Application of visible-near-infrared hyperspectral imaging technology coupled with wavelength selection algorithm for rapid determination of moisture content of soybean seeds

Abstract

Moisture content is a crucial factor affecting the quality of soybean seeds. However, the determination of moisture content of soybean seeds is time-consuming and expensive. In this study, visible-near-infrared hyperspectral imaging technology (400–1000 nm) coupled with wavelength selection algorithm was applied to determine the moisture content of soybean seeds. Hyperspectral images of 96 soybean samples were obtained, and the sample set partitioning based on joint x-y distance algorithm was used to divide the calibration and prediction sets after removing outliers. Then, partial least squares regression (PLSR) models based on the original and preprocessing spectra were established, and the prediction effect of the original spectra was better than that of the preprocessing spectra. Five wavelength selection algorithms were used to select feature wavelengths to optimize the models further. Each wavelength selection algorithm was run 100 times independently to investigate its stability. The PLSR models were established based on the results of wavelength selection, and the prediction effects of all models were statistically analyzed. Results showed that the combination of interval variable iterative space shrinkage approach and successive projections algorithm (IVISSA-SPA) based on the original spectra was the most suitable model for the determination of moisture content of soybean seeds. The prediction accuracies of the IVISSA-SPA model were R2P = 0.9713 ± 0.0044, RMSEP = 0.307 ± 0.021 and RPD = 6.058 ± 0.344 in 100 independent experiments. Results indicated that visible-near-infrared hyperspectral imaging coupled with wavelength selection algorithm provided a rapid method for determining the moisture content of soybean seeds.