Hyperspectral Imaging Combined with Back Propagation Neural Network Optimized by Sparrow Search Algorithm for Predicting Gelatinization Properties of Millet Flour

For large-scale rapid detection of the gelatinization parameters of millet flour, a method to predict the gelatinization characteristics of millet flour was explored using hyperspectral imaging combined with deep learning. The average spectral data of millet flour were obtained through successive hyperspectral data feature extraction and preprocessing, and based on the data matrix obtained, a regression model to predict the gelatinization parameters of millet flour samples was developed using a back propagation (BP) neural network optimized by sparrow search algorithm (SSA). The results showed that the spectral data pre-processing program used in this study could standardize and simplify the process of spectral data extraction and pre-processing, and this program was generally applicable to spectral data extraction and pre-processing for powder and fine particle samples. BP algorithm and SSA-optimized BP algorithm were used to predict the gelatinization parameters of millet flour. The mean square error (MSE) between the prediction value and the tested value of each parameter decreased after optimization of BP algorithm, from 0.0266 to 0.0175 for peak viscosity. Therefore, the SSA optimized BP algorithm could predict the gelatinization properties of millet flour more accurately. This study can provide theoretical support for the application of hyperspectral imaging coupled with deep learning in the prediction of the gelatinization properties of millet flour.