Using FTIR spectroscopy to model alkaline pretreatment and enzymatic saccharification of six lignocellulosic biomasses

Quantifying the Abundance of Alkane Moieties in Lignins with FTIR Spectroscopy and PLS Regression; Estimating Grafting Degree of Esterification

As society is rapidly converting from fossil-based materials to greener alternatives, the valorization of lignin through chemical modification has been given considerable attention. Characterizing this highly heterogeneous biopolymer is a constant challenge, and an emerging strategy for dealing with variations in material characteristics is combining traditional analytical techniques with chemometrics, such as Fourier-transform infrared (FTIR) spectroscopy with partial least squares regression (PLSR). Here, a calibration data set was built based on FTIR spectra and the total carbon-hydrogen bond (CHB) content of mixtures of technical lignins and alkanes, meant to emulate esterified samples. From this data, a PLSR model was built which predicted the CHB content of esterified lignin reaction products with an RMSECV=5.685 mmol/g and RMSEPred=5.827 mmol/g, and from which the weight percentage of ester-to-lignin was determined. When compared to wet-chemical analysis, good agreement between the techniques was found with an obtained RMSEPred=8.3 % and a R2 Train=0.9752 for the degree of esterification. This indicates high model predictability and goodness of fit, and that the calibration data set successfully emulated esterified lignin samples.

Isolation of delignifying bacteria and optimization of microbial pretreatment of biomass for bioenergy

Microbial pretreatment of lignocellulosic biomass holds significant promise for environmentally friendly biofuel production, offering an alternative to fossil fuels. This study focused on the isolation and characterization of two novel delignifying bacteria, GIET1 and GIET2, to enhance cellulose accessibility by lignin degradation. Molecular characterization confirmed their genetic identities, providing valuable microbial resources for biofuel production. Our results revealed distinct preferences for temperature, pH, and incubation period for the two bacteria. Bacillus haynesii exhibited optimal performance under moderate conditions and shorter incubation period, making it suitable for rice straw and sugarcane bagasse pretreatment. In contrast, Paenibacillus alvei thrived at higher temperatures and slightly alkaline pH, requiring a longer incubation period ideal for corn stalk pretreatment. These strain-specific requirements highlight the importance of tailoring pretreatment conditions to specific feedstocks. Structural, chemical, and morphological analyses demonstrated that microbial pretreatment reduced the amorphous lignin, increasing cellulose crystallinity and accessibility. These findings underscore the potential of microbial pretreatment to enhance biofuel production by modifying the lignocellulosic biomass. Such environmentally friendly bioconversion processes offer sustainable and cleaner energy solutions. Further research to optimize these methods for scalability and broader application is necessary in the pursuit for more efficient and greener biofuel production.

Microfluidic reactor designed for time-lapsed imaging of pretreatment and enzymatic hydrolysis of lignocellulosic biomass

The effect of tissue-specific biochemical heterogeneities of lignocellulosic biomass on biomass deconstruction is best understood through confocal laser scanning microscopy (CLSM) combined with immunohistochemistry. However, this process can be challenging, given the fragility of plant materials, and is generally not able to observe changes in the same section of biomass during both pretreatment and enzymatic hydrolysis. To overcome this challenge, a custom polydimethylsiloxane (PDMS) microfluidic imaging reactor was constructed using standard photolithographic techniques. As proof of concept, CLSM was performed on 60 μm-thick corn stem sections during pretreatment and enzymatic hydrolysis using the imaging reactor. Based on the fluorescence images, the less lignified parenchyma cell walls were more susceptible to pretreatment than the lignin-rich vascular bundles. During enzymatic hydrolysis, the highly lignified protoxylem cell wall was the most resistant, remaining unhydrolyzed even after 48 h. Therefore, imaging thin whole biomass sections was useful to obtain tissue-specific changes during biomass deconstruction.

Effect of alkaline pre-treatment on hydrolysis rate and methane production during anaerobic digestion of paunch solid waste

Paunch is comprised of the partially digested feed contained in cattle or sheep and contributes 20-50% of organic waste produced at red meat processing facilities. Anaerobic digestion has been identified as a promising technology for paunch treatment, however treatment times can be long and when combined with the moderate degradability of paunch this results in high treatment costs that need to be improved. Pre-treatment was investigated as a strategy to improve AD of paunch, alkaline treatment (NaOH or KOH) was selected due to the high lignin content. A range of alkaline loadings (1-20 g 100gTS-1) were tested with an equivalent hydroxide molar concentration of 9-250 mM [OH-]. Alkaline pre-treatment improved both the hydrolysis rate and the overall degradability of paunch solid by up to 4.4 times and 60%, respectively. The enhanced hydrolysis rate and methane yield was correlated to changes in material composition during pre-treatment. While alkaline concentration was an important factor, there were no significant improvements at alkaline concentrations above 12 g 100gTS-1 (150 mM [OH-]).

Isolation of Saccharibacillus WB17 strain from wheat bran phyllosphere and genomic insight into the cellulolytic and hemicellulolytic complex of the Saccharibacillus genus

The microorganisms living on the phyllosphere (the aerial part of the plants) are in contact with the lignocellulosic plant cell wall and might have a lignocellulolytic potential. We isolated a Saccharibacillus strain (Saccharibacillus WB17) from wheat bran phyllosphere and its cellulolytic and hemicellulolytic potential was investigated during growth onto wheat bran. Five other type strains from that genus selected from databases were also cultivated onto wheat bran and glucose. Studying the chemical composition of wheat bran residues by FTIR after growth of the six strains showed an important attack of the stretching C-O vibrations assigned to polysaccharides for all the strains, whereas the C = O bond/esterified carboxyl groups were not impacted. The genomic content of the strains showed that they harbored several CAZymes (comprised between 196 and 276) and possessed four of the fifth modules reflecting the presence of a high diversity of enzymes families. Xylanase and amylase activities were the most active enzymes with values reaching more than 4746 ± 1400 mIU/mg protein for the xylanase activity in case of Saccharibacillus deserti KCTC 33693 T and 452 ± 110 mIU/mg protein for the amylase activity of Saccharibacillus WB17. The total enzymatic activities obtained was not correlated to the total abundance of CAZyme along that genus. The Saccharibacillus strains harbor also some promising proteins in the GH30 and GH109 modules with potential arabinofuranosidase and oxidoreductase activities. Overall, the genus Saccharibacillus and more specifically the Saccharibacillus WB17 strain represent biological tools of interest for further biotechnological applications.

NIR robustness model of variable selection investigation of critical quality attributes coupled with different simulate noises by prediction capability and reproducibility

variable selection is critical to select characteristic variables of critical quality attributes to improve model performance and interpret the identified variables in multivariate calibration. However, classical variable selection methods were developed and optimized by the prediction error. It is rare for the robustness evaluation of variable selection methods. In this study, the robustness of four different variable selection methods was investigated by adding different types of simulate noises to validation set and calibration and validation sets, respectively. The reproducibility as well as root mean squared error of prediction (RMSEP) were used together as common measure in assessing the robustness of different variable selection methods. The robustness of four variable selection methods method was investigated using two near infrared (NIR) datasets including open-source dataset of corn and Chinese herbal medicine (CHM) dataset. The result illustrated that variable importance in projection (VIP) was substantially more robust to additive noise, with smaller RMSEP value and high reproducibility. This provides a novel strategy for the reliability evaluation of variable selection methods in NIR model of critical quality attributes.

Predicting biochar cation exchange capacity using Fourier transform infrared spectroscopy combined with partial least square regression

Determination of cation exchange capacity (CEC) in biochar by applying traditional wet methods is laborious, time-consuming, and generates chemical wastes. In this study, models were developed based on partial least square regression (PLSR) to predict CECs of biochars produced from a wide variety of feedstocks using Fourier transform infrared spectroscopy (FTIR). PLSR models used to predict CEC of biochars on weight (CEC-W) and carbon (CEC-C) basis were obtained from twenty-four biochars derived from several origins of feedstock, as well as compositions and mixtures, including four reference biochar samples. Biochars were grouped according to their CEC-W values (range of 4.0 to 150 cmol_c kg^-1) or CEC-C values (range of 6.0 to 312 cmol_c kg^-1). FTIR spectra highlighted features of the main functional groups responsible for biochar's CEC, which allowed a high prediction capacity for the PLSR models (R²_pred ~ 0.9). Regression coefficients were associated to spectral variables of the organic matrix polar functional groups that contributed positively and negatively for biochar CEC. Phenolic and carboxylic were the main functional groups contributing to a higher biochar CEC, while CH and CC groups decreased the density of negative charges on the charred matrices. Chemometric models were highly robust to estimate biochar CEC, mainly on a weight basis, in a fast, reliable and economic way, compared to CEC conventional laboratory methods.

Valorisation of wheat bran to produce natural pigments using selected microorganisms

Pigments are compounds with highly diverse structures and wide uses, which production is increasing worldwide. An eco-friendly method of bioproduction is to use the ability of some microorganisms to ferment on renewable carbon sources. Wheat bran (WB) is a cheap and abundant lignocellulosic co-product of low recalcitrance to biological conversion. Microbial candidates with theoretical ability to degrade WB were first preselected using specific databases. The microorganisms were Ashbya gossypii (producing riboflavin), Chitinophaga pinensis (producing flexirubin), Chromobacterium vaccinii (violacein) and Gordonia alkanivorans (carotenoids). Growth was shown for each on minimal salt medium supplemented with WB at 5 g.L^-1. Activities of the main enzymes consuming WB were measured, showing leucine amino-peptidase (up to 8.45 IU. mL^-1) and β-glucosidase activities (none to 6.44 IU. mL^-1). This was coupled to a FTIR (Fourier Transform Infra-Red) study of the WB residues that showed main degradation of the WB protein fraction for C. pinensis, C. vaccinii and G. alkanivorans. Production of the pigments on WB was assessed for all the strains except Ashbya, with values of production reaching up to 1.47 mg.L^-1. The polyphasic approach used in this study led to a proof of concept of pigment production from WB as a cheap carbon source.

Development of an on-line Raman spectral analytical method for monitoring and endpoint determination of the Cornu Caprae Hircus hydrolysis process

OBJECTIVES

Cornu Caprae Hircus (goat horn, GH), a medicinal animal horn, is frequently used in traditional Chinese medicine, and hydrolysis is one of the most important processes for GH pretreatment in pharmaceutical manufacturing. In this study, on-line Raman spectroscopy was applied to monitor the GH hydrolysis process by the development of partial least squares (PLS) calibration models for different groups of amino acids.

METHODS

Three steps were considered in model development. In the first step, design of experiments (DOE)-based preprocessing method selection was conducted. In the second step, the optimal spectral co-addition number was determined. In the third step, sample selection or reconstruction methods based on hierarchical clustering analysis (HCA) were used to extract or reconstruct representative calibration sets from the pool of hydrolysis process samples and investigated for their ability to improve model performance.

KEY FINDINGS

This study has shown the feasibility of using on-line Raman spectral analysis for monitoring the GH hydrolysis process based on the designed measurement system and appropriate model development steps.

CONCLUSIONS

The proposed Raman-based calibration models are expected to be used in GH hydrolysis process monitoring, leading to more rapid material information acquisition, deeper process understanding, more accurate endpoint determination and thus better product quality consistency.

Effects of the ultrasound-assisted pretreatments using borax and sodium hydroxide on the physicochemical properties of Chinese fir

This work investigated the physicochemical properties of Chinese fir after ultrasound-assisted pretreatments with borax and sodium hydroxide additives in an aqueous solution. TGA, FTIR, and XRD were used to analyze the thermal degradation processes, changes in chemical structures, and crystallinity of the treated samples, respectively. Additionally, the release of volatiles from wood pyrolysis was measured on-line by the TG-FTIR apparatus. In thermal analysis, all samples showed main degradation stages at 220-500 °C, and alkaline compounds could efficiently shift the process to lower temperatures with lower maximum weight loss rate (MWLR) and more residues. From TG-FTIR, it was observed that CO₂ was the primary gas product from pyrolysis in the alkaline-treated samples, while there were more carbonyl compounds released in the control and deionized water groups. Due to the destruction and removal of hemicellulose and lignin after alkaline treatments, the related peaks changed greatly. Changes in the ester groups caused by saponification also accounted for one of the most significant differences between samples. Moreover, except for the deionized water group without sonication, the crystallinity of the samples increased from 6.34% to 11.29%. Overall, comparing the samples treated with or without ultrasound, the results showed that the ultrasound treatment did influence the samples' physicochemical properties, and its' effects varied by the basicity of the solution. This in-depth investigation offers a better understanding of ultrasound-assisted and alkaline pretreatments of wood materials.

Thermal assisted alkaline pretreatment of rice husk for enhanced biomass deconstruction and enzymatic saccharification: Physico-chemical and structural characterization

Thermal assisted alkaline pretreatment (TAAP) of rice husk (RH) was investigated to facilitate enzymatic saccharification by enhancing the enzyme accessibility to cellulosic components. Statistically guided experiments based on the Box-Behnken design involving four factors viz. biomass loading, particle size, NaOH loading and reaction time was considered for optimization. The maximum sugar yield of 371 mg g^-1 biomass was obtained at optimized pretreatment condition [biomass loading (10% w/w), particle size (0.25-0.625 mm), NaOH loading (2% w/w), and reaction time (40 min)]. The TAAP of RH resulted in the efficient removal of lignin (14.9-54% (w/w)) with low hemicellulose solubilization [10.7-33.1% (w/w)] and with a simultaneous increase in cellulose concentration [32.65-51.65% (w/w)]. The SEM analysis indicated increased porosity and biomass disruption during TAAP. The FTIR analysis showed progressive removal of noncellulosic constituents, and XRD analysis revealed an increase in cellulose crystallinity post-TAAP indicating the effectiveness of pretreatment.

A novel biochar from Manihot esculenta Crantz waste: application for the removal of Malachite Green from wastewater and optimization of the adsorption process

The adsorptive removal of Malachite Green (MG) by a novel biochar namely Cassava Rind Carbon (CRC) was studied in a batch system. Moreover, Box-Behnken Response Surface Methodology was used to optimize operating conditions of the adsorption process. Characterization was done by Thermo Gravimetric Analysis (TGA), Attenuated Total Reflectance Fourier Transform Infra-Red Spectroscopy (ATR/FTIR), Brunauer-Emmett-Teller (BET) surface area, Scanning Electron Microscopy (SEM), X-Ray Diffraction (XRD) and pH zero charge point (pH_ZCP). The pseudo-second-order model and Langmuir model provided the best fit for kinetic and isotherm, respectively. The maximum capacity of dye adsorbed was 932.98 mg/g at 25 °C. The influence of temperature, the mass of adsorbent and the concentration of dye was studied. The optimal amount of adsorbed MG was 1,363.58 mg/g corresponding to 50 °C, 5 mg of CRC and 150 mg/L of dye. According to the high performance exhibited by CRC in this study, Manihot esculenta Crantz waste can be used as a better and low-cost biomass for wastewater decolourization.

Optimization of Parameter Selection for Partial Least Squares Model Development

In multivariate calibration using a spectral dataset, it is difficult to optimize nonsystematic parameters in a quantitative model, i.e., spectral pretreatment, latent factors and variable selection. In this study, we describe a novel and systematic approach that uses a processing trajectory to select three parameters including different spectral pretreatments, variable importance in the projection (VIP) for variable selection and latent factors in the Partial Least-Square (PLS) model. The root mean square errors of calibration (RMSEC), the root mean square errors of prediction (RMSEP), the ratio of standard error of prediction to standard deviation (RPD), and the determination coefficient of calibration (Rcal(2)) and validation (Rpre(2)) were simultaneously assessed to optimize the best modeling path. We used three different near-infrared (NIR) datasets, which illustrated that there was more than one modeling path to ensure good modeling. The PLS model optimizes modeling parameters step-by-step, but the robust model described here demonstrates better efficiency than other published papers.

High-level hemicellulosic arabinose predominately affects lignocellulose crystallinity for genetically enhancing both plant lodging resistance and biomass enzymatic digestibility in rice mutants

Rice is a major food crop with enormous biomass residue for biofuels. As plant cell wall recalcitrance basically decides a costly biomass process, genetic modification of plant cell walls has been regarded as a promising solution. However, due to structural complexity and functional diversity of plant cell walls, it becomes essential to identify the key factors of cell wall modifications that could not much alter plant growth, but cause an enhancement in biomass enzymatic digestibility. To address this issue, we performed systems biology analyses of a total of 36 distinct cell wall mutants of rice. As a result, cellulose crystallinity (CrI) was examined to be the key factor that negatively determines either the biomass enzymatic saccharification upon various chemical pretreatments or the plant lodging resistance, an integrated agronomic trait in plant growth and grain production. Notably, hemicellulosic arabinose (Ara) was detected to be the major factor that negatively affects cellulose CrI probably through its interlinking with β-1,4-glucans. In addition, lignin and G monomer also exhibited the positive impact on biomass digestion and lodging resistance. Further characterization of two elite mutants, Osfc17 and Osfc30, showing normal plant growth and high biomass enzymatic digestion in situ and in vitro, revealed the multiple GH9B candidate genes for reducing cellulose CrI and XAT genes for increasing hemicellulosic Ara level. Hence, the results have suggested the potential cell wall modifications for enhancing both biomass enzymatic digestibility and plant lodging resistance by synchronically overexpressing GH9B and XAT genes in rice.

Investigating lignin key features in maize lignocelluloses using infrared spectroscopy

Lignins and their cross-linking to hemicelluloses detrimentally affect the cellulose-to-ethanol conversion of grass lignocelluloses. Screening appropriate grass cell walls and their compositional changes during the various steps of the process calls for a high-throughput analytical technique. Such a performance can be fulfilled by Fourier transform mid-infrared (FT-MIR) spectroscopy. In the present paper, a set of maize cell walls from mature stems were selected, including brown midrib samples. Lignin fractions were isolated by mild acidolysis to obtain a set of purified maize lignin standards. The lignin content and the percentage of lignin-derived p-hydroxyphenyl (H), guaiacyl (G), and syringyl (S) thioacidolysis monomers were determined. In addition, the composition of cell wall polysaccharides, as well as the amount of ester-linked p-coumaric (CA) and ferulic (FA) acids, was measured by wet chemistry. Partial least square (PLS) analyses were applied to infrared and chemical data of cell walls. The resulting models showed a good predictive ability with regard to the lignin content, to the frequency of S (or G) thioacidolysis monomers, and to the level of ester-linked CA of maize cell walls. The loading plots and regression coefficients revealed relevant infrared absorption bands.

Fourier transform mid infrared spectroscopy applications for monitoring the structural plasticity of plant cell walls

Fourier transform mid-infrared (FT-MIR) spectroscopy has been extensively used as a potent, fast and non-destructive procedure for analyzing cell wall architectures, with the capacity to provide abundant information about their polymers, functional groups, and in muro entanglement. In conjunction with multivariate analyses, this method has proved to be a valuable tool for tracking alterations in cell walls. The present review examines recent progress in the use of FT-MIR spectroscopy to monitor cell wall changes occurring in muro as a result of various factors, such as growth and development processes, genetic modifications, exposition or habituation to cellulose biosynthesis inhibitors and responses to other abiotic or biotic stresses, as well as its biotechnological applications.

Rapid determination of bulk microalgal biochemical composition by Fourier-Transform Infrared spectroscopy

Analysis of bulk biochemical composition is a key in fundamental and applied studies of microalgae and is essential to understanding responses to different cultivation scenarios. Traditional biochemical methods for the quantification of lipids, carbohydrates and proteins are often time-consuming, often involve hazardous reagents, require significant amounts of biomass and are highly dependent on practitioner proficiency. This study presents a rapid and non-destructive method, utilising Fourier-Transform Infrared (FTIR) spectroscopy for the simultaneous determination of lipid, protein and carbohydrate content in microalgal biomass. A simple univariate regression was applied to sets of reference microalgal spectra of known composition and recognised IR peak integrals. A robust single-species model was constructed, with coefficients of determination r(2)>0.95, high predictive accuracy and relative errors below 5%. The applicability of this methodology is demonstrated by monitoring the time-resolved changes in biochemical composition of the marine alga Nannochloropsis sp. grown to nitrogen starvation.