A comparative study on the performance of terahertz, near-infrared, and hyperspectral spectroscopy for wood identification

Wood species identification is of paramount significance in wood products manufacturing and applications. In contrast to traditional wood identification methods, spectroscopy-based technology offers a rapid, cost-effective, and efficient alternative. This study focuses on five wood species as experimental materials and aims to obtain three distinct wood spectra for each: near-infrared (NIR) spectra, hyperspectral image spectral information, and terahertz (THz) spectra. These spectra underwent pre-processing techniques such as Savitzky–Golay smoothing (SG), normalization, multiple scattering correction (MSC), and standard normalized variate (SNV), followed by dimensionality reduction through principal component analysis (PCA). Subsequently, the processed data were input into a partial least squares discriminant analysis (PLS-DA) for recognition. The results demonstrate the best recognition accuracy of 99.8% for THz spectra, 98.7% for NIR spectra, and 97.3% for hyperspectral image spectral information. The THz spectra exhibited the highest recognition accuracy, particularly with the SG-preprocessed THz spectra. These preprocessed spectra effectively removed noise and smoothed the spectral curves compared to the raw spectra.