Discriminant analysis of wood-based materials using near-infrared spectroscopy

Abstract

 This study deals with the suitable discriminant techniques of wood-based materials by means of near-infrared spectroscopy (NIRS) and several chemometric analyses. The concept of Mahalanobis' generalized distance, K nearest neighbors (KNN), and soft independent modeling of class analogy (SIMCA) were evaluated to determine the best analytical procedure. The difference in the accuracy of classification with the spectrophotometer, the wavelength range as the explanatory variables, and the light-exposure condition of the sample were examined in detail. It was difficult to apply Mahalanobis' generalized distances to the classification of wood-based materials where NIR spectra varied widely within the sample category. The performance of KNN in the NIR region (800–2500 nm), for which the device used in the laboratory was employed, exhibited a high rate of correct answers of validation (>98%) independent of the light-exposure conditions of the sample. When employing the device used in the field, both KNN and SIMCA revealed correct answers of validation (>88%) at wavelengths of 550–1010 nm. These results suggest the applicability of NIRS to a reasonable classification of used wood at the factory and at job sites.