Prediction of Wood Density Independently of Moisture Conditions Using near Infrared Spectroscopy

Abstract

Near infrared (NIR) spectra obtained from 100 Japanese larch ( Larix kaempferi) wood samples containing various amounts of moisture were used to examine the effect of moisture conditions on the accuracy of predicting wood density. Partial least squares regression (PLS-R) analysis was performed to predict wood density under air dry (DEN_ar), water impregnated (DEN_wi) and oven dry (DEN_ov) conditions. The NIR spectra varied with the moisture conditions of the wood, where the characteristic absorbance bands in the vicinity of 7320 cm−1 (1366 nm), 7160 cm−1 (1400 nm) and 7000 cm−1 (1428 nm) were related to cellulose and water. The spectral differences between high- and low-density samples varied depending on the moisture conditions; high-density samples showed low absorbance values at 7160 cm−1 when wet and showed high absorbance values at 7320 cm−1 and 7000 cm−1 when dry. DEN_ar, DEN_wi and DEN_ov could be predicted using spectra collected from the corresponding moisture conditions [coefficient of determination ( R2) = 0.79–0.89; standard error of prediction ( SEP) = 24–26 kg m−3]. Prediction of DEN_ar and DEN_ov could also be achieved using spectra collected from various moisture conditions ( R2 = 0.86–0.87, SEP=22 kg m−3). The loadings from PLS-R analysis indicated that the absorption bands in the vicinity of 7320 cm−1, 7160 cm−1 and 7000 cm−1 played an important role in predicting wood density. NIR spectroscopy has the potential to predict wood density independently of the moisture content of the sample.