Selection of discriminant mid-infrared wavenumbers by combining a naïve Bayesian classifier and a genetic algorithm: Application to the evaluation of lignocellulosic biomass biodegradation

Mathematical Biosciences
Abbas RammalHassan Fenniri

Abstract

Infrared spectroscopy provides useful information on the molecular compositions of biological systems related to molecular vibrations, overtones, and combinations of fundamental vibrations. Mid-infrared (MIR) spectroscopy is sensitive to organic and mineral components and has attracted growing interest in the development of biomarkers related to intrinsic characteristics of lignocellulose biomass. However, not all spectral information is valuable for biomarker construction or for applying analysis methods such as classification. Better processing and interpretation can be achieved by identifying discriminating wavenumbers. The selection of wavenumbers has been addressed through several variable- or feature-selection methods. Some of them have not been adapted for use in large data sets or are difficult to tune, and others require additional information, such as concentrations. This paper proposes a new approach by combining a naïve Bayesian classifier with a genetic algorithm to identify discriminating spectral wavenumbers. The genetic algorithm uses a linear combination of an a posteriori probability and the Bayes error rate as the fitness function for optimization. Such a function allows the improvement of both the compactnes...Continue Reading

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