Rapid resolution of types and proportions of broken grains using hyperspectral imaging and optimization algorithm

The rapid differentiation of the type and proportion of raw materials is the key factor for the adjustment of the subsequent process parameters and to ensure product quality. Therefore, in this study, a combination of an optimization algorithm and hyperspectral imaging (HSI) technology was used to distinguish many varieties of broken grains and determine their mixing ratio. The reflectance of the region of interest (ROI) of the sample was extracted, and the outliers were then removed by the density-based spatial clustering of application with noise (DBSCAN) method and the Mahalanobis distance (MD). The principal component analysis (PCA) algorithm was then used to analyze the spectral data after removing the outliers. Next, iterative variable subset optimization (IVSO) and competitive adaptive reweighting sampling (CARS) were used to extract the feature wavelengths. Four classification and recognition models (PNN, GRNN, BPNN, and RBFNN) based on the full and characteristic wavelengths were then established. A comparison of the results revealed that the BPNN model achieved the best effect; the classification recognition accuracies of the training set, testing set, and verification set of the full and characteristic wavelength models were above 99%. To determine the differences between BPNN models based on the full and characteristic wavelengths, seven different kernel functions were used to train the BPNN models; the classification recognition accuracy of the BPNN models trained by the one-step secant (OSS) and resilient functions was greater than 99%. Therefore, by using a combination of an optimization algorithm and HSI technology, it is possible to quickly distinguish mixed grains and determine their mixing ratio. This study provides new technical guidance for the liquor industry to improve the quality of liquor.