Wood-waste classification using interpretable machine learning

Abstract

Determining and classifying the contents of wood wastes, which possess an extremely heterogeneous structure, is crucial for their optimal utilization. The heterogeneous nature of waste wood poses significant challenges for its separation and classification. Also, the classical chemometric methods are not ergonomic in terms of time and cost. For this reason, this study aimed to develop an efficient ML-based decision support system (DSS) for accurate classification of waste wood in addition to being an ergonomic approach versus chemometric methods. To develop ML-based DSS, firstly absorbance values at 650–4000 cm−1 wavelength were measured using FTIR-ATR device for 200 wood-waste samples. Absorbance values at 52 key wavelengths, identified as the most effective for characterizing the samples, were used as input features to estimate ML classification models using artificial neural networks (ANN), random forest (RF), ensemble learning, multiple logistic regression (MLR), discriminant analysis, Naive Bayes, K-nearest neighbour (KNN), and support vector machines (SVM). The analysis results indicated that the developed DSS achieved quite a high classification accuracy rates, approaching 100%. As a result, this kind of ML-based DSS can be used as a practical and efficient tool to determine and classify the contents of wood wastes. © 2025 Informa UK Limited, trading as Taylor & Francis Group.