Non-destructive determination of core-transition-outer wood of Pinus nigra combining FTIR spectroscopy and prediction models

Abstract

The early detection of wood quality by using advanced analytical non-destructive methods is an ongoing area of research and is highly interesting for the forest-based sector. The presence of core wood has negative effects on the performance of this material; hence, it is essential to identify this region as well as to define the endpoint where the region defined as transition wood begins. The purpose of this study was to apply a novel methodology for wood quality classification, based on FTIR spectroscopy, in combination with chemometrics, generating models capable of differentiating and predicting core wood, transition, and outer wood of Pinus nigra. This study also attempts to classify which specific IR bands define the chemical differentiation of woody regions. The results of the predictive models generated were satisfactory, attaining a full identification of classes with a non-linear SVM-DA model showing higher correlation coefficients than the generated linear SIMCA and PLS-DA models. SIMCA and PLS-DA were suitable for bands contrast and for categorizing the IR fingerprint in relation to the coretransition-outer regions. This study presents discriminative models generated from a non-destructive and relatively fast IR methodology with high correlation coefficients, thus improving the existing methods that are currently practiced for identifying wood quality. However, the database requires progressive calibration and adjustments to have acceptable reliability when validating the methods over time.