Application of near-infrared spectroscopy for predicting the wood density of teakwood stands by increment cores and thin wood surface sampling

Abstract

Density, as the most critical information for wood materials could be predicted by near-infrared (NIR) spectroscopy. Increment cores and thin wood samples of teakwood ( Tectona grandis) served as research materials for this new approach to sampling for NIR spectroscopy, explained in detail. Density data were combined with their NIR spectra data for analysis using cross-validation partial least squares regression as chemometrics to produce a density prediction model. A new approach of increment cores from teakwood stands for NIR spectroscopy measurements for density predictions resulting in a 0.34 coefficient of determination for cross-validation (R2CV) and 0.40 of R2CV for thin wood samples as the best result from the first derivative with 25 smoothing points of NIR spectra. The improvement from the previous research for density prediction accuracy resulted in an R2CV value of 0.55. The smaller diameter of the increment core sample than the NIR light beam's diameter and illuminated mixed surface (transversal and radial) are supposed to give weaker prediction accuracy by this new approach.