Species- and Moisture-based Sorting of Green Timber Mix with Near Infrared Spectroscopy

Zhu Zhou, Sohrab Rahimi, Stavros Avramidis, Yiming Fang


Methods suitable for the determination and classification of green timber mix (western hemlock and amabilis fir), with respect to species and moisture content, were developed and tested using near infrared spectroscopy and chemometrics. One thousand two hundred samples were distributed into a calibration set (720 samples) and a prediction set (480 samples). Partial least squares (PLS) and least squares-support vector machines (LS-SVM) for both regression (PLSR and LS-SVR) and classification (PLS-DA and LS-SVC) with different spectral preprocessing methods were implemented. LS-SVM outperformed PLS models for both regression and classification. The coefficient of determination (R2p) and root mean square error (RMSEP) of prediction for the best LS-SVR model with spectra pretreated by smooth and first derivative were 0.9824 and 8.7%, respectively, for wood moisture content prediction in the range of 30% to 253%. The best classification model was LS-SVC with spectra pretreated by smooth and second derivative, with overall accuracies of 99.8% in the prediction set, when the samples were divided into four classes. NIRS combined with LS-SVM can be used as a rapid alternative method for qualitative and quantitative analysis of green hem-fir mix before kiln drying. The results could be helpful for sorting green hem-fir mixes with an on-line application.


Green timber; Least squares-support vector machines (LS-SVM); Moisture; Near infrared spectroscopy (NIRS); Sorting; Species

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