Feasibility of near-infrared spectroscopy for online multiple trait assessment of sawn lumber

Abstract

Near-infrared (NIR) spectroscopy coupled with multivariate analysis was applied to estimate multiple traits of sawn lumber. The effects of the lumber conveying speed (LCS) and measurement resolution of spectra (MRS) on the calibrations were examined. NIR spectra ranging from 1300 to 2300 nm were acquired at LCSs of 10, 20, and 30 m/min and at MRSs of 2, 4, and 16 nm. Prediction models of bending strength (F b), modulus of elasticity in bending tests (E b), dynamic modulus of elasticity (E fr), and wood density (DEN) were developed using partial least-squares (PLS) analysis. LCS and MRS did not significantly influence the calibration performance for any wood property. The regression coefficients also showed no clear differences for any of the conditions. This indicates that the important explanatory variables included in the models are not greatly influenced by these measurement conditions. PLS2 analysis results, when presented graphically, allowed easy interpretation of the relationships between wood mechanical properties and chemical components, e.g., bending strength and stiffness were mainly related to polysaccharides cellulose and hemicellulose. NIR spectroscopy has considerable potential for online grading of sawn lumber, despite the harsh measurement conditions.