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Vitrinite reflectance (VRo) is a standard petrographic method for assessing thermal maturity (rank) of coal. The vitrinite reflectance technique, however, requires significant petrographic experience, can be time-consuming, and may be biased by analyst subjectivity. Correlations between coal rank and Raman spectral properties are a promising alternative that can supplant some of the limitations inherent in the VRo protocol. The traditional peak-fitting methodologies for quantifying metrics from Raman spectra, however, also suffer from analyst subjectivity that can affect correlations between analyte and spectral properties. This research combines high-throughput Raman spectroscopy with multivariate analysis (MVA) to create calibration models for the prediction of coal rank though VRo and atomic O/C ratio. MVA techniques eliminate the ambiguous subjectivity prevalent in peak-fitting methods by evaluating the full Raman spectrum, then identifying the integral vibrational modes for constructing accurate models. Partial least squares (PLS) regression models were developed using Raman spectra and VRo values (0.23–5.23%) for 68 geographically diverse coal samples. The calibration set was validated using one-half of the samples to rigorously assess the model’s predictive accuracy. The root mean standard error of prediction was 0.19 for the VRo model and 0.014 for the atomic O/C model. Both models exhibited linear correlations, with coefficients of determination (R2) for the validation set of 0.99 (VRo) and 0.93 (atomic O/C), despite the geographic and rank diversity of the samples. This study demonstrates the applicability and power of using PLS models for the prediction of both the VRo and atomic O/C ratio from Raman spectra. The quantitative MVA protocol contained herein provides a Raman alternative to the VRo industry benchmark for coal rank that is not subject to the limitations and subjectivity of peak-fitting methods.

Pulmonary toxicity studies on carbon nanotubes focus primarily on as-produced materials and rarely are guided by a life cycle perspective or integration with exposure assessment. Understanding toxicity beyond the as-produced, or pure native material, is critical, due to modifications needed to overcome barriers to commercialization of applications. In the first series of studies, the toxicity of as-produced carbon nanotubes and their polymer-coated counterparts was evaluated in reference to exposure assessment, material characterization, and stability of the polymer coating in biological fluids. The second series of studies examined the toxicity of aerosols generated from sanding polymer-coated carbon-nanotube-embedded or neat composites. Postproduction modification by polymer coating did not enhance pulmonary injury, inflammation, and pathology or in vitro genotoxicity of as-produced carbon nanotubes, and for a particular coating, toxicity was significantly attenuated. The aerosols generated from sanding composites embedded with polymer-coated carbon nanotubes contained no evidence of free nanotubes. The percent weight incorporation of polymer-coated carbon nanotubes, 0.15% or 3% by mass, and composite matrix utilized altered the particle size distribution and, in certain circumstances, influenced acute in vivo toxicity. Our study provides perspective that, while the number of workers and consumers increases along the life cycle, toxicity and/or potential for exposure to the as-produced material may greatly diminish.

Corymbia (a eucalypt) is an important forestry genus and a potential lignocellulosic bioenergy feedstock. The composition of the lignocellulosic cell wall significantly impacts pretreatment efficiency and conversion to biofuel but is variable and changes with age. In this study, we estimated Klason lignin content, composition, and monosaccharide (glucose and xylose) release after enzymatic saccharification of untreated and hydrothermally pretreated biomass from Corymbia parental species Corymbia torelliana (CT), Corymbia citriodora subsp. variegata (spotted gum; CCV), and interspecific F1 hybrids (CT×CCV) at ages 9 and 12 years from planting. Analysis of lignin composition derived from syringyl/guaiacyl monolignols (S/G) found significant differences among taxa, with CT S/G ratios (2.2 and 2.0) being significantly lower than CCV (2.6 and 2.3) or hybrids (2.5 and 2.3) at ages 9 and 12 respectively. In general, enzymatic saccharification yields from untreated biomass were significantly different among taxa, with CT (113 and 75mgg−1) and hybrids (108 and 81mgg−1) yielding significantly higher glucose from untreated biomass than CCV (82 and 56mgg−1) at ages 9 and 12 respectively. Comparison of traits within taxa between ages 9 and 12 found S/G ratios and glucose yields from untreated biomass were significantly lower in CT, CCV and hybrid taxa. In conclusion, the formation of lignocellulosic cell walls is complex, influenced by genetics and age of material, requiring optimization of rotation age for biofuel production and other industrial processes.

As the attraction of creating biofuels and bio-based chemicals from lignocellulosic biomass has increased, researchers have been challenged with developing a better understanding of lignin structure, quantity and potential uses. Lignin has frequently been considered a waste-product from the deconstruction of plant cell walls, in attempts to isolate polysaccharides that can be hydrolyzed and fermented into fuel or other valuable commodities. In order to develop useful applications for lignin, accurate analytical instrumentation and methodologies are required to qualitatively and quantitatively assess, for example, what the structure of lignin looks like or how much lignin comprises a specific feedstock׳s cellular composition. During the past decade, various diverse strategies have been employed to elucidate the structure and composition of lignin. These techniques include using two-dimensional nuclear magnetic resonance to resolve overlapping spectral data, measuring biomass with vibrational spectroscopy to enable modeling of lignin content or monomeric ratios, methods to probe and quantify the linkages between lignin and polysaccharides, or refinements of established methods to provide higher throughput analyses, less use of consumables, etc. This review seeks to provide a comprehensive overview of many of the advancements achieved in evaluating key lignin attributes. Emphasis is placed on research endeavored in the last decade.

The productivity of plants as biofuel or biomaterial crops is established by both the yield of plant biomass per unit area of land and the efficiency of conversion of the biomass to biofuel. Higher yielding biofuel crops with increased conversion efficiencies allow production on a smaller land footprint minimizing competition with agriculture for food production and biodiversity conservation. Plants have traditionally been domesticated for food, fibre and feed applications. However, utilization for biofuels may require the breeding of novel phenotypes, or new species entirely. Genomics approaches support genetic selection strategies to deliver significant genetic improvement of plants as sources of biomass for biofuel manufacture. Genetic modification of plants provides a further range of options for improving the composition of biomass and for plant modifications to assist the fabrication of biofuels. The relative carbohydrate and lignin content influences the deconstruction of plant cell walls to biofuels. Key options for facilitating the deconstruction leading to higher monomeric sugar release from plants include increasing cellulose content, reducing cellulose crystallinity, and/or altering the amount or composition of noncellulosic polysaccharides or lignin. Modification of chemical linkages within and between these biomass components may improve the ease of deconstruction. Expression of enzymes in the plant may provide a cost-effective option for biochemical conversion to biofuel.

The current research builds upon a previously published study that demonstrated the combination of Raman spectroscopy coupled with multivariate analysis (MVA) for the prediction of thermal maturity in coal by evaluating the efficacy of this method for the prediction of thermal maturity in shale. MVA techniques eliminate analyst bias in peak-fitting methods by using the full Raman spectrum, and then extricating the important spectral regions for distinguishing samples and building accurate, robust models. Partial least squares (PLS) regression models were developed using Raman spectra and VRo values (0.58–4.59%) for 53 geographically diverse shale chip samples, and 43 shale powder samples. Separate PLS models were built using Raman spectra from shale chips or powders. The calibration sets were validated using approximately one-third of the samples to rigorously assess the predictive accuracy of the models. The root mean standard error of prediction was 0.24 for the shale chip model, and 0.28 for the shale powder model. The coefficients of determination (R2) for the cross-validated data sets were identical (0.90, chips; 0.90, powders), revealing a strong linearity despite the geographic and age diversity of the samples. This study demonstrates the validity of using PLS models for the prediction of shale VRo from Raman spectra. The MVA method described herein presents a Raman alternative to the VRo industry benchmark for assessing thermal maturity in shale that is not imperiled by the shortcomings and subjectivity of peak-fitting methods.

In order for a lignocellulosic bioenergy feedstock to be considered sustainable, it must possess a high rate of growth to supply biomass for conversion. Despite the desirability of a fast growth rate for industrial application, it is unclear what effect growth rate has on biomass composition or saccharification. We characterized Klason lignin, glucan, and xylan content with response to growth in Corymbia interspecific F1 hybrid families (HF) and parental species Corymbia torelliana and C. citriodora subspecies variegata and measured the effects on enzymatic hydrolysis from hydrothermally pretreated biomass. Analysis of biomass composition within Corymbia populations found similar amounts of Klason lignin content (19.7–21.3%) among parental and hybrid populations, whereas glucan content was clearly distinguished within C. citriodora subspecies variegata (52%) and HF148 (60%) as compared to other populations (28–38%). Multiple linear regression indicates that biomass composition is significantly impacted by tree size measured at the same age, with Klason lignin content increasing with diameter breast height (DBH) (+0.12% per cm DBH increase), and glucan and xylan typically decreasing per DBH cm increase (-0.7 and -0.3%, respectively). Polysaccharide content within C. citriodora subspecies variegata and HF-148 were not significantly affected by tree size. High-throughput enzymatic saccharification of hydrothermally pretreated biomass found significant differences among Corymbia populations for total glucose production from biomass, with parental Corymbia torelliana and hybrids HF-148 and HF-51 generating the highest amounts of glucose (~180 mg/g biomass, respectively), with HF-51 undergoing the most efficient glucan-to-glucose conversion (74%). Based on growth rate, biomass composition, and further optimization of enzymatic saccharification yield, high production Corymbia hybrid trees are potentially suitable for fast-rotation bioenergy or biomaterial production.

The conversion of lignocellulosic biomass to fuels, chemicals, and other commodities has been explored as one possible pathway toward reductions in the use of non-renewable energy sources. In order to identify which plants, out of a diverse pool, have the desired chemical traits for downstream applications, attributes, such as cellulose and lignin content, or monomeric sugar release following an enzymatic saccharification, must be compared. The experimental and data analysis protocols of the standard methods of analysis can be time-consuming, thereby limiting the number of samples that can be measured. High-throughput (HTP) methods alleviate the shortcomings of the standard methods, and permit the rapid screening of available samples to isolate those possessing the desired traits. This study illustrates the HTP sugar release and pyrolysis-molecular beam mass spectrometry pipelines employed at the National Renewable Energy Lab. These pipelines have enabled the efficient assessment of thousands of plants while decreasing experimental time and costs through reductions in labor and consumables.

© 2015 Lupoi et al. Background: Slow-degrading, fossil fuel-derived plastics can have deleterious effects on the environment, especially marine ecosystems. The production of bio-based, biodegradable plastics from or in plants can assist in supplanting those manufactured using fossil fuels. Polyhydroxybutyrate (PHB) is one such biodegradable polyester that has been evaluated as a possible candidate for relinquishing the use of environmentally harmful plastics. Results: PHB, possessing similar properties to polyesters produced from non-renewable sources, has been previously engineered in sugarcane, thereby creating a high-value co-product in addition to the high biomass yield. This manuscript illustrates the coupling of a Fourier-transform infrared microspectrometer, equipped with a focal plane array (FPA) detector, with multivariate imaging to successfully identify and localize PHB aggregates. Principal component analysis imaging facilitated the mining of the abundant quantity of spectral data acquired using the FPA for distinct PHB vibrational modes. PHB was measured in the chloroplasts of mesophyll and bundle sheath cells, acquiescent with previously evaluated plant samples. Conclusion: This study demonstrates the power of IR microspectroscopy to rapidly image plant sections to provide a snapshot of the chemical composition of the cell. While PHB was localized in sugarcane, this method is readily transferable to other value-added co-products in different plants.

The creation of fuels, chemicals, and materials from plants can aid in replacing products fabricated from non-renewable energy sources. Before using biomass in downstream applications, it must be characterized to assess chemical traits, such as cellulose, lignin, or lignin monomer content, or the sugars released following an acid or enzymatic hydrolysis. The measurement of these traits allows researchers to gauge the recalcitrance of the plants, and develop efficient deconstruction strategies to maximize yields. Standard methods for assessing biomass phenotypes often have experimental protocols that limit their use for screening sizeable numbers of plant species. Raman spectroscopy, a non-destructive, non-invasive vibrational spectroscopy technique, is capable of providing qualitative, structural information and quantitative measurements. Applications of Raman spectroscopy have aided in alleviating the constraints of standard methods by coupling spectral data with multivariate analysis to construct models capable of predicting analytes. Hydrolysis and fermentation products, such as glucose and ethanol, can be quantified off-, at-, or on-line. Raman imaging has enabled researchers to develop a visual understanding of reactions, such as different pretreatment strategies, in real time, while also providing integral chemical information. This review provides an overview of what Raman spectroscopy is, and how it has been applied to the analysis of whole lignocellulosic biomass, its derivatives, and downstream process monitoring.

Biomass representing different classes of bioenergy feedstocks, including woody and herbaceous species, was measured with 1064 nm Raman spectroscopy. Pine, oak, poplar, kenaf, miscanthus, pampas grass, switchgrass, alfalfa, orchard grass, and red clover were included in this study. Spectral differences have been identified with an emphasis on lignin guaiacyl and syringyl monomer content and carotenoid compounds. The interpretation of the Raman spectra was correlated with 13C-nuclear magnetic resonance cross-polarization/magic-angle spinning spectra of select biomass samples. Thioacidolysis quantification of guaiacyl and syringyl monomer composition and the library of Raman spectra were used as a training set to develop a principal component analysis model for classifying plant samples and a principal component regression model for quantifying lignin guaiacyl and syringyl composition. Raman spectroscopy with 1064 nm excitation offers advantages over alternative techniques for biomass characterization, including low spectral backgrounds, higher spectral resolution, short analysis times, and nondestructive analyses.

This dissertation focuses on techniques for (1) increasing ethanol yields from saccharification and fermentation of cellulose using immobilized cellulase, and (2) the characterization and classification of lignocellulosic feedstocks, and quantification of useful parameters such as the syringyl/guaiacyl (S/G) lignin monomer content using 1064 nm dispersive multichannel Raman spectroscopy and chemometrics.

The mixed phenylpropanoid polymer lignin is one of the most abundant biopolymers on the planet and is used in the paper, pulp and biorenewable industries. For many downstream applications, the lignin monomeric composition is required, but traditional methods for performing this analysis do not necessarily represent the lignin composition as it existed in the plant. Herein, it is shown that Raman spectroscopy can be used to measure the lignin monomer composition. The use of 1064 nm excitation is needed for lignin analyses since high fluorescence backgrounds are measured at wavelengths as long as 785 nm. The instrument used for these measurements is a 1064 nm dispersive multichannel Raman spectrometer that is suitable for applications outside of the laboratory, for example in-field or in-line analyses and using remote sensing fiber optics. This spectrometer has the capability of acquiring toluene/acetonitrile spectra with 800 cm(-1) spectral coverage, 6.5 cm(-1) spectral resolution and 54 S/N ratio in 10s using 280 mW incident laser powers. The 1135-1350 cm(-1) and 1560-1650 cm(-1) regions of the lignin spectrum can be used to distinguish among the three primary model lignin monomers: coumaric, ferulic and sinapic acids. Mixtures of the three model monomers and first derivative spectra or partial least squares analysis of the phenyl ring breathing modes around 1600 cm(-1) are used to determine sugarcane lignin monomer composition. Lignin extracted from sugarcane is shown to have a predominant dimethoxylated and monomethoxylated phenylpropanoid content with a lesser amount of non-methoxylated phenol, which is consistent with sugarcane's classification as a non-woody angiosperm. The location of the phenyl ring breathing mode peaks do not shift in ethanol, methanol, isopropanol, 1,4 dioxane or acetone.

Ethanol yields were 2.1 (P = 0.06) to 2.3 (P = 0.01) times higher in simultaneous saccharification and fermentation (SSF) reactions of microcrystalline cellulose when cellulase was physisorbed on silica nanoparticles compared to enzyme in solution. In SSF reactions, cellulose is hydrolyzed to glucose by cellulase while yeast simultaneously ferments glucose to ethanol. The 35 C temperature and the presence of ethanol in SSF reactions are not optimal conditions for cellulase. Immobilization onto solid supports can stabilize the enzyme and promote activity at non-optimum reaction conditions. Mock SSF reactions that did not contain yeast were used to measure saccharification products and identify the mechanism for the improved ethanol yield using immobilized cellulase. Cellulase adsorbed to 40 nm silica nanoparticles produced 1.6 times (P = 0.01) more glucose than cellulase in solution in 96 h at pH 4.8 and 35 C. There was no significant accumulation (

The effect of corn stover pretreatment on glucose quantitation in hydrolysate using Raman spectroscopy is evaluated. Dilute sulfuric-acid pretreatment results in a 20 mg mL(-1) glucose limit of detection in hydrolysate. Soaking in aqueous ammonia pretreatment produces a 4 mg mL(-1) limit of detection. Water, ethanol or hexane extraction of corn stover reduces the spectral background that limits glucose detection in dilute acid hydrolysate. Additionally, a Raman spectroscopy multi-peak fitting method is presented to simultaneously measure glucose and xylose concentration in hydrolysate. This method yields a 6.1% average relative standard error at total saccharide concentrations above 45 mg mL(-1). When only cellulase is present, glucose and xylose yield were measured by Raman spectroscopy to be 32 ± 4 and 7.0 ± 0.8 mg mL(-1), respectively. When both cellulase and hemicellulase were present, xylose yield increased to 18.0 ± 0.5 mg mL(-1). Enzymatic or colorimetric assays confirmed the validity of the Raman spectroscopy results.

Lignocellulosic biomass has been proposed as an option for reducing global dependence on nonrenewable energy sources, such as oil. Selection and development of biomass feedstocks that efficiently yield the maximum fuel or biomaterial requires the availability of reliable methods for compositional and structural characterization of plant material. Many standard methods for biomass analysis are laborious and slow, and employ a variety of harsh reagents requiring some degree of remediation. The use of simpler and more rapid spectroscopic methods has proved invaluable in analyzing biomass. In the twenty-first century, researchers have employed techniques such as Raman, mid-infrared, and near-infrared spectroscopy for a wide range of applications in endeavors to further understand biofuel feedstocks. While many methods remain time consuming and expensive, a growing interest in high-throughput spectroscopic techniques has provided faster and larger scale feedstock screening for desirable traits. This review seeks to provide an overview of both high-throughput techniques and those requiring longer analysis times but still providing abundant qualitative and quantitative data. While applications of these instrumental methods have been researched for decades, more recent developments will be discussed here.

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials.

Background: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). Results: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. Conclusion: Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks. © 2014 Lupoi et al.; licensee BioMed Central Ltd.