Identification of twelve frequently processed wood species from Ivorian forests by carpenters using near-infrared spectroscopy
Abstract
This study demonstrates the efficacy of near-infrared (NIR) spectroscopy combined with chemometric techniques for identifying twelve tropical wood species commonly processed in Ivorian carpentry workshops. Principal Component Analysis (PCA) was employed to explore spectral variability, revealing distinct groupings corresponding to differences in chemical composition, wood density, and anatomical characteristics. Although some overlap was observed between certain species, three major trends could be distinguished, indicating meaningful structural or chemical similarities within each group. Subsequently, a Partial Least Squares Discriminant Analysis (PLS-DA) model was developed using 864 spectral samples. The model achieved a classification accuracy of 97.8% with raw spectra, which increased to 98.96% after applying the first derivative as a preprocessing step. This improvement confirmed the utility of spectral preprocessing for enhancing signal separation and reducing scattering effects. Several species were classified with perfect accuracy, demonstrating highly distinctive spectral signatures. Others showed improved discrimination after preprocessing, suggesting closer chemical similarities that were better resolved through data treatment. The strong overall performance confirms the robustness of this approach. The results affirm that NIR spectroscopy, when combined with appropriate chemometric tools, offers a reliable, rapid, and non-destructive method for tropical wood identification. This approach is particularly valuable in contexts requiring species-level discrimination, such as timber traceability, quality control, or combating illegal logging in biodiverse tropical regions.
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