No abstract

Cereal crops play an important role in preventing chronic diseases and regulating human functions due to their rich phytochemicals. However, an increasing number of cases of cereal-crop adulteration are occurring, affecting the nutrition of food and threatening food safety. Therefore, this study used hyperspectral imaging (HSI) and a back-propagation neural network (BPNN) to establish the variety identification model of different grain shapes, colors, and sizes. This model can conduct a quantitative and qualitative analysis of samples rapidly and nondestructively, because HSI can collect spectral information and spatial information of samples. Meanwhile, the Watershed algorithm was used to identify cereal varieties by particles, and the model identification performance was verified by the unlabeled test sets. The results show that the data extraction success rate of the new watershed algorithm reached 98%, and the comprehensive identification accuracy of the model reached 90%. In addition, the cereal in the training set can be changed to identify other cereal crops, thereby providing a method of rapid and nondestructive adulteration detection of cereals. Practical ApplicationsCereal crops play an important role in preventing chronic diseases and regulating human functions due to their rich phytochemicals. However, an increasing number of cases of cereal-crop adulteration are occurring, affecting the nutrition of food and threatening food safety. Therefore, this study used hyperspectral imaging (HSI) and a back-propagation neural network (BPNN) to establish the variety identification model of different grain shapes, colors, and sizes. This model can conduct a quantitative and qualitative analysis of samples rapidly and nondestructively, because HSI can collect spectral information and spatial information of samples at the same time. The spectral information was used for qualitative analysis, while spatial information was used for quantitative analysis, so this model can realize the rapid and nondestructive detection of different varieties of cereals. And we can also change the training-set data to realize the variety identification of other varieties of crops, which provides guidance for the method detecting the adulteration of cereal crops.