Machine learning approach for predicting single cell lag time of Salmonella Enteritidis after heat and chlorine treatment. (June 2022)

• Record Type: Journal Article
• Authors: Lin, Zijie; Qin, Xiaojie; Li, Jing; Zohaib Aslam, Muhammad; Sun, Tianmei; Li, Zhuosi; Wang, Xiang; Dong, Qingli
• Is Part Of: Food research international. Volume 156(2022)
• Publication Date: 2022-06
• Subjects: Machine learning -- Single cell -- Lag time -- Feature importance analysis -- Foodborne pathogen -- Food safety; Food -- Analysis -- Periodicals; Food industry and trade -- Periodicals; Food industry and trade -- Canada -- Periodicals; Food Technology -- Periodicals; Food -- Periodicals; Food-Processing Industry -- Periodicals; Aliments -- Industrie et commerce -- Périodiques; Aliments -- Industrie et commerce -- Canada -- Périodiques; Aliments -- Recherche -- Périodiques; Food industry and trade; Canada; Periodicals; Electronic journals; 664.005
• DOI: 10.1016/j.foodres.2022.111132
• Languages: English
• ISSNs: 0963-9969

Abstract

Graphical abstract: Highlights: Four supervised machine learning models were developed for single cell lag times of Salmonella Enteritidis after heat and chlorine treatment. ANN model achieved the best prediction performance for single cell lag times. The influence of the population lag time and sublethal injury rate on the single cell lag time were evaluated. Machine learning models may be useful in improving the accuracy of risk assessment for single cells of S. Enteritidis. Abstract: The importance of single-cell variability is increasingly prominent with the developments in foodborne pathogens modeling. Traditional predictive microbiology model cannot accurately describe the growth behavior of small numbers of cells due to individual cell heterogeneity. The objective of the present study was to develop predictive models for single cell lag times of Salmonella Enteritidis after heat and chlorine treatment. A time-lapse microscopy method was employed to evaluate the single cell lag time by monitoring cell divisions. Four supervised machine learning algorithms including gradient boosting regression tree (GBRT), artificial neural network (ANN), random forest (RF), and support vector regression (SVR) were applied and compared. Results show that all four machine learning models have good predictive capabilities without an overfitting of the data. The ANN approach demonstrated superior prediction performance over other machine learning models (RMSE: 0.209, MAE: 0.135 and R 2 : 0.989). Furthermore, the SHapley Additive exPlanation (SHAP) measures were used to capture the influence of each feature on the model output, and results revealed that population lag times and sublethal injury rate have dominant impacts on the single cell lag time. Consequently, the findings generated from this study may be useful in managing the potential food safety risk caused by single cells of foodborne pathogens.