Investigation of NIR Spectroscopy and Electrical Resistance-Based Approaches for Moisture Determination of Logging Residues and Sweet Sorghum
Keywords:Biomass, Electrical resistance, Moisture content, Moisture meter, Near-infrared spectroscopy, Outlier detection, Partial least squares
Techniques based on electrical resistance and near-infrared (NIR) spectroscopy were used to determine the moisture content (MC) of logging residues and sweet sorghum. The MC of biomass is a factor to be controlled that can affect the quality of final products. To accurately measure the moisture in fragmented materials, it is essential to increase the bulk density of the materials by compression. The low bulk density increased the error from the oven-drying MC and the variation between repeated measurements. The calculated correction factor made it possible to use a commercial wood moisture meter for biomass materials. Ordinary least squares regression models built with the electrical resistance data achieved coefficients of determination (R2) of 0.933 and 0.833 with root mean square errors (RMSE) of 0.505 and 0.891, respectively, for the MC predictions of logging residue and sweet sorghum. Partial least squares regression models combined with NIR spectroscopy achieved R2 of 0.942 and 0.958 with RMSE of 1.318 and 3.681 for logging residue and sweet sorghum, respectively. In contrast to the electrical resistance-based models, the NIR-based models could predict the MC regardless of the bulk density of the materials. Data transformation by the second derivative and removal of outliers contributed to the improvement of the prediction of the NIR-based models.