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

Abstract

The rapid identification of foodborne pathogenic bacteria is an important task. Compared with traditional detection methods, Raman spectroscopy is a non-destructive testing method and it can reduce the identification time. In order to improve the accuracy and efficiency of Raman spectra identification of Escherichia coil O157:H7 and Brucellasuis vaccine strain 2, this paper proposes a classification model that based on principal component analysis and Stacking algorithm. Grid search and K-fold cross validation are used to improve the robustness of the model. Compared with other models such as K Nearest Neighbor, and Support Vector Machine, the experimental results show that the Stacking algorithm as an ensemble algorithm has the highest accuracy rate of 95.73%, which has achieved the expected results.