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Defects on the surface of solid wood boards directly affect their mechanical properties and product grades. This study investigated the use of near-infrared spectroscopy (NIRS) to detect and classify defects on the surface of solid wood boards. Pinus koraiensis was selected as the raw material. The experiments focused on the ability to use the model to sort defects on the surface of wood into four types, namely live knots, dead knots, cracks, and defect-free. The test data consisted of 360 NIR absorption spectra of the defect samples using a portable NIR spectrometer, with the wavelength range of 900 to 1900 nm. Three pre-processing methods were used to compare the effects of noise elimination in the original absorption spectra. The NIR discrimination models were developed based on partial least squares and discriminant analysis (PLS-DA), least squares support vector machine (LS-SVM), and back-propagation neural network (BPNN) from 900 to approximately 1900 nm. The results demonstrated that the BPNN model exhibited the highest classification accuracy of 97.92% for the model calibration and 97.50% for the prediction set. These results suggest that there is potential for the NIR method to detect defects and differentiate between types of defects on the surface of solid wood boards.

Potential of Near-infrared Spectroscopy to Detect Defects on the Surface of Solid Wood Boards