Near-Infrared Spectroscopy and Machine Learning for Wood Species Discrimination in an Amazon Floodplain Forest Management Area

Abstract

This study analyzes near-infrared (NIR) spectral characteristics of the wood of Hevea spruceana (Benth.) Müll. Arg., Hura crepitans L., Ocotea cymbarum Kunth, and Pseudobombax munguba (Mart.) Dugand from an Amazon floodplain forest area located in the Mamirauá Sustainable Development Reserve, aiming at their discrimination using artificial intelligence. The samples were collected as increment cores, from which NIR spectra were randomly collected in the transversal anatomical surface and compared. Principal component analysis (PCA) was applied to explore variation patterns in the data. Additionally, the classifier support vector machine algorithm, partial least squares–discriminant analysis (PLS-DA), and k-nearest neighbors regression were used to evaluate the accuracy in distinguishing the woods based on the NIR data. The results indicate similar spectral behavior among the species, with differences in absorbance intensities. PCA revealed a greater tendency for samples of the same species to cluster together, with Ocotea cymbarum showing the highest tendency for grouping. Among the classifiers, PLS-DA achieved the highest accuracy (98%). We can conclude that NIR spectroscopy combined with artificial intelligence classifiers has the potential to distinct wood species from the Amazon floodplain forest analyzed.