Machine Vision Based Granular Raw Material Adulteration Identification in Baijiu Brewing

This paper proposes a machine vision based sorghum grain adulteration identification method for Baijiu brewing applications. A case study with respect to glutinous sorghum mixed with japonica sorghum is conducted. The designed sampling box with vibrating table is used to capture images with mobile phone. Image preprocessing, including brightness equalization, binarization, morphological operations and connected component calculation, are used to segment different sorghum particles and thus extract particle size, shape and color features including area, major and minor axis length, eccentricity, roundness, shape factor, aspect ratio, RGB, HSV and CIELAB parameters. With principal component analysis (PCA) for data dimensionality reduction, the support vector machine (SVM) is used to classify sorghum varieties and discriminate whether sorghum grains are adulterated. The preliminary experimental results show that the proposed machine vision based method and SVM model with radial basis function (RBF) kernel achieved an accuracy of 84.71 % for the classification of glutinous sorghum and japonica sorghum. Moreover, the model identifies and labels impurity grains precisely on the constructed manually constructed samples for adulterated sorghums, which demonstrates the promising for the nondestructive and low-cost adulteration identification of sorghum raw material in Baijiu brewing process and may promote the digital transformation of the industry and improve production efficiency.