基于SVM方法的猪肉新鲜度分类问题研究

文中采取减压贮藏方式对新鲜猪肉进行了贮藏实验,测定了不同贮藏时间样品的挥发性盐基氮含量(TVB-N)、细菌总数、pH值及感官评价数据,并运用支持向量机(support vector machine,SVM)对这些样本数据进行训练,选取不同的核函数,得到SVM神经网络模型,随后利用此模型对测试数据进行猪肉新鲜度分类预测。实验表明,根据样本特性进行数据预处理,且选取合适的核函数后,SVM神经网络能得到极高的猪肉新鲜度正确分类率。

Studies on the Classification of Pork Freshness by SVM

The pork freshness is a big safety issue on people's health.In this paper,fresh pork samples were stored in decompression storage room.The TVB-N content,total bacterial count,pH value and sensory scores of the samples were determined at different storage stage.SVM neural networks models were obtained by training the sample data with different kernel functions and cross-validation.Furthermore,the test data were used to predict the freshness of pork sample by SVM neural network.The experiment results suggested that the SVM neural networks obtained higher correct classification rate of pork freshness with the right kernel function and cross-validation according to the sample performance.