Application of segmented analysis via multivariate curve resolution with alternating least squares to 1H-nuclear magnetic resonance spectroscopy to identify different sugar sources.

The different types of sugar employed in the food industry exhibit chemical similarity and are mostly dominated by sucrose. Owing to the sugar origin of and differences in production, the presence of certain minor organic compounds differs. To differentiate between sugars based on their botanical source, geographical origin, or storage conditions, commercial brown sugars and sugar beet extracts were analyzed by 1H NMR spectroscopy applying a segmented analysis by means of multivariate curve resolution-alternating least squares (MCR-ALS). Principal component analysis and partial least squares-discriminant analysis yielded excellent differentiation between sugars from different sources after the application of this preprocessing strategy; without loss of chemical information and with direct interpretation of the results. By applying a segmented analysis via MCR-ALS to 1H NMR sugar data, similar spectroscopic profiles could be differentiated. This improved the selectivity of 1H NMR spectroscopy for sugar source differentiation which can be useful for industrial sugar authentication purposes.

Effects on Lignin Redistribution in Eucalyptus globulus Fibres Pre-Treated by Steam Explosion: A Microscale Study to Cellulose Accessibility.

The objective of this study was to investigate structural changes and lignin redistribution in Eucalyptus globulus pre-treated by steam explosion under different degrees of severity (S0), in order to evaluate their effect on cellulose accessibility by enzymatic hydrolysis. Approximately 87.7% to 98.5% of original glucans were retained in the pre-treated material. Glucose yields after the enzymatic hydrolysis of pre-treated material improved from 19.4% to 85.1% when S0 was increased from 8.53 to 10.42. One of the main reasons for the increase in glucose yield was the redistribution of lignin as micro-particles were deposited on the surface and interior of the fibre cell wall. This information was confirmed by laser scanning confocal fluorescence and FT-IR imaging; these microscopic techniques show changes in the physical and chemical characteristics of pre-treated fibres. In addition, the results allowed the construction of an explanatory model for microscale understanding of the enzymatic accessibility mechanism in the pre-treated lignocellulose.

Fourier-transform infrared imaging and multivariate analysis for direct identification of principal polysaccharides in brown seaweeds.

The current hydrocolloid industry requires new techniques for biomass characterization, which can quickly and ecologically characterize contained sugars. This work proposes the use of Fourier Transform Infrared microspectroscopy in combination with multivariate methods, to localize and identify the main carbohydrates and other components present in fresh brown seaweeds, avoiding time-consuming samples pre-treatments. Infrared images of Macrocystis pyrifera samples were analyzed by Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) and Principal Component Analysis (PCA) as chemometrics techniques to identify the compounds. MCR-ALS was the best strategy, delivering pure spectra of chemical compound that PCA did not. The carbohydrates identified by this method were 1-3-ß-glucans divided into endofibers and laminarin; two types of fucoidans (rich in fucose or mannuronic acid), alginate and mannitol, besides other compounds such as proteins. This technique represents an opportunity for the hydrocolloid industry for a modern, rapid and environmentally-friendly characterization of macroalgal biomass to enhance its use.

Chemical Characterization and Determination of the Anti-Oxidant Capacity of Two Brown Algae with Respect to Sampling Season and Morphological Structures Using Infrared Spectroscopy and Multivariate Analyses.

Brown algae biomass has been shown to be a highly important industrial source for the production of alginates and different nutraceutical products. The characterization of this biomass is necessary in order to allocate its use to specific applications according to the chemical and biological characteristics of this highly variable resource. The methods commonly used for algae characterization require a long time for the analysis and rigorous pretreatments of samples. In this work, nondestructive and fast analyses of different morphological structures from Lessonia spicata and Macrocystis pyrifera, which were collected during different seasons, were performed using Fourier transform infrared (FT-IR) techniques in combination with chemometric methods. Mid-infrared (IR) and near-infrared (NIR) spectral ranges were tested to evaluate the spectral differences between the species, seasons, and morphological structures of algae using a principal component analysis (PCA). Quantitative analyses of the polyphenol and alginate contents and the anti-oxidant capacity of the samples were performed using partial least squares (PLS) with both spectral ranges in order to build a predictive model for the rapid quantification of these parameters with industrial purposes. The PCA mainly showed differences in the samples based on seasonal sampling, where changes were observed in the bands corresponding to polysaccharides, proteins, and lipids. The obtained PLS models had high correlation coefficients (r) for the polyphenol content and anti-oxidant capacity (r > 0.9) and lower values for the alginate determination (0.7 < r < 0.8). Fourier transform infrared-based techniques were suitable tools for the rapid characterization of algae biomass, in which high variability in the samples was incorporated for the qualitative and quantitative analyses, and have the potential to be used on an industrial scale.

Fourier transform infrared imaging and microscopy studies of Pinus radiata pulps regarding the simultaneous saccharification and fermentation process.

The distribution and chemical patterns of lignocellulosic components at microscopic scale and their effect on the simultaneous saccharification and fermentation process (SSF) in the production of bioethanol from Pinus radiata pulps were analyzed by the application of diverse microscopical techniques, including scanning electronic microscopy (SEM), confocal laser scanning microscopy (CLSM) and attenuated total reflectance (ATR) - Fourier transform infrared microspectroscopy. This last technique was accompanied with multivariate methods, including principal component analysis (PCA) and multivariate curve resolution with alternating least squares (MCR-ALS) to evaluate the distribution patterns and to generate pure spectra of the lignocellulosic components of fibers. The results indicate that the information obtained by the techniques is complementary (ultrastructure, confocality and chemical characterization) and that the distribution of components affects the SSF yield, identifying lignin coalescence droplets as a characteristic factor to increase the SSF yield. Therefore, multivariate analysis of the infrared spectra enabled the in situ identification of the cellulose, lignin and lignin-carbohydrates arrangements. These techniques could be used to investigate the lignocellulosic components distribution and consequently their recalcitrance in many applications where minimal sample manipulation and microscale chemical information is required.

Infrared spectroscopy as alternative to wet chemical analysis to characterize Eucalyptus globulus pulps and predict their ethanol yield for a simultaneous saccharification and fermentation process.

Bioethanol can be obtained from wood by simultaneous enzymatic saccharification and fermentation step (SSF). However, for enzymatic process to be effective, a pretreatment is needed to break the wood structure and to remove lignin to expose the carbohydrates components. Evaluation of these processes requires characterization of the materials generated in the different stages. The traditional analytical methods of wood, pretreated materials (pulps), monosaccharides in the hydrolyzated pulps, and ethanol involve laborious and destructive methodologies. This, together with the high cost of enzymes and the possibility to obtain low ethanol yields from some pulps, makes it suitable to have rapid, nondestructive, less expensive, and quantitative methods to monitoring the processes to obtain ethanol from wood. In this work, infrared spectroscopy (IR) accompanied with multivariate analysis is used to characterize chemically organosolv pretreated Eucalyptus globulus pulps (glucans, lignin, and hemicellulosic sugars), as well as to predict the ethanol yield after a SSF process. Mid (4,000-400 cm(-1)) and near-infrared (12,500-4,000 cm(-1)) spectra of pulps were used in order to obtain calibration models through of partial least squares regression (PLS). The obtained multivariate models were validated by cross validation and by external validation. Mid-infrared (mid-IR)/NIR PLS models to quantify ethanol concentration were also compared with a mathematical approach to predict ethanol yield estimated from the chemical composition of the pulps determined by wet chemical methods (discrete chemical data). Results show the high ability of the infrared spectra in both regions, mid-IR and NIR, to calibrate and predict the ethanol yield and the chemical components of pulps, with low values of standard calibration and validation errors (root mean square error of calibration, root mean square error of validation (RMSEV), and root mean square error of prediction), high correlation between predicted and measured by the reference methods values (R (2) between 0.789 and 0.997), and adequate values of the ratio between the standard deviation of the reference methods and the standard errors of infrared PLS models relative performance determinant (RPD) (greater than 3 for majority of the models). Use of IR for ethanol quantification showed similar and even better results to the obtained with the discrete chemical data, especially in the case of mid-IR models, where ethanol concentration can be estimated with a RMSEV equal to 1.9 g L(-1). These results could facilitate the analysis of high number of samples required in the evaluation and optimization of the processes.