Efficiency of near-infrared spectroscopy in classifying Amazonian wood wastes for bioenergy generation

Finding methods to classify heterogeneous logging wastes from sustainable forest management in the Brazilian Amazonia is essential to increase the production and quality of charcoal. This study proposes a method to classify logging wastes of 12 Amazon hardwoods based on near-infrared (NIR) spectroscopy. The traits evaluated were basic density (BAD) and wet basis moisture content (MCwb). The spectral signatures obtained from the radial and transverse surfaces of the wood samples were submitted to principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). Spectral data measured on the radial surface of the wood yielded clearer clusters in the PCA score graph, considering the five BAD classes (very low, low, medium, high, and very high). The most promising PLS-DA model for wood classification based on BAD classes was calibrated with the radial surface spectra treated by the first derivative and validated in an independent lot with 97.9% correct classifi-cations. A few incorrect classifications of low-density wood occurred. Still, NIR spectroscopy combined with multivariate statistics proved to be a reliable and fast tool to distinguish the wood from branches of native Amazonian species concerning BAD. It will enable more rationality and sustainability in using these natural resources for bioenergy purposes.

Automated summary

This study proposes a method to classify logging wastes of 12 Amazon hardwoods based on near-infrared spectroscopy. The results show that near-infrared spectroscopy combined with multivariate statistics is a reliable and fast tool for wood classification. The method achieved high accuracy in distinguishing different wood classes based on basic density and moisture content.