Classification and characterization of thermally modified timber using visible and near-infrared spectroscopy and artificial neural networks: a comparative study on the performance of different NDE methods and ANNs

Abstract

Visible and near-infrared (VIS–NIR) spectroscopy was used for classifying and predicting the properties of thermally modified Western hemlock wood. The specimens were treated at 170 °C, 212 °C, and 230 °C. The dimensional reduction was performed using linear discriminant analysis, and the resulted dataset was used for wood classification using the support vector machines and the linear vector quantization neural network. The VIS–NIR dataset was also used to predict the wood moisture content, swelling coefficient, water absorption, density, dynamic modulus of elasticity, and hardness. The “adaptive neuro-fuzzy inference system” (ANFIS), “Group Method of Data Handling” (GMDH), and “multilayer perceptron” (MLP) neural networks were employed for predicting the wood properties. It was shown that regardless of the type of the neural network, NIR dataset provided a robust model with 100% classification accuracy, which can be implemented in industrial scale for in-line timber quality control. The results indicated that the ANFIS and GMDH neural network showed higher performance than the MLP model for predicting the wood properties. While the VIS–NIR data resulted in a promising accuracy for predicting the wood moisture content and dimensional stability parameters, it did not seem suitable for the prediction of wood density and its mechanical properties. The performance of the VIS–NIR spectroscopy method for classification and characterization of heat-treated timber was compared with that obtained using the color measurement and the stress wave method detected by the acoustic emission sensor.