VIS-NIR hyperspectral imaging and multivariate analysis for direct characterization of pelagic fish species.

The identification of fish species and their physical and chemical characterization play a crucial role in the fishing industry, fish-food research and the management of marine resources. Traditional methods for species identification, such as expert observation, DNA barcoding and meta-barcoding, though effective, require labor-intensive laboratory work. Consequently, there is a pressing need for more objective and efficient methodologies for accurate fish species identification and characterization.

Application of segmented analysis via multivariate curve resolution with alternating least squares to H-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 H NMR spectroscopy applying a segmented analysis by means of multivariate curve resolution-alternating least squares (MCR-ALS).

Effects on Lignin Redistribution in 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 pre-treated by steam explosion under different degrees of severity (S), in order to evaluate their effect on cellulose accessibility by enzymatic hydrolysis. Approximately 87.7% to 98.

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.

Evaluation of the microscopic distribution of florfenicol in feed pellets for salmon by Fourier Transform infrared imaging and multivariate analysis.

Fourier Transform infrared imaging and multivariate analysis were used to identify, at the microscopic level, the presence of florfenicol (FF), a heavily-used antibiotic in the salmon industry, supplied to fishes in feed pellets for the treatment of salmonid rickettsial septicemia (SRS). The FF distribution was evaluated using Principal Component Analysis (PCA) and Augmented Multivariate Curve Resolution with Alternating Least Squares (augmented MCR-ALS) on the spectra obtained from images with pixel sizes of 6.25 μm × 6.

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.

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.

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.