The calorific value of biomass fuels is affected by the moisture content. In this study, the moisture content in corn straw, wheat straw, and rice straw was measured and predicted based on near-infrared spectroscopy (NIR) data. The prediction performance of the partial least squares (PLS) model was the best when first-order derivative preprocessing and the stochastic method of dataset division were used at the same time. The correlation coefficient of calibration (R_c^2), the root mean square error of calibration (RMSEC), and the root mean square error of prediction (RMSEP) were 0.937, 1.984 and 3.411 respectively. The results showed that PLS model based on NIR has the potential to rapidly characterize the moisture content of biomass fuel.
Keywords Moisture, Straw, Biomass, Near infrared spectroscopy