优化支持向量机在鲜切生菜加工HACCP分类中的应用
Application of optimized support vector machine in HACCP classification of fresh-cut lettuce processing
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摘要: 该文介绍了一种基于支持向量机模型解决鲜切生菜加工HACCP（hazard analysis and critical control point）关键控制点智能发现的方法。在通用支持向量机模型进行鲜切生菜加工关键控制点的发现试验中，依靠人工经验选取支持向量机（support vector machine，SVM）核函数参数，识别关键控制点准确率波动较大。该文利用遗传算法优化支持向量机核函数参数的选取，构建了一种基于遗传算法的支持向量机（GA-SVM）模型，应用该模型在鲜切生菜生产关键控制点的发现试验中获得了87.5%的识别率，比传统方法稳定性更高。该方法对其他HACCP关键控制点的智能发现具有很好的借鉴意义。Abstract: An automatic CCPs (critical control points) identification method for lettuce’s fresh-cut processing HACCP (hazard analysis and critical control point) implementation based on optimized SVM (support vector machine) model was introduced. The CCPs identification tests which use the general SVM algorithm showed its weakness because of the man-made kernel selection and the classification results had larger fluctuation. Generic algorithm was used to obtain an optimized kernel function and then the GA-SVM model was proposed. The model had been applied in the CCPs identification of lettuce’s fresh-cut processing and the classification accuracy of 87.5% was stabler than the traditional method. The method is also general which can be easily popularized to other HACCP implementations.
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