Performance of visible and Near-infrared spectroscopy to predict the energetic properties of wood

Wood can be used for fuel by direct burning, or as a raw material for other fuels; however, it is necessary to evaluate the energy properties to ensure the optimal use of this material. The most relevant characteristics to be analyzed are the higher heating value, volatile material content, fixed carbon content, and ash content. Along with the traditional methods, there are also non-destructive evaluations that are optimized for speed and reliability. Among these methods, visible spectroscopy and near-infrared spectroscopy have been proven to be robust for the prediction of several wood properties. The aim of this study was to evaluate the potential of visible spectroscopy and near-infrared spectroscopy for species discrimination and prediction of higher heating value, volatile material content, fixed carbon content, and ash content for Eucalyptus saligna, Eucalyptus dunnii, and Eucalyptus benthamii woods. For this purpose, multivariate principal component analysis and partial least squares regression were applied to the collected spectra. The principal component analysis satisfactorily discriminated the three species, explaining 99% of the variance of the visible spectroscopy spectra and 73% of that of the near-infrared spectra. The estimation of energetic properties through partial least squares regression was satisfactory for both visible spectroscopy and near-infrared spectroscopies, which presented calibration R² values close to 1 and low errors for all properties studied.