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.