A specific case in the classification of woods by FTIR and chemometric: discrimination of Fagales from Malpighiales

Fourier transform infrared (FTIR) spectroscopic data was used to classify wood samples from nine species within the Fagales and Malpighiales using a range of multivariate statistical methods. Taxonomic classification of the family Fagaceae and Betulaceae from Angiosperm Phylogenetic System Classification (APG II System) was successfully performed using supervised pattern recognition techniques. A methodology for wood sample discrimination was developed using both sapwood and heartwood samples. Ten and eight biomarkers emerged from the dataset to discriminate order and family, respectively. In the species studied FTIR in combination with multivariate analysis highlighted significant chemical differences in hemicelluloses, cellulose and guaiacyl (lignin) and shows promise as a suitable approach for wood sample classification

Application of chemometric analysis to infrared spectroscopy for the identification of wood origin

Chemical characteristics of wood are used in this study for plant taxonomy classification based on the current Angiosperm Phylogeny Group classification (APG III System) for the division, class and subclass of woody plants. Infrared spectra contain information about the molecular structure and intermolecular interactions among the components in wood but the understanding of this information requires multivariate techniques for the analysis of highly dense datasets. This article is written with the purposes of specifying the chemical differences among taxonomic groups, and predicting the taxa of unknown samples with a mathematical model. Principal component analysis, t-test, stepwise discriminant analysis and linear discriminant analysis, were some of the chosen multivariate techniques. A procedure to determine the division, class, subclass and order of unknown samples was built with promising implications for future applications of Fourier Transform Infrared spectroscopy in wood taxonomy classification

Analytical methods for lignocellulosic biomass structural polysaccharides

The use of lignocellulosic biomass has been postulated as a potential pathway toward diminishing global dependence on nonrenewable sources of chemicals and fuels. Before a specific feedstock can be selected for biochemical conversion into biofuels and bio-based chemicals, it must first be characterized to evaluate the chemical composition of the cell walls. Polysaccharides, specifically cellulose and hemicellulose, are often the focal point of these appraisals, since these constituents are the dominant substrates converted into monomeric sugars like glucose and xylose. These monosaccharides can be transformed, using microorganisms like yeast, into substances such as ethanol. Plant species containing abundant polysaccharides are highly desirable, as higher quantities of sugars should translate into larger end-product yields. Given the vast pool of potential feedstocks, qualitative and quantitative analytical methods are needed to assess cell wall polysaccharides. Many of these tools, such as wet chemical and chromatographic techniques, have been ubiquitously used for some time. Shortcomings in these analyses, however, prevent their usage in screening large sample sets for quintessential, high-yield, fuel-producing traits. This chapter briefly summarizes how analytical spectroscopy can lessen some of these limitations and how it has been utilized for polysaccharide analysis