This review article examines past and current research on the application of near-infrared (NIR) reflectance/transmittance spectroscopy (NIRS) for real-time monitoring of moisture content and density of solid wood. Most of the applications of NIRS on solid wood have focussed on the application of multivariate statistics as exploratory tools for the prediction of physical, chemical and mechanical properties, such as moisture content, density, stiffness, cellulose and lignin content. However, very few studies on the development of optical models and the use of NIRS transmittance techniques on solid wood have been reported. NIRS technology has the potential to be used as a rapid tool that could be employed for at-line measurement and monitoring of wood properties in the forest products industry.Keywords: wood properties, near-infrared spectroscopy, multivariate statistics, optical model, transmittance, reflectance résumé Cet article de revue examine la recherche passée et actuelle sur l'application de la spectroscopie proche infra-rouge en mode réflectance/transmittance pour le suivi en temps réel de la teneur en eau et de la densité du bois solide. La plupart des applications de la spectroscopie proche infra-rouge sur le bois solide ont porté sur l'application des méthodes statistiques multi-variées pour l' estimation des propriétés physiques, chimiques et mécaniques, telles que la teneur en eau, la densité, la dureté, le contenu en cellulose et en lignine. Cependant, peu d' études ont porté sur le développement de modèles optiques et sur l'utilisation de la spectroscopie proche infra-rouge en mode transmittance pour le bois solide. La spectroscopie proche infra-rouge a le potentiel d' être utilisée comme un outil rapide de mesure et suivi en temps réel des propriétés du bois dans l'industrie des produits forestiers.
Portable sensors are required for rapid and nondestructive measurements of wood properties in the field to ensure optimal use of the fiber. We tested a handheld near-infrared (NIR) spectrometer to estimate moisture content (MC) and basic specific gravity (BSG) of quaking aspen (Populus tremuloides Michx.) and balsam poplar (Populus balsamifera L.) frozen and thawed logs. Partial least square (PLS) regression general models were developed to estimate MC and BSG by considering the influence of the following factors: log conditions (frozen and thawed wood), species, and types of wood (sapwood and heartwood). In addition, the influence of MC was also considered when estimating BSG. Including the two types of wood in a general model improved MC prediction (R 2 p ¼ 0.83, RMSE p = 11.1%) while including the two species improved BSG prediction (R 2 p ¼ 0.57, RMSE p = 0.022). Similar accuracies were obtained for BSG prediction from green (R 2 p ¼ 0.35, RMSE p = 0.027) and oven-dried wood (R 2 p ¼ 0.43, RMSE p = 0.027). PLS discriminant analysis was applied successfully to NIR spectra to sort the wood according to their MC, BSG, species, and wood type with overall accuracies of 86%, 65%, 98%, and 79%, respectively.
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