A novel combined application of capacitive method and near-infrared spectroscopy for predicting the density and moisture content of solid wood

Abstract

The use of a capacitive method and near-infrared (NIR) spectroscopy to simultaneously predict the density and moisture content (MC) of wood samples was investigated. Prediction accuracy of both methods was individually investigated by multivariate analyses. The capacity and absorbance at two wavelengths in the NIR range were combined to predict both the properties by the specific models. All wavelength combinations in the range of 908–1676 nm were tested, and the best combination yielding the highest coefficient of determination (R 2) was chosen. This novel method showed a strong correlation between predicted and measured data, independent of sample thickness and wood species. The prediction accuracy of the wood samples, from green wood to oven-dried conditions, showed promising results for all thicknesses, with R 2 = 0.79, root-mean-square error of cross-validation (RMSECV) = 0.10 g/cm3, and residual predictive deviation (RPD) = 2.22 for density and R 2 = 0.80, RMSECV = 25.70%, and RPD = 2.22 for MC. In the case of below fiber saturation point to oven-dried state, R 2 value was slightly decreased in the prediction of MC and slightly increased in the prediction of density, but RMSECV of MC declined significantly (maximum 5.46%) compared to the range of MC from saturated point (maximum 39.56%). These results were considerably better than those obtained by modeling the capacitive or NIR method individually, and improvement was particularly apparent in estimating density. The results suggest the possibility of a new device combining the capacitive method and NIR spectroscopy to predict density and MC more accurately.