Determination of water content in corn stover silage using near-infrared spectroscopy

The aim of this study was to evaluate the feasibility of utilizing near-infrared spectroscopy to determine the water content of corn stover silage across a wide range. The water contents of 208 samples were measured, and their corresponding near-infrared spectra were simultaneously collected. The effects of different preprocessing methods, such as derivation, standard normal variety (SNV), multiplicative scatter correction (MSC), and non-preprocessing methods for the obtained near-infrared spectra on the performance of calibration models were compared. The calibration models were established by modified partial least squares (MPLS) regression. The results showed that the calibration model developed from the successive preprocessing of MSC and first-order derivation (1-D) achieved the optimal performance. The correlation coefficients of the calibration and validation subset were 0.974 and 0.949, respectively, and the standard errors of the calibration and cross validation were 4.249% and 4.256%, respectively. External validation was performed on 60 samples. The correlation coefficient between the measured and predicted values of the calibration model was 0.973 and the prediction model’s relative percent deviation was 4.317. This indicated that the mathematical model of near-infrared spectroscopy predicted the water content in corn stover silage with high accuracy. The study showed that the near-infrared spectroscopy technology can be used for rapid and non-destructive testing across a wide range of water contents in the corn stover silage.