基于PCA-Stacking模型的食源性致病菌拉曼光谱识别

Raman Spectroscopic Classification of Foodborne Pathogenic Bacteria Based on PCA-Stacking Model

食源性致病菌的快速识别是一项重要的工作,与传统检测方法相比,拉曼光谱能在无损检测的同时加快鉴别速度。为了提高大肠杆菌O157…H7以及布鲁氏菌S2株拉曼光谱识别的准确性和效率,提出一种基于主成分分析与Stacking算法的集成判别模型,使用网格搜索以及K折交叉验证来提高模型的稳健性。与逻辑回归、K近邻、支持向量机等单一模型进行对比,实验结果证明PCA-Stacking集成模型有最高的准确率,达99.73%,达到了预期效果。

The rapid identification of foodborne pathogenic bacteria is an important task.Compared with the traditional detection methods,Raman spectroscopy is a non-destructive testing method and can simultaneously enhance the identification speed.In order to improve the accuracy and efficiency of Raman spectroscopic identification of Escherichia coil O157…H7 and Brucella suis vaccine strain S2,a integral classification model is proposed based on the principal component analysis and the Stacking algorithm,whose robustness is improved by the grid search and K-fold cross validation.It is experimentally confirmed that compared with the logistic regression,K nearest neighbor,support vector machine and other single models,the integral model based on the Stacking algorithm possesses the highest accuracy rate of 99.73%the expected result is achieved.