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Although visible and near-infrared (Vis-NIR) spectroscopy can rapidly and nondestructively identify wood species, the conventional spectrometer approach relies on the aggregate light absorption due to the chemical composition of wood and light scattering originating from the physical structure of wood. Hence, much of the work in this area is still limited to further spectral pretreatments, such as baseline correction and standard normal variate to reduce the light scattering effects. However, it should be emphasized that the light scattering rather than absorption in wood is dominant, and this must be effectively utilized to achieve highly accurate and robust wood classification. Here a novel method based on spatially resolved diffuse reflectance (wavelength range: 600–1000 nm) was demonstrated to classify 15 kinds of wood. A portable Vis-NIR spectral measurement system was designed according to previous simulations and experimental results. To simplify spectral data analysis (i.e., against overfitting), support vector machine (SVM) model was constructed for wood sample classification using principal component analysis (PCA) scores. The classification accuracies of 98.6% for five-fold cross-validation and 91.2% for test set validation were achieved. This study offers enhanced classification accuracy and robustness over other conventional nondestructive approaches for such various kinds of wood and sheds light on utilizing visible and short-wave NIR light scattering for wood classification.

Demonstration of the applicability of visible and near-infrared spatially resolved spectroscopy for rapid and nondestructive wood classification