Prediction Methodology for Wood Compression Strength Based on Particle-Filtered Near-Infrared Spectroscopy

The measurement of wood mechanical properties is important for engineering design and applications. This study investigated near-infrared (NIR) spectroscopy coupled with particle filter (PF) and partial least-squares (PLS) methods to predict wood compression strength. Three structural timbers (Acer mono, birch, and toothed oak) were studied. The NIR spectra were collected from 900 to 1700 cm-1 and preprocessed by a standard normal variate transformation combined with Savitzky-Golay filtering. The prediction model coefficient matrix and standard variance were obtained by a PF iterative process, and their ratio was used to select the NIR feature wavelength points. A PLS prediction model based on NIR spectroscopy was established to predict the wood compression strength. Compared with the successive projection algorithm (SPA) and Kalman filtering (KF), the PF-PLS prediction model outperformed the other models in all three wood samples, resulting in a high correlation coefficient (r) of 0.89, 0.92, and 0.90, a low root-mean-square error of prediction (RMSEP) of 6.30, 10.60, and 9.71, and a fast average detection speed of 0.28 s, 0.46 s, and 0.33 s, respectively. The optimal PF selection can effectively reduce the redundant information of the NIR matrix and improve the accuracy and efficiency of the prediction model.