Predicting oven-dry density of Sugi (Cryptomeria japonica) using near infrared (NIR) spectroscopy and its effect on performance of wood moisture meter

Abstract

We propose a non-destructive method to predict the oven-dry density of Sugi (Cryptomeria japonica D. Don) using near infrared (NIR) spectroscopy so as to calibrate a commercial moisture meter. A prediction model for oven-dry density was developed using NIR spectra obtained from Sugi samples with a known density. The density of air-dried Sugi boards was predicted with the developed model. Then, the moisture content (MC) of the boards was measured by a hand-held capacitance-type and an in-line microwave moisture meters. For each board, the moisture meters were calibrated by the predicted density. The predicted density was correlated with the measured one with an R2 of 0.81 and a standard error of prediction (SEP) of 15.3 kg/m3 within the measured density of 279.2–436.4 kg/m3, indicating that the developed model was applicable for predicting oven-dry density of Sugi. The MC readings of both moisture meters showed a good correlation with the oven-dry MC that ranged from 12.1 to 28.9 %. For both moisture meters, the density calibration with the NIR-predicted density gave a higher R2 and a lower SEP than with the conventional calibration with the mean density. These results demonstrate that the present density calibration using NIR spectroscopy could improve the performance of the moisture meters for the air-dried Sugi boards with varying densities.