Rapid and nondestructive evaluations of wood mechanical properties by near infrared spectroscopy

Near infrared (NIR) spectroscopy, coupled with multivariate analytic statistical techniques, has been used to predict the mechanical properties of solid wood samples taken from small clear and full length lumber specimens of hybrid larch. The specific mechanical characteristics evaluated were the modulus of elasticity (MOE), the modulus of rupture (MOR), compression strength parallel to the grain (CS) in small clear specimens, dynamic modulus of elasticity of air-dried lumbers (Efr), and wood density (DEN). Partial least squares (PLS) regression calibrations were developed for each wood property and found that the calibrations gave relatively strong relationships between laboratory-measured and NIR-predicted values in small clear specimens, with coefficients of determination ranging from 0.61 to 0.89. The calibration models were applied to the prediction data sets (unknown samples), suggesting that NIR spectroscopy has the potential to predict the mechanical properties with adequate accuracy. Comparison of the prediction based on spectra obtained from the radial vs tangential face showed that the prediction models based on spectra obtained from radial face were slightly superior. Although the accuracy of calibration and prediction was lesser than the small clear specimens, reasonable predictive model for wood stiffness could be taken from the full length lumber specimens. Regression coefficients resulting from PLS analysis indicates that variation in wood components such as cellulose, lignin and possibly hemicellulose governs wood mechanical properties.