Lignocellulose Determination and Categorization Analysis for Biofuel Pellets Based on FT-IR Spectra

Abstract

Lignocellulose determination and categorization analysis are critical to the treatment of biofuel pellets. For lignocellulose determination, partial least square (PLS) regression models based on full-range spectra, feature intervals, and feature bands were established. The models that were based on feature bands displayed the best performance, with the determination coefficients of 0.956, 0.864, and 0.926 for cellulose, hemicellulose, and lignin, respectively. For the categorization analysis issue, two specific cases were studied. First, linear discriminant analysis and random forest were used for biofuel pellet classification. Second, fuzzy clustering based on transitive closure was adopted to implement the hierarchical clustering of biofuel pellets without the label information. Excellent categorization results were obtained. This paper provides an effective auxiliary means for the pretreatment and storage of biofuel pellets with the utilization of Fourier transform infrared (FT-IR) spectra.