School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
Food Res Int. 2022 Jun;156:111132. doi: 10.1016/j.foodres.2022.111132. Epub 2022 Mar 14.
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: 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.
单细胞变异性的重要性随着食源性致病菌建模的发展而日益凸显。由于单个细胞的异质性，传统的预测微生物学模型无法准确描述少数细胞的生长行为。本研究的目的是开发用于沙门氏菌肠热病后热和氯处理的单细胞滞后时间的预测模型。使用时滞显微镜方法通过监测细胞分裂来评估单细胞滞后时间。应用了四种有监督的机器学习算法，包括梯度提升回归树（GBRT）、人工神经网络（ANN）、随机森林（RF）和支持向量回归（SVR），并进行了比较。结果表明，所有四种机器学习模型都具有良好的预测能力，而不会对数据进行过度拟合。ANN 方法在其他机器学习模型（RMSE：0.209、MAE：0.135 和 R：0.989）上表现出优越的预测性能。此外，使用 SHapley Additive exPlanation（SHAP）度量来捕获每个特征对模型输出的影响，结果表明群体滞后时间和亚致死损伤率对单细胞滞后时间有主要影响。因此，本研究的结果可能有助于管理食源性致病菌单细胞引起的潜在食品安全风险。
