Determination of three Shorea species by near-infrared spectroscopy and anatomical structures Available to Purchase

Shorea, well known as Meranti, is one of the most important woods in Indonesia. However, identifying the species is still challenging, especially in the timber trade, to prevent misclassification and document falsification. Thus, a non-destructive method is required as a rapid and handy tool in the field. This study aimed to determine three species of Shorea, namely Shorea parvifolia, S. leprosula and S. bracteolata, originating from Indonesia (Sumatra and Kalimantan). The determination of the woods species was used Near-Infrared Spectroscopy (NIRS), and then validated by their anatomical characteristics. For NIRS, the multivariate analysis, namely Principal Component Analysis (PCA) and a pre-treatment Savitzky-Golay (SG) filter were used To collect the NIR spectral signal at wavenumbers of 8000-4000 cm⁻¹. Further, the data was classified using the k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classifiers. The results showed that the accuracy of both algorithms was approximately 67%, which is relatively low. k-NN was able to differentiate S. parvifolia with high accuracy but cannot separate S. leprosula and S. bracteolata. While SVM approaches better separation for S. leprosula and S. bracteolata, even though the matrix score was 50:50. The SVM is probably better at classifying because it does not require complicated data processing like k-NN. The observation of anatomical structures revealed differences between Shorea species. Crystals presented in S. parvifolia and S. leprosula; however, radial canals were observed only in S. leprosula. Crystals were absent in S. bracteolata, but silica bodies were found in rays parenchyma. The observation of microscopic structures can differentiate three species of Meranti wood up to the species level, but this method takes time and requires specific knowledge. Thus, NIRS is feasible as an alternative for rapid wood identification.