UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

Assessment of some wood properties by near infrared spectroscopy Ayanleye, Samuel

Abstract

Near-infrared spectroscopy (NIRS) is a suitable technique for characterizing many materials, including wood, and has been used to predict several wood properties. However, existing reports on this use of NIRS have paid little attention to the effect of wood surface condition and grain orientation. This study therefore used NIRS to assess wood density, modulus of elasticity, modulus of rupture, grain angle, and annual ring width, studying whether and how surface condition and grain orientation affected the measurement of these properties. The research focused on using NIRS coupled with partial least squares regression (PLS-R) to predict the properties of two softwoods (Western hemlock and Douglas-fir). PLS-R models were calibrated and validated using the test-set validation method. The predictive accuracies based on grain orientation (quarter-sawn and flat-sawn) and wood surface condition (rough and smooth) were compared. Models developed using reduced wavelengths also showed the possibility of predicting these properties using a narrow spectral range. The results of this study showed that calibrations based on mixed sets, which included both cross-sections, were inferior to those based on these cross-sections separately. Promising predictive models were obtained for density (Rp² = 0.66), modulus of elasticity (Rp² = 0.78), and modulus of rupture (Rp² = 0.82), with poor correlations for grain angle and annual ring width (Rp² ≤ 0.50). Further, the rough surface predictions outperformed those from the smooth surface for all properties. The quarter-sawn sections also showed better predictive ability than the flat-sawn sections for both surface conditions. The only exception was for modulus of rupture, where the trend was reversed. The results therefore show the potential for using NIRS as a non-destructive technique to predict the properties of wood.

Item Citations and Data

Rights

Attribution-NonCommercial-NoDerivatives 4.0 International

Usage Statistics