Rapid determination of sugar content in corn stover hydrolysates using near infrared spectroscopy

Addressing raw material variability: In-line FTIR sugar composition analysis of lignocellulosic process streams

For a sustainable economy, biorefineries that use second-generation feedstocks to produce biochemicals and biofuels are essential. However, the exact composition of renewable feedstocks depends on the natural raw materials used and is therefore highly variable. In this contribution, a process analytical technique (PAT) strategy for determining the sugar composition of lignocellulosic process streams in real-time to enable better control of bioprocesses is presented. An in-line mid-IR probe was used to acquire spectra of ultra-filtered spent sulfite liquor (UF-SSL). Independent partial least squares models were developed for the most abundant sugars in the UF-SSL. Up to 5 sugars were quantified simultaneously to determine the sugar concentration and composition of the UF-SSL. The lowest root mean square errors of the predicted values obtained per analyte were 1.02 g/L arabinose, 1.25 g/L galactose, 0.50 g/L glucose, 1.60 g/L mannose, and 0.85 g/L xylose. Equipped with this novel PAT tool, new bioprocessing strategies can be developed for UF-SSL.

Comprehensive approach of methods for microstructural analysis and analytical tools in lignocellulosic biomass assessment - A review

The trend in the modern world is to replace fossil fuels with green energy sources in order to reduce their environmental impact. The biorefinery industry, within this premise, needs to establish quantitative and qualitative analytical methods to better understand lignocellulosic biomass composition and structure. This paper presents chemical techniques (chromatography, thermal analysis, HRMS, FTIR, NIR, and NMR) and physicochemical techniques (XRD, optical and electron microscopy techniques - Confocal fluorescence, Raman, SPM, AFM, SEM, and TEM) for the microstructural characterization of lignocellulosic biomass and its derivatives. Each of these tools provides different and complementary information regarding molecular and microstructural composition of lignocellulosic biomass. Understanding these properties is essential for the design and operation of associated biomass conversion processing facilities. PAT, monitored in real-time, ensures an economical and balanced mass-energy process. This review aimed to help researchers select the most suitable analytical technique with which to investigate biomass feedstocks with recalcitrant natures.

Raman Calibration Models for Chemical Species Determination in CO2-Loaded Aqueous MEA Solutions Using PLS and ANN Techniques

The improvement in energy efficiency is recognized as one of the significant parameters for achieving our net-zero emissions target by 2050. One exciting area for development is conventional carbon capture technologies. Current amine absorption-based systems for carbon capture operate at suboptimal conditions resulting in an efficiency loss, causing a high operational expenditure. Knowledge of qualitative and quantitative speciation of CO2-loaded alkanolamine systems and their interactions can improve the equipment design and define optimal operating conditions. This work investigates the potential of Raman spectroscopy as an in situ monitoring tool for determining chemical species concentration in the CO2-loaded aqueous monoethanolamine (MEA) solutions. Experimental information on chemical speciation and vapour-liquid equilibrium was collected at a range of process parameters. Then, partial least squares (PLS) regression and an artificial neural network (ANN) were applied separately to develop two Raman species calibration models where the Kent–Eisenberg model correlated the species concentrations. The data were paired and randomly distributed into calibration and test datasets. A quantitative analysis based on the coefficient of determination (R2) and root mean squared error (RMSE) was performed to select the optimal model parameters for the PLS and ANN approach. The R2 values of above 0.90 are observed for both cases indicating that both regression techniques can satisfactorily predict species concentration. ANN models are slightly more accurate than PLS. However, PLS (being a white box model) allows the analysis of spectral variables using a weight plot.

Quali-quantitative monitoring of chemocatalytic cellulose conversion into lactic acid by FT-NIR spectroscopy

Chemocatalytic conversion of cellulose into lactic acid is a valuable alternative to simple sugar fermentation. Nevertheless, the procedures still need optimization to be translated to the industrial scale. Such translation would benefit by on-line monitoring of reaction parameters by fast, inexpensive, direct spectroscopic techniques. In this work, we propose the application of FT-NIR spectroscopy as a suitable analytical tool for monitoring the chemocatalytic conversion of cellulose into lactic acid. Comparison between different FT-NIR spectra at different reaction temperatures and times was exploited to qualitatively indicate the feasibility of the reaction. Besides, an FT-NIR prediction model was proposed for rapidly estimating the molar distribution of cellulose catalytic degradation components in the reaction mixtures. The calibration model was based on reference samples analysed by HPLC. The model was validated by an external validation set. Relevant statistical values of Ratio Performance to Deviations (RPD) referred to both calibration and external validation were obtained, thus demonstrating the potential of such analytical technique in process monitoring.

Rapid determination of total phenolic content of whole wheat flour using near-infrared spectroscopy and chemometrics

Phenolics in whole wheat products provide many health benefits. Wheat breeders, producers, and end-users are becoming increasingly interested in wheats with higher total phenolic content (TPC). Whole wheat flour with higher phenolics may have greater marketing value in the future. However, conventional methods determining TPC are costly and labor-intensive, which are not practical for wheat breeders to analyze several thousands of lines within a limited timeframe. We presented a novel application of near-infrared spectroscopy for TPC prediction in whole wheat flour. The optimal regression model demonstrated R² values of 0.92 and 0.90 for the calibration and validation sets, and a residual prediction deviation value of 3.4. The NIR method avoids the tedious extraction and TPC assay procedures, making it more convenient and cost-effective. Our result also demonstrated that NIR can accurately quantify phenolics even at low concentration (less than 0.2%) in the food matrix such as whole wheat flour.

A new high-throughput assay for determining soluble sugar in sorghum internode-extracted juice

A high-throughput method combining liquid handling system and 96-well microplate pipetting format was developed for total sugar determination. With this new method, we characterized diverse sugar accumulation in sorghum varieties. Sweet sorghum accumulates large amounts of sucrose in its stalk and, therefore, has emerged as one important bioenergy crop. The commonly used sugar measurement, Brix, limits the characterization of internode variation of the sugar concentrations due to its low throughput. Here we developed a low-cost, high-throughput method to determine profiles of total sugars in sorghum internodes with a liquid handling system-based sample preparation and a phenol-sulfuric acid assay in 96-well microplate format. The present method generates results highly correlated with commonly used Brix measurements (r = 0.922). The inter-assay coefficient of variation ranged from 4.8 to 7.6%. The present method can reliably estimate mixed sugars composed of 80% sucrose. We characterized the profiles of 35 sorghum accessions and identified 21 accessions with significantly different sugar concentrations between internodes either due to dried-up internodes or concentration differences. As a high-throughput alternative to Brix measurements, the new method makes it possible to phenotype total sugars from large numbers of internode samples and, therefore, will be useful for genetic and breeding purposes.

Rapid Determination of Acetic Acid, Furfural, and 5-Hydroxymethylfurfural in Biomass Hydrolysates Using Near-Infrared Spectroscopy

Near-infrared spectroscopy (NIRS) is a rapid detection technique that has been used to characterize biomass. The objective of this study was to develop suitable NIRS models to predict the acetic acid, furfural, and 5-hydroxymethylfurfural (HMF) contents in biomass hydrolysates. Using a uniform distribution of pretreatment conditions, 60 representative biomass hydrolysates were prepared. Partial least-squares regression was used to develop models capable of predicting acetic acid, furfural, and HMF contents. Optimal models were built using the wavenumber range of 9000-8000 and 7000-5000 cm^-1 with high R ² for calibration and validation models, small root-mean-square error of calibration (<0.21) and root-mean-square error of prediction (RMSEP, <0.42), and a ratio of the standard deviation of the reference values to the RMSEP of >2.7. The NIRS method largely reduced the analytical time from ∼55 to <1 min and has no cost for reagents.