Visible-Near Infrared Spectroscopy and Chemometric Methods for Wood Density Prediction and Origin/Species Identification

This study aimed to rapidly and accurately identify geographical origin, tree species, and model wood density using visible and near infrared (Vis-NIR) spectroscopy coupled with chemometric methods. A total of 280 samples with two origins (Jilin and Heilongjiang province, China), and three species, Dahurian larch (Larix gmelinii (Rupr.) Rupr.), Japanese elm (Ulmus davidiana Planch. var. japonica Nakai), and Chinese white poplar (Populus tomentosa carriere), were collected for classification and prediction analysis. The spectral data were de-noised using lifting wavelet transform (LWT) and linear and nonlinear models were built from the de-noised spectra using partial least squares (PLS) and particle swarm optimization (PSO)-support vector machine (SVM) methods, respectively. The response surface methodology (RSM) was applied to analyze the best combined parameters of PSO-SVM. The PSO-SVM model was employed for discrimination of origin and species. The identification accuracy for tree species using wavelet coefficients were better than models developed using raw spectra, and the accuracy of geographical origin and species was greater than 98% for the prediction dataset. The prediction accuracy of density using wavelet coefficients was better than that of constructed spectra. The PSO-SVM models optimized by RSM obtained the best results with coefficients of determination of the calibration set of 0.953, 0.974, 0.959, and 0.837 for Dahurian larch, Japanese elm, Chinese white poplar (Jilin), and Chinese white poplar (Heilongjiang), respectively. The results showed the feasibility of Vis-NIR spectroscopy coupled with chemometric methods for determining wood property and geographical origin with simple, rapid, and non-destructive advantages.