Comparing modelling performance of chemometric methods for wood discrimination by near infrared spectroscopy

Comparative wood anatomy is the most accepted (traditional) method for wood identification. However, there is an ongoing search for an effective method where traditional methods may be insufficient in distinguishing on the species level. Near-infrared spectroscopy (NIRS) is one of the developing methods for wood identification. Near-infrared data of Scots pine, black pine, sessile oak and Hungarian oak were collected and examined in the spectral range of 12,000-4000 cm(-1) with a resolution of 4 cm(-1). Data were analyzed by partial least squares discriminate analysis (PLS-DA), decision trees (DT), artificial neural networks (ANN) and support vector machines (SVM). Raw data were subjected to multiple scatter correction (MSC), standard normal variate (SNV), Savitzky-Golay for derivatives (first [FD], second [SD]) and smoothing (Sm) and combinations of these preprocessing methods (Sm + FD, Sm + SD, FD + MSC, FD + SNV). Model performance compared through test accuracies. Accuracies varied between 99-100%, 76-98% and 73-96%, for genus level, oak and pine species, respectively. PLS-DA and SVM were found the most successful models. This study revealed that it is possible to discriminate Scots pine from black pine, and sessile oak from Hungarian oak by near-infrared spectroscopy and multivariate data analysis.