Wood density is a key indicator for tree functionality and end utilization. Appropriate chemometric methods play an important role in the successful prediction of wood density by visible and near infrared (Vis-NIR) spectroscopy. The objective of this study was to select appropriate pre-processing, variable selection and multivariate calibration techniques to improve the prediction accuracy of density in Chinese white poplar (Populus tomentosa carriere) wood. The Vis-NIR spectra were de-noised using four methods (lifting wavelet transform, LWT; wavelet transform, WT; multiplicative scatter correction, MSC; and standard normal variate, SNV), and four variable selection techniques, including successive projections algorithm (SPA), uninformative variables elimination (UVE), competitive adaptive reweighted sampling (CARS) and iteratively retains informative variables (IRIV), were compared to simplify the dimension of the high-dimensional spectral matrix. The non-linear models of generalized regression neural network (GRNN) and support vector machine (SVM) were performed using these selected variables. The results showed that the best prediction was obtained by GRNN models combined with the LWT and CARS method for Chinese white poplar wood density ($R_p^2$ = 0.870; RMSEP = 13 Kg/m³; ${\mathrm{RPD}}_p$ = 2.774).

The variation of wood properties between different geographical origin and tree species has an important influence on end use applications. This study aimed to investigate the feasibility of wood origin and species classification based on visible and near infrared spectroscopy and chemometric methods. The influence of geographical origin on tree species identification also was analyzed. A total of 530 samples with 2 origins and 5 tree species were collected for analysis. The raw reflectance spectra were preprocessed by spectral transformation technique, and nonlinear discrimination models were built by support vector machine (SVM) using various spectral forms. Three algorithms—grid search (GS), genetic algorithm (GA), and particle swarm optimization (PSO)—were applied to optimize the parameters of SVM models, respectively. Regardless of spectral forms and optimization techniques, the prediction accuracy was lower than that of the calibration set for wood origin and tree species identification. Except for reflectance spectra, prediction accuracy of 100 percent was obtained based on SVM in combination with three algorithms for origin discrimination. However, SVM in combination with reflectance spectra and GS technique achieved the best prediction accuracy (93.18%) for tree species identification. These results demonstrated that visible and near infrared spectroscopy combined with chemometric techniques can be used for geographical origin and tree species determination.