Classification and identification of foodborne pathogenic bacteria by Raman spectroscopy based on PCA and LightGBM algorithm

Foodborne pathogens are the most common food safety hazards, and the traditional detection methods of foodborne pathogens are cumbersome, long waiting time and slow efficiency. This paper studies two common foodborne pathogenic bacteria, Brucella and Escherichia coli. LightGBM algorithm combined with Principal Component Analysis (PCA) was used to analyze Raman spectrum sample data to solve the problem of classification and detection of foodborne pathogens. The results show that LightGBM algorithm has excellent detection rate. Compared with traditional machine learning algorithm models such as Decision Tree, Random Forest and XGBoost, LightGBM algorithm has the following advantages of low memory consumption and model training speed fastly, and the model accuracy rate reaches 91.23%.