Application of MTF-SKNet for Wood Species Classification Using Mid-Infrared Spectroscopy

Abstract

With the recovery of the economy and growth of living standards, the demand for wood furniture is increasing, leading to a focus on wood quality and market value. Mid-infrared (MIR) spectroscopy, which characterizes molecular vibrations, is well-suited for wood classification due to its ability to identify molecular structures. This study utilizes a Fourier Transform Infrared (FTIR) spectrometer to classify 31 wood species based on their commercial categories. While the basic composition of wood species is similar, spectral data are overall close, necessitating a robust approach for accurate identification. To address this, a two-dimensional transformation of the spectral data is performed, to convert wavenumber sequence and state transition probabilities (quantized intensity levels) of spectra into a matrix, followed by deep learning classification using the transformed data. This resulted in the development of the MTF-SKNet model, achieving a classification accuracy of 93% for wood species. The model demonstrated strong generalization performance, reaching 96% accuracy in classifying the rosewood category of woods.