Method for automatically identifying spectra of different wood cell wall layers in Raman imaging data set

Raman imaging for the identification of Teflon microplastics and nanoplastics released from non-stick cookware

The characterisation of microplastics is still difficult, and the challenge is even greater for nanoplastics. A possible source of these particles is the scratched surface of a non-stick cooking pot that is mainly coated with Teflon. Herein we employ Raman imaging to scan the surfaces of different non-stick pots and collect spectra as spectrum matrices, akin to a hyperspectral imaging process. We adjust and optimise different algorithms and create a new hybrid algorithm to extract the extremely weak signal of Teflon microplastics and particularly nanoplastics. We use multiple characteristic peaks of Teflon to create several images, and merge them to one, using a logic-based algorithm (i), in order to cross-check them and to increase the signal-noise ratio. To differentiate the varied peak heights towards image merging, an algebra-based algorithm (ii) is developed to process different images with weighting factors. To map the images via the whole set of the spectrum (not just from the individual characteristic peaks), a principal component analysis (PCA)-based algorithm (iii) is employed to orthogonally decode the spectrum matrix to the PCA spectrum and PCA intensity image. To effectively extract the Teflon spectrum information, a new hybrid algorithm is developed to justify the PCA spectra and merge the PCA intensity images with the algebra-based algorithm (PCA/algebra-based algorithm) (iv). Based on these developments and with the help of SEM, we estimate that thousands to millions of Teflon microplastics and nanoplastics might be released during a mimic cooking process. Overall, it is recommended that Raman imaging, along with the signal recognition algorithms, be combined with SEM to characterise and quantify microplastics and nanoplastics.

Raman imaging: An indispensable technique to comprehend the functionalization of lignocellulosic material

With the increasing demands on sustainability in the material science and engineering landscape, the use of wood, a renewable and biodegradable material, for new material development has drawn increasing attentions in the materials science community. To promote the development of new wood-based materials, it is critical to understanding not only wood's hierarchical structure from molecule to macroscale level, but also the interactions of wood with other materials and chemicals upon modification and functionalization. In this review, we discuss the recent advances in the Raman imaging technique, a new approach that combines spectroscopy and microscopy, in wood characterization and structural evolution monitoring during functionalization. We introduce the principles of Raman spectroscopy and common Raman instrumentations. We survey the use of traditional Raman spectroscopy for lignocellulosic material characterizations including cellulose crystallinity determination, holocellulose discrimination, and lignin substructure evaluation. We briefly review the recent studies on wood property enhancement and functional wood-based material development through wood modification including thermal treatment, acetylation, furfurylation, methacrylation, delignification. Subsequently, we highlight the use of the Raman imaging for visualization, spatial and temporal distribution of wood cell wall structure, as well as the microstructure evolution upon functionalization. Finally, we discuss the future prospects of the field.

Visualization and quantification of content and hydrogen bonding state of water in apple and potato cells by confocal Raman microscopy: A comparison study

Water is the most abundant component in fresh fruit and vegetables and its distribution and hydrogen bonding state in cells has a significant influence on food processing. In the current study, an improved method based on our earlier studies was developed to directly visualize the spatial distribution of content and hydrogen bonding state of water in apple and potato cells for the first time and the difference in water distribution in these cells was compared. Additionally, based on the distribution images of content and hydrogen bonding state of water in different regions in apple and potato tissues, the total water and free water contents, and the hydrogen bonding state of free water were quantified and compared with those obtained by nuclear magnetic resonance and Marinchik methods, demonstrating that the method could be successfully used for quantifying the content and hydrogen bonding state of water in fruit and vegetable cells.

Multivariate Raman mapping for phenotypic characterization in plant tissue sections

Identifying and characterizing the biochemical variation in plant tissues is an important task in many research fields. Small spectral differences of the plant cell wall that are caused by genetic or environmental influences may be superimposed by individual variation as well as by a microscopic heterogeneity in molecular composition and structure of different histological substructures. A set of 56 samples from Cucumis sativus (cucumber) plants, comprising a total of ~168,000 spectra from tissue sections of leaf, stem, and roots was investigated by Raman microspectroscopic mapping excited at 532 nm. A multivariate analysis was carried out in order to assess the variation of the spectra with respect to origin of the tissue, the histological (cell wall) substructures, and the possibility to discriminate the spectra obtained from different individuals that had been subjected to two different conditions during growth. Combining the results of principal component analysis (PCA) based classification with the original spatial information in the maps of 23 sections of leaf xylem, variation in cell wall composition is found for four different individuals that also includes a discrimination of tissue grown in the presence and absence of additional silicic acid in the irrigation water of the plants. The spectral data point to differences in a contribution by carotenoids, as well as by hydroxycinnamic acids to the spectra. The results give new insight into the chemical heterogeneity of plant tissues and may be useful for elucidating biochemical processes associated with biomineralization by vibrational spectroscopy.

Visualising lignin quantitatively in plant cell walls by micro-Raman spectroscopy

As a main component in plant cell wall, lignin is commonly determined by wet chemical analysis which only provides general information rather than specifics for different cell wall layers. To address this issue, we attempted to use micro-Raman spectroscopy for quantitative visualisation of the lignin in various cell wall layers during delignification.

Predictive Modeling of Lignin Content for the Screening of Suitable Poplar Genotypes Based on Fourier Transform-Raman Spectrometry

The quick and non-invasive evaluation of lignin from biomass has been the focus of much attention. Several types of spectroscopies, for example, near-infrared (NIR) and Fourier transform-Raman (FT-Raman), have been successfully applied to build quantitative predictive lignin models based on chemometrics. However, due to the effect of sample moisture content and ambient humidity on its signals, NIR spectroscopy requires sophisticated pre-testing preparation. In addition, the current FT-Raman predictive models require large variations in the independent value inputs as restrictions in the corresponding mathematical algorithms prevent the effective biomass screening of suitable genotypes for lignin contents within a narrow range. In order to overcome the limitations associated with the current methods, in this paper, we employed Raman spectra excited using a 1064 nm laser, thus avoiding the impact of water and auto-fluorescence on NIR signals. The optimal baseline correction method, data type, mathematical algorithm, and internal reference were selected in order to build quantitative lignin models based on the data with limited variation. The resulting two predictive models, constructed through lasso and ridge regressions, respectively, proved to be effective in assessing the lignin content of poplar in large-scale breeding and genetic engineering programs.

Visualization of the in situ distribution of contents and hydrogen bonding states of cellular level water in apple tissues by confocal Raman microscopy

Raman spectroscopy has been employed for studying the hydrogen bonding states of water molecules for decades, however, Raman imaging data contain thousands of spectra, making it challenging to obtain information on water with different hydrogen bonds. In the current study, a novel method combining confocal Raman microscopy (CRM) imaging with the iterative curve fitting algorithms was developed to determine the distribution of water contents at the cellular level and water states with different hydrogen bonds in apple tissues. Raman imaging data ranging from 2700 to 3800 cm-1 were acquired from whole cells in the apple tissue, which were then decomposed into seven sub-peaks using the fixed-position Gaussian iterative curve fitting (FPGICF) algorithm. The content and hydrogen bonding states of cellular water were calculated as the area sum of the OH stretching vibration and the area ratio of DA-OH over DDAA-OH stretching vibration or the number of hydrogen bonds of each water molecule, respectively. Finally, the area of each sub-peak, the area sum of the OH stretching vibration, and the area ratio of DA-OH over DDAA-OH stretching vibration were used to visualize the distribution of each sub-peak, water contents and water states with different hydrogen bonds, respectively. In addition, it was found that the number of hydrogen bonds of each water molecule could also be considered as a criterion to describe the hydrogen bond states of water in apple tissues. The availability of such information should provide new insights for future study of cellular water in other food materials.

Subcellular dissolution of xylan and lignin for enhancing enzymatic hydrolysis of microwave assisted deep eutectic solvent pretreated Pinus bungeana Zucc

The mechanism for enhancing enzymatic hydrolysis during microwave-assisted deep eutectic solvent (Mw-DES) pretreatment in deconstruction of plant cell wall was proposed by combining wet chemical analysis and microscopic measurements. Mw-DES pretreatment achieved significantly higher enzymatic conversion of 81.90% with lower lignin and comparable xylan removal (42.81% and 74.73%, respectively). While DES pretreated sample with higher lignin and xylan removal (66.59% and 74.93%, respectively) obtained limited sugar yield (45.67%). There were no significant differences with respect to chemical structures of lignin fraction between DES and Mw-DES pretreatment but primary discrepancies of topochemical and morphological changes were observed. Non- or low-substituted xylan was directly removed from secondary walls (SW) exposed more cellulose for enzyme attacking after Mw-DES pretreatment. Meanwhile, high-substituted xylan and lignin were synergistically dissolved from cell corner middle lamella (CCML). These topochemical changes of components resulted in cracked and porous cell wall structure, thus facilitating the accessibility of cellulose.

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.

Synergy of hemicelluloses removal and bovine serum albumin blocking of lignin for enhanced enzymatic hydrolysis

A cost efficient synergistic strategy combining mild alkaline pretreatment (0.5-5% NaOH at 70 °C for 60 min) and bovine serum albumin (BSA) blocking of lignin was evaluated for effective conversion of poplar. The highest glucose yield of 69.2% was obtained for 5% alkaline pretreated sample, which was 4.4 times that of untreated sample. The enhanced enzymatic hydrolysis was attributed to significant hemicelluloses removal with limited delignification. Delignification mainly occurred in secondary wall, leading to more open cell wall structure, thus facilitating better transport of enzyme. Hemicelluloses removal helped split adjacent microfibrils, thus increased the specific sites for cellulase binding. After BSA addition in enzymatic hydrolysis, cellulose conversion further improved to 78.4% with 33% reduction of cellulase dosage due to decreased non-specific adsorption of cellulase on residual lignin. The utilization of synergistic alkaline pretreatment - BSA strategy may improve the overall economics of biomass conversion and successful commercial implementation of biorefineries.

Insight into plant cell wall chemistry and structure by combination of multiphoton microscopy with Raman imaging

Spontaneous Raman scattering microspectroscopy, second harmonic generation (SHG) and 2-photon excited fluorescence (2PF) were used in combination to characterize the morphology together with the chemical composition of the cell wall in native plant tissues. As the data obtained with unstained sections of Sorghum bicolor root and leaf tissues illustrate, nonresonant as well as pre-resonant Raman microscopy in combination with hyperspectral analysis reveals details about the distribution and composition of the major cell wall constituents. Multivariate analysis of the Raman data allows separation of different tissue regions, specifically the endodermis, xylem and lumen. The orientation of cellulose microfibrils is obtained from polarization-resolved SHG signals. Furthermore, 2-photon autofluorescence images can be used to image lignification. The combined compositional, morphological and orientational information in the proposed coupling of SHG, Raman imaging and 2PF presents an extension of existing vibrational microspectroscopic imaging and multiphoton microscopic approaches not only for plant tissues.

Combining Raman Imaging and Multivariate Analysis to Visualize Lignin, Cellulose, and Hemicellulose in the Plant Cell Wall

The application of Raman imaging to plant biomass is increasing because it can offer spatial and compositional information on aqueous solutions. The analysis does not usually require extensive sample preparation; structural and chemical information can be obtained without labeling. However, each Raman image contains thousands of spectra; this raises difficulties when extracting hidden information, especially for components with similar chemical structures. This work introduces a multivariate analysis to address this issue. The protocol establishes a general method to visualize the main components, including lignin, cellulose, and hemicellulose within the plant cell wall. In this protocol, procedures for sample preparation, spectral acquisition, and data processing are described. It is highly dependent upon operator skill at sample preparation and data analysis. By using this approach, a Raman investigation can be performed by a non-specialist user to acquire high-quality data and meaningful results for plant cell wall analysis.

Method for Removing Spectral Contaminants to Improve Analysis of Raman Imaging Data

The spectral contaminants are inevitable during micro-Raman measurements. A key challenge is how to remove them from the original imaging data, since they can distort further results of data analysis. Here, we propose a method named “automatic pre-processing method for Raman imaging data set (APRI)”, which includes the adaptive iteratively reweighted penalized least-squares (airPLS) algorithm and the principal component analysis (PCA). It eliminates the baseline drifts and cosmic spikes by using the spectral features themselves. The utility of APRI is illustrated by removing the spectral contaminants from a Raman imaging data set of a wood sample. In addition, APRI is computationally efficient, conceptually simple and potential to be extended to other methods of spectroscopy, such as infrared (IR), nuclear magnetic resonance (NMR), X-Ray Diffraction (XRD). With the help of our approach, a typical spectral analysis can be performed by a non-specialist user to obtain useful information from a spectroscopic imaging data set.

Tissue specific response of Miscanthus×giganteus to dilute acid pretreatment for enhancing cellulose digestibility

The recalcitrance in grasses varies according to cell type and tissue. In this study, dilute acid pretreatment was performed on Miscanthus×giganteus internodes that include rind and pith regions which showing heterogeneous structural and chemical changes. Pretreatment on pith effectively hydrolyzed 73.33% hemicelluloses and separated cohesive cell walls from the compound middle lamella due to lignin migration. Lignin droplets with an average diameter of 49.5±29.3nm were concurrently coalesced on wall surface, that in turn exposed more microfibrils deep in walls to be enzymatically hydrolyzed reaching 82.55%. By contrast, the rind with a relatively intergrated cell structure was covered by larger lignin droplets (101.2±44.1nm) and filled with inaccessible microfibrils limiting enzymatic sacchrification (31.50%). Taken together, the cellulose digestibility of biomass was not majorly influenced by cellulose crystallinity, while it was strongly correlated with the positive effects of hemicelluloses degradation, lignin redistribution, cellulose exposure and loosening cell wall structure.

Biopolymer Green Lubricant for Sustainable Manufacturing

We report on the preparation of a biopolymer thin film by hydroxypropyl methylcellulose (HPMC), which can be used as a dry green lubricant in sustainable manufacturing. The thin films were characterized through scanning electron microscopy, energy-dispersive spectroscopy, and Raman spectroscopy; the films showed desirable levels of thickness, controllability, and uniformity. Tribology tests also showed desirable tribological and antiwear behaviors, caused by the formation of transfer layers. Zebrafish embryo toxicity studies showed that HPMC has excellent solubility and biocompatibility, which may show outstanding potential for applications as a green lubricant. The results of the present study show that these techniques for biopolymer HPMC provide an ecologically responsible and convenient method for preparing functional thin films, which is particularly applicable to sustainable manufacturing.

Probing and visualizing the heterogeneity of fiber cell wall deconstruction in sugar maple (Acer saccharum) during liquid hot water pretreatment

The S2 layer was differentiated into heavy-damaged region with more polysaccharides removed and relatively intact light-damaged region after LHW pretreatment.

Phase Segregation in Individually Dried Particles Composed of Biopolymers

Mixing of two biopolymers can results in phase separation due to their thermodynamically incompatibility under certain conditions. This phenomenon was first reported when the solution was allowed to equilibrate, but it has later been observed also as a consequence of drying. The challenges of this study were to observe phase segregation by confocal Raman microscopy and LV-SEM on dried film, individually dried particles, and spray dried particles. The influence of the solid content and the phase ratio (composition) of a HPMC/maltodextrin mixture on the localization of the ingredients in the individually dried particles was investigated. We observed that phase segregation of HPMC and maltodextrin is induced by solvent evaporation in film drying, single particle drying, as well as spray drying. The phase ratio is an important parameter that influences the localization of the HPMC-enriched phase and maltodextrin-enriched phase, i.e., to the particle surface, to the core, or in a more or less bicontinuous pattern. The drying time, affected by the solids content, was found to control the level of advancement of the phase segregation.