Abstract：In recent years, quality problems of Chinese bacon such as acid values and peroxide values exceeding the national standard, color fading, oil exudation and sticky feeling to the touch have received growing attention. With that in mind, a fast, accurate and practical detection method to evaluate Chinese bacon quality is presented in this paper. We established a predictive model for bacon quality detection by using the support vector machine (SVM) approach based on the near-infrared spectral data (acid value, peroxide value, volatile base nitrogen) and microscopic image data (the total number of microbial colonies). Moreover, the model was optimized by using particle swarm optimization (PSO) algorithm. It was found that the prediction results of the SVM model and the biochemical method were consisted for bacon quality classification. Besides, the predictive accuracy of the classification mode was increased from 97.5% to 100% after optimization. The SVM model optimized by PSO proved to be able to quickly and accurately detect Chinese bacon quality.
郭培源，刘艳芳，邢素霞，王昕琨. 基于支持向量机及粒子群算法的腊肉品质等级检测[J]. 肉类研究, 2017, 31(3): 30-34.
GUO Peiyuan, LIU Yanfang, XING Suxia, WANG Xinkun. Predication of Chinese Bacon Quality Grades Based on Support Vector Machine and Particle Swarm Optimization Algorithm. Meat Research, 2017, 31(3): 30-34.
