Mechanical properties of wood materials using near-infrared spectroscopy based on correlation local embedding and partial least-squares

Abstract

Abstract This study used near-infrared (NIR) spectroscopy to predict mechanical properties of wood. NIR spectra were collected in wavelengths 900–1700 nm, and spectra averaged by radial and tangential surface spectra were used to establish a partial least square (PLS) model based on correlation local embedding (CLE). Mongolian oak (Quercus mongolica Fisch. ex Ledeb.) was used to test the effectiveness of the model. The cross-validation method was used to verify the robustness of the CLE–PLS model. Ninety samples were tested as the calibration set and forty-five as the validation set. The results show that the prediction coefficient of determination ($$R_{p}^{2}$$Rp2) is 0.80 for MOR, and 0.78 for MOE. The ratio of performance to deviation is 2.23 for MOR and 2.15 for MOE.