. Here, we studied the cuticle of tomato (Solanum lycopersicum L.) fruits in situ using confocal Raman microscopy. Microsections from cuticles isolated at different developmental stages were scanned to visualize cuticle components with a spatial resolution of 342 nm by univariate and multivariate data analysis. Three main components, cutin, polysaccharides, and aromatics, were identified, with the latter exhibiting the strongest Raman scattering intensity. Phenolic acids and flavonoids were differentiated within the cuticle, and three schematic cuticle models were identified during development. Phenolic acids were found across the entire cuticle at the earliest stage of development, i.e. during the formation of the procuticle layer. Based on a mixture analysis with reference component spectra, the phenolic acids were identified as mainly esterified p-coumaric acid together with free p-hydroxybenzoic acid. During the cell expansion period of growth, phenolic acids accumulated in an outermost layer of the cuticle and in the middle region of the pegs. In these stages of development, cellulose and pectin were detected next to the inner cuticle region, close to the epidermal cell where flavonoid impregnation started during ripening. In the first ripening stage, chalconaringenin was observed, while methoxylated chalcones were chosen by the algorithm to fit the mature cuticle spectra. The colocation of carbohydrates, esterified p-coumaric acid, and methoxylated chalconaringenin suggests that the latter two link polysaccharide and cutin domains. Elucidating the different distribution of aromatics within the cuticle, suggests important functions: (1) overall impregnation conferring mechanical and thermal functions (2) the outermost phenolic acid layer displaying UV-B protection of the plant tissue.

Classification and Prediction by Pigment Content in Lettuce (Lactuca sativa L.) Varieties Using Machine Learning and ATR-FTIR Spectroscopy

Green or purple lettuce varieties produce many secondary metabolites, such as chlorophylls, carotenoids, anthocyanins, flavonoids, and phenolic compounds, which is an emergent search in the field of biomolecule research. The main objective of this study was to use multivariate and machine learning algorithms on Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)-based spectra to classify, predict, and categorize chemometric attributes. The cluster heatmap showed the highest efficiency in grouping similar lettuce varieties based on pigment profiles. The relationship among pigments was more significant than the absolute contents. Other results allow classification based on ATR-FTIR fingerprints of inflections associated with structural and chemical components present in lettuce, obtaining high accuracy and precision (>97%) by using principal component analysis and discriminant analysis (PCA-LDA)-associated linear LDA and SVM machine learning algorithms. In addition, PLSR models were capable of predicting Chla, Chlb, Chla+b, Car, AnC, Flv, and Phe contents, with R2P and RPDP values considered very good (0.81−0.88) for Car, Anc, and Flv and excellent (0.91−0.93) for Phe. According to the RPDP metric, the models were considered excellent (>2.10) for all variables estimated. Thus, this research shows the potential of machine learning solutions for ATR-FTIR spectroscopy analysis to classify, estimate, and characterize the biomolecules associated with secondary metabolites in lettuce.

Rapid Quantification Method for Yield, Calorimetric Energy and Chlorophyll a Fluorescence Parameters in Nicotiana tabacum L. Using Vis-NIR-SWIR Hyperspectroscopy

High-throughput and large-scale data are part of a new era of plant remote sensing science. Quantification of the yield, energetic content, and chlorophyll a fluorescence (ChlF) remains laborious and is of great interest to physiologists and photobiologists. We propose a new method that is efficient and applicable for estimating photosynthetic performance and photosystem status using remote sensing hyperspectroscopy with visible, near-infrared and shortwave spectroscopy (Vis-NIR-SWIR) based on rapid multivariate partial least squares regression (PLSR) as a tool to estimate biomass production, calorimetric energy content and chlorophyll a fluorescence parameters. The results showed the presence of typical inflections associated with chemical and structural components present in plants, enabling us to obtain PLSR models with R2P and RPDP values greater than >0.82 and 3.33, respectively. The most important wavelengths were well distributed into 400 (violet), 440 (blue), 550 (green), 670 (red), 700−750 (red edge), 1330 (NIR), 1450 (SWIR), 1940 (SWIR) and 2200 (SWIR) nm operating ranges of the spectrum. Thus, we report a methodology to simultaneously determine fifteen attributes (i.e., yield (biomass), ΔH°area, ΔH°mass, Fv/Fm, Fv’/Fm’, ETR, NPQ, qP, qN, ΦPSII, P, D, SFI, PI(abs), D.F.) with high accuracy and precision and with excellent predictive capacity for most of them. These results are promising for plant physiology studies and will provide a better understanding of photosystem dynamics in tobacco plants when a large number of samples must be evaluated within a short period and with remote acquisition data.

Raman Method in Identification of Species and Varieties, Assessment of Plant Maturity and Crop Quality—A Review

The present review covers reports discussing potential applications of the specificity of Raman techniques in the advancement of digital farming, in line with an assumption of yield maximisation with minimum environmental impact of agriculture. Raman is an optical spectroscopy method which can be used to perform immediate, label-free detection and quantification of key compounds without destroying the sample. The authors particularly focused on the reports discussing the use of Raman spectroscopy in monitoring the physiological status of plants, assessing crop maturity and quality, plant pathology and ripening, and identifying plant species and their varieties. In recent years, research reports have presented evidence confirming the effectiveness of Raman spectroscopy in identifying biotic and abiotic stresses in plants as well as in phenotyping and digital selection of plants in farming. Raman techniques used in precision agriculture can significantly improve capacities for farming management, crop quality assessment, as well as biological and chemical contaminant detection, thereby contributing to food safety as well as the productivity and profitability of agriculture. This review aims to increase the awareness of the growing potential of Raman spectroscopy in agriculture among plant breeders, geneticists, farmers and engineers.

Practical considerations for quantitative and reproducible measurements with stimulated Raman scattering microscopy

This tutorial review presents the most important practical considerations for sample preparation, instrument set-up, image acquisition and data analysis to obtain reproducible SRS measurements.

Cell wall characteristics during sexual reproduction of Mougeotia sp. (Zygnematophyceae) revealed by electron microscopy, glycan microarrays and RAMAN spectroscopy

Mougeotia spp. collected from field samples were investigated for their conjugation morphology by light-, fluorescence-, scanning- and transmission electron microscopy. During a scalarifom conjugation, the extragametangial zygospores were initially surrounded by a thin cell wall that developed into a multi-layered zygospore wall. Maturing zygospores turned dark brown and were filled with storage compounds such as lipids and starch. While M. parvula had a smooth surface, M. disjuncta had a punctated surface structure and a prominent suture. The zygospore wall consisted of a polysaccharide rich endospore, followed by a thin layer with a lipid-like appaerance, a massive electron dense mesospore and a very thin exospore composed of polysaccharides. Glycan microarray analysis of zygospores of different developmental stages revealed the occurrence of pectins and hemicelluloses, mostly composed of homogalacturonan (HG), xyloglucans, xylans, arabino-galactan proteins and extensins. In situ localization by the probe OG7-13^{AF 488} labelled HG in young zygospore walls, vegetative filaments and most prominently in conjugation tubes and cross walls. Raman imaging showed the distribution of proteins, lipids, carbohydrates and aromatic components of the mature zygospore with a spatial resolution of ~ 250 nm. The carbohydrate nature of the endo- and exospore was confirmed and in-between an enrichment of lipids and aromatic components, probably algaenan or a sporopollenin-like material. Taken together, these results indicate that during zygospore formation, reorganizations of the cell walls occured, leading to a resistant and protective structure.

Raman spectroscopy reveals collagen and phospholipids as major components of hyalinosis in the arteriolosclerotic ulcer of Martorell

BACKGROUND

Arteriolosclerotic ulcers of Martorell are histologically characterized by hyaline arteriolosclerosis resulting in concentric occlusion of the arteriolar lumina. Although several authors have previously reported on hyaline changes in hypertensive arteriolopathies, so far, little information is available on the molecular composition of hyaline wall depositions.

OBJECTIVES

This study aimed at the molecular characterization of hyaline arteriolar deposits in patients with hypertensive arteriolopathy using confocal Raman spectroscopy.

METHODS

Samples of patients diagnosed with arteriolosclerotic ulcers of Martorell were analysed using confocal Raman spectroscopy. The findings were correlated with histological analyses. Skin samples from healthy, non-hypertensive patients served as controls.

RESULTS

Confocal Raman spectroscopy analysis revealed that subendothelial hyaline deposits in arteriolosclerotic ulcers are mainly composed of collagen and phospholipids, in particular phosphatidylcholine. The presence of collagen within hyaline deposits was confirmed by Masson's Trichrome and Picrosirius Red staining. Additionally, the presence of collagen could also be shown for hypertensive nephrosclerosis. Actin was markedly decreased in hyalinized compared to control vessels, corresponding to the loss of smooth muscle cells in the process of hyalinization. This was confirmed by immunofluorescence staining for α-smooth muscle actin and desmin.

CONCLUSION

The present findings suggest that arteriolar hyaline deposits in hypertensive arteriolopathy are mainly composed of collagen and phospholipids, in particular phosphatidylcholine. Together with the concurrent absence of actin, these findings suggest that potentially critical disease mechanisms involve pressure-induced vascular smooth muscle cell apoptosis with subsequent deposition of collagen.

Raman spectroscopy enables phenotyping and assessment of nutrition values of plants: a review

Our civilization has to enhance food production to feed world's expected population of 9.7 billion by 2050. These food demands can be met by implementation of innovative technologies in agriculture. This transformative agricultural concept, also known as digital farming, aims to maximize the crop yield without an increase in the field footprint while simultaneously minimizing environmental impact of farming. There is a growing body of evidence that Raman spectroscopy, a non-invasive, non-destructive, and laser-based analytical approach, can be used to: (i) detect plant diseases, (ii) abiotic stresses, and (iii) enable label-free phenotyping and digital selection of plants in breeding programs. In this review, we critically discuss the most recent reports on the use of Raman spectroscopy for confirmatory identification of plant species and their varieties, as well as Raman-based analysis of the nutrition value of seeds. We show that high selectivity and specificity of Raman makes this technique ideal for optical surveillance of fields, which can be used to improve agriculture around the world. We also discuss potential advances in synergetic use of RS and already established imaging and molecular techniques. This combinatorial approach can be used to reduce associated time and cost, as well as enhance the accuracy of diagnostics of biotic and abiotic stresses.

Multivariate analysis of Brillouin imaging data by supervised and unsupervised learning

Brillouin imaging relies on the reliable extraction of subtle spectral information from hyperspectral datasets. To date, the mainstream practice has been to use line fitting of spectral features to retrieve the average peak shift and linewidth parameters. Good results, however, depend heavily on sufficient signal-to-noise ratio and may not be applicable in complex samples that consist of spectral mixtures. In this work, we thus propose the use of various multivariate algorithms that can be used to perform supervised or unsupervised analysis of the hyperspectral data, with which we explore advanced image analysis applications, namely unmixing, classification and segmentation in a phantom and live cells. The resulting images are shown to provide more contrast and detail, and obtained on a timescale ∼10² faster than fitting. The estimated spectral parameters are consistent with those calculated from pure fitting.

A belt for the cell: cellulosic wall thickenings and their role in morphogenesis of the 3D puzzle cells in walnut shells

Walnut (Juglans regia) kernels are protected by a tough shell consisting of polylobate sclereids that interlock into a 3D puzzle. The shape transformations from isodiametric to lobed cells is well documented for 2D pavement cells, but not for 3D puzzle sclereids. Here, we study the morphogenesis of these cells by using a combination of different imaging techniques. Serial face-microtomy enabled us to reconstruct tissue growth of whole walnut fruits in 3D, and serial block face-scanning electron microscopy exposed cell shapes and their transformation in 3D during shell tissue development. In combination with Raman and fluorescence microscopy, we revealed multiple loops of cellulosic thickenings in cell walls, acting as stiff restrictions during cell growth and leading to the lobed cell shape. Our findings contribute to a better understanding of the 3D shape transformation of polylobate sclereids and the role of pectin and cellulose within this process.

Overview of Popular Techniques of Raman Spectroscopy and Their Potential in the Study of Plant Tissues

Raman spectroscopy is one of the main analytical techniques used in optical metrology. It is a vibration, marker-free technique that provides insight into the structure and composition of tissues and cells at the molecular level. Raman spectroscopy is an outstanding material identification technique. It provides spatial information of vibrations from complex biological samples which renders it a very accurate tool for the analysis of highly complex plant tissues. Raman spectra can be used as a fingerprint tool for a very wide range of compounds. Raman spectroscopy enables all the polymers that build the cell walls of plants to be tracked simultaneously; it facilitates the analysis of both the molecular composition and the molecular structure of cell walls. Due to its high sensitivity to even minute structural changes, this method is used for comparative tests. The introduction of new and improved Raman techniques by scientists as well as the constant technological development of the apparatus has resulted in an increased importance of Raman spectroscopy in the discovery and defining of tissues and the processes taking place in them.

Pixel-based Raman hyperspectral identification of complex pharmaceutical formulations

Hyperspectral imaging has been widely used for different kinds of applications and many chemometric tools have been developed to help identifying chemical compounds. However, most of those tools rely on factorial decomposition techniques that can be challenging for large data sets and/or in the presence of minor compounds. The present study proposes a pixel-based identification (PBI) approach that allows readily identifying spectral signatures in Raman hyperspectral imaging data. This strategy is based on the identification of essential spectral pixels (ESP), which can be found by convex hull calculation. As the corresponding set of spectra is largely reduced and encompasses the purest spectral signatures, direct database matching and identification can be reliably and rapidly performed. The efficiency of PBI was evaluated on both known and unknown samples, considering genuine and falsified pharmaceutical tablets. We showed that it is possible to analyze a wide variety of pharmaceutical formulations of increasing complexity (from 5 to 0.1% (w/w) of polymorphic impurity detection) for medium (150 x 150 pixels) and big (1000 x 1000 pixels) map sizes in less than 2 min. Moreover, in the case of falsified medicines, it is demonstrated that the proposed approach allows the identification of all compounds, found in very different proportions and, sometimes, in trace amounts. Furthermore, the relevant spectral signatures for which no match is found in the reference database can be identified at a later stage and the nature of the corresponding compounds further investigated. Overall, the provided results show that Raman hyperspectral imaging combined with PBI enables rapid and reliable spectral identification of complex pharmaceutical formulations.

Multicolour chemical imaging of plant tissues with hyperspectral stimulated Raman scattering microscopy

Recent development of stimulated Raman scattering (SRS) microscopy allows for label-free biological imaging with chemical specificity based on molecular-vibrational signatures. In particular, hyperspectral SRS imaging can acquire a molecular-vibrational spectrum at each pixel, allowing us not only to investigate the spectral difference of various biological molecules but also to discriminate different constituents based on their spectral difference. However, the number of constituents discriminated in previous label-free SRS imaging was limited to four because of the subtleness of spectral difference. Here, we report hyperspectral SRS imaging of plant tissues including leaves of Camellia japonica, roots of Arabidopsis thaliana, and thalli of a liverwort Marchantia polymorpha L. We show that SRS can discriminate as many as six components in Marchantia polymorpha L. without labeling. Our results demonstrate the effectiveness of hyperspectral SRS imaging as a tool for label-free multicolour imaging analysis of various biomolecules in plant tissues.

Raman imaging reveals in-situ microchemistry of cuticle and epidermis of spruce needles

BACKGROUND

The cuticle is a protective layer playing an important role in plant defense against biotic and abiotic stresses. So far cuticle structure and chemistry was mainly studied by electron microscopy and chemical extraction. Thus, analysing composition involved sample destruction and the link between chemistry and microstructure remained unclear. In the last decade, Raman imaging showed high potential to link plant anatomical structure with microchemistry and to give insights into orientation of molecules. In this study, we use Raman imaging and polarization experiments to study the native cuticle and epidermal layer of needles of Norway spruce, one of the economically most important trees in Europe. The acquired hyperspectral dataset is the basis to image the chemical heterogeneity using univariate (band integration) as well as multivariate data analysis (cluster analysis and non-negative matrix factorization).

RESULTS

Confocal Raman microscopy probes the cuticle together with the underlying epidermis in the native state and tracks aromatics, lipids, carbohydrates and minerals with a spatial resolution of 300 nm. All three data analysis approaches distinguish a waxy, crystalline layer on top, in which aliphatic chains and coumaric acid are aligned perpendicular to the surface. Also in the lipidic amorphous cuticle beneath, strong signals of coumaric acid and flavonoids are detected. Even the unmixing algorithm results in mixed endmember spectra and confirms that lipids co-locate with aromatics. The underlying epidermal cell walls are devoid of lipids but show strong aromatic Raman bands. Especially the upper periclinal thicker cell wall is impregnated with aromatics. At the interface between epidermis and cuticle Calcium oxalate crystals are detected in a layer-like fashion. Non-negative matrix factorization gives the purest component spectra, thus the best match with reference spectra and by this promotes band assignments and interpretation of the visualized chemical heterogeneity.

CONCLUSIONS

Results sharpen our view about the cuticle as the outermost layer of plants and highlight the aromatic impregnation throughout. In the future, developmental studies tracking lipid and aromatic pathways might give new insights into cuticle formation and comparative studies might deepen our understanding why some trees and their needle and leaf surfaces are more resistant to biotic and abiotic stresses than others.

Raman mapping coupled to self-modelling MCR-ALS analysis to estimate active cosmetic ingredient penetration profile in skin

Confocal Raman mapping (CRM) is a powerful, label free, non-destructive tool, enabling molecular characterization of human skin with applications in the dermo-cosmetic field. Coupling CRM to multivariate analysis can be used to monitor the penetration and permeation of active cosmetic ingredients (ACI) after topical application. It is presently illustrated how multivariate curve resolution alternating least squares (MCR-ALS) can be applied to detect and semi-quantitatively describe the diffusion profile of Delipidol, a commercially available slimming ACI, from Raman spectral maps. Although the analysis outcome can be critically dependent on the a priori selection of the number of regression components, it is demonstrated that profiling of the kinetics of diffusion into the skin can be established with or without additionnal spectral equality constraints in the multivariate analysis, with similar results. Ultimately, MCR-ALS, applied without spectral equality contraints, specifically identifies the ACI as one of main spectral components enabling to investigate its distribution and penetration into the stratum corneum and underlying epidermis layers.

From the Soft to the Hard: Changes in Microchemistry During Cell Wall Maturation of Walnut Shells

The walnut shell is a hard and protective layer that provides an essential barrier between the seed and its environment. The shell is based on only one unit cell type: the polylobate sclerenchyma cell. For a better understanding of the interlocked walnut shell tissue, we investigate the structural and compositional changes during the development of the shell from the soft to the hard state. Structural changes at the macro level are explored by X-ray tomography and on the cell and cell wall level various microscopic techniques are applied. Walnut shell development takes place beneath the outer green husk, which protects and delivers components during the development of the walnut. The cells toward this outer green husk have the thickest and most lignified cell walls. With maturation secondary cell wall thickening takes place and the amount of all cell wall components (cellulose, hemicelluloses and especially lignin) is increased as revealed by FTIR microscopy. Focusing on the cell wall level, Raman imaging showed that lignin is deposited first into the pectin network between the cells and cell corners, at the very beginning of secondary cell wall formation. Furthermore, Raman imaging of fluorescence visualized numerous pits as a network of channels, connecting all the interlocked polylobate walnut shells. In the final mature stage, fluorescence increased throughout the cell wall and a fluorescent layer was detected toward the lumen in the inner part. This accumulation of aromatic components is reminiscent of heartwood formation of trees and is suggested to improve protection properties of the mature walnut shell. Understanding the walnut shell and its development will inspire biomimetic material design and packaging concepts, but is also important for waste valorization, considering that walnuts are the most widespread tree nuts in the world.

Principles and Applications of Vibrational Spectroscopic Imaging in Plant Science: A Review

Detailed knowledge about plant chemical constituents and their distributions from organ level to sub-cellular level is of critical interest to basic and applied sciences. Spectral imaging techniques offer unparalleled advantages in that regard. The core advantage of these technologies is that they acquire spatially distributed semi-quantitative information of high specificity towards chemical constituents of plants. This forms invaluable asset in the studies on plant biochemical and structural features. In certain applications, non-invasive analysis is possible. The information harvested through spectral imaging can be used for exploration of plant biochemistry, physiology, metabolism, classification, and phenotyping among others, with significant gains for basic and applied research. This article aims to present a general perspective about vibrational spectral imaging/micro-spectroscopy in the context of plant research. Within the scope of this review are infrared (IR), near-infrared (NIR) and Raman imaging techniques. To better expose the potential and limitations of these techniques, fluorescence imaging is briefly overviewed as a method relatively less flexible but particularly powerful for the investigation of photosynthesis. Included is a brief introduction to the physical, instrumental, and data-analytical background essential for the applications of imaging techniques. The applications are discussed on the basis of recent literature.

Label-free visualization of lignin deposition in loquats using complementary stimulated and spontaneous Raman microscopy

The lignification triggered by biotic or abiotic stresses hardens fruits and vegetables and eventually influences their consumer appeal. Extensive prior efforts have been made to unveil the underlying mechanism of flesh lignification, primarily focused on its physicochemical and molecular biological properties. Nevertheless, most of these studies used destroyed and homogenized bulk tissues as analytes; as a result, potentially valuable spatial information was lost. In this study, the deposition of lignin in loquat flesh during lignification was visualized from the tissue level to the single-cell level by combining the advantages of stimulated Raman scattering (SRS) and spontaneous Raman microscopy using label-free in situ molecular imaging. SRS has the advantages of being fast and providing large-area chemical imaging to reveal the spatial heterogeneity of lignin and cell wall polysaccharide distribution in loquat flesh. After 2 days of storage at 0 °C, increased lignins were observed by large-area SRS imaging. In addition, microscopic SRS images of the flesh cells indicated that the increased lignins were trapped in the cell corner (CC) and middle lamella (ML). Furthermore, the compositional and structural features of lignified cells (LCs), CC and ML of loquat flesh were investigated by spontaneous Raman microscopy, and the results showed that the LCs were a combination of lignin, cellulose, and hemicellulose, whereas CC and ML showed only deposited lignin and pectin without cross-linked cellulose and hemicellulose. This result further suggests that the lignins in the CC and ML regions of loquats were later synthesized alone during postharvest storage. This innovative combination of SRS and spontaneous Raman microscopy allows the label-free macroscale and fine chemical imaging of plant cell walls and will enhance our fundamental understanding of the structures and functions of the plant cell wall.

Analysis of Cellulose and Lignocellulose Materials by Raman Spectroscopy: A Review of the Current Status

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