Prediction of moisture content of wood using Modified Random Frog and Vis-NIR hyperspectral imaging

Abstract

Vis-NIR hyperspectral imaging was adopted to predict the moisture content (MC) of wood. A total of 30 beech blocks were used in the experiments, each block was dried 7 times to generate different cases of moisture contents. The 210 samples were divided into a calibration set (140) and a prediction set (70) using Kennard-stone (KS) algorithm. The spectral data of each sample was collected by the hyperspectral imaging system, and pretreated using standard normal variate (SNV). To solve the problem that the random frog algorithm (RF) needs to reset threshold after obtaining the selection probability of all variables at a time, adaptive reweighted sampling (ARS) and exponentially decreasing function (EDF) are applied to improve RF in this paper. The modified RF (MRF) was compared with RF, successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS). Based on the full spectrum data (FS) and four characteristic spectral data, different models were built using Gaussian process regression (GPR). The results show that the MRF not only avoids setting thresholds, but also improves the stability and accuracy of the model. The MRF-GPR model achieved the best predictive performance of wood moisture content, with Rp2, RMSEP of 0.9785, 1.6125%, respectively. So the hyperspectral imaging technique is suitable for predicting the moisture content of wood.