基于支持向量机的食源性致病菌近红外光谱鉴别

Identification of Food-Borne Bacteria by Near Infrared Spectroscopy Based on Support Vector Machine

以近红外光谱法结合支持向量机对大肠杆菌O157∶H7、单增李斯特菌、金黄色葡萄球菌进行了分类鉴别。对预处理后的3种食源性致病菌近红外光谱数据进行主成分分析,以前26个主成分向量为支持向量机输入量建立支持向量机模型,对径向基函数核函数分类器与多项式核函数分类器进行了对比分析。结果表明,以径向基函数为核函数的支持向量机在核参数为0.5时对3种食源性致病菌的鉴别效果最好,与国标方法相比结果一致,其鉴别准确率均达到100%。

Escherichia coli O157:H7, Listeria monocytogenes and Staphylococcus aureus were identified by combined use of near infrared(NIR) spectroscopy and support vector machine(SVM) in this study. After pretreatment, the NIR spectra of the three species of food-borne bacteria were analyzed by principal component analysis(PCA), and the first 26 principal components(PCs) were extracted and used as inputs to establish an SVM model. A comparison was performed between the radial basis function(RBF) SVM classifiers and the polynomial SVM classifiers. The results indicate that the three kinds of food-borne bacteria can be completely identified by the RBF-SVM classifier with a nuclear parameter of 0.5, giving a prediction accuracy of 100% and results consistent with those obtained from the Chinese national standard.