Abstract：
Shiga toxin-producing Escherichia coli (STEC) can be life-threatening and lead to major outbreaks. The prevention of STEC-related infections can be provided by control measures at all stages of the food chain. The growth performance of E. coli O157:H7 at different temperatures in raw ground beef spiked with cocktail inoculum was investigated using machine learning (ML) models to address this problem. After spiking, ground beef samples were stored at 4, 10, 20, 30 and 37 °C. Repeated E. coli O157 enumeration was performed at 0-96 h with 21 times repeated counting. The obtained microbiological data were evaluated with ML methods (Artificial Neural Network (ANN), Random Forest (RF), Support Vector Regression (SVR), and Multiple Linear Regression (MLR)) and statistically compared for valid prediction. The coefficient of determination (R2) and mean squared error (MSE) are two essential criteria used to evaluate the model performance regarding the comparison between the observed value and the prediction made by the model. RF model showed superior performance with 0.98 R2 and 0.08 MSE values for predicting the growth performance of E. coli O157 at different temperatures. MLR model predictions were obtained further from the observed values with 0.66 R2 and 2.7 MSE values. Our results indicate that ML methods can predict of E. coli O157:H7 growth in ground beef at different temperatures to strengthen food safety professionals and legal authorities to assess contamination risks and determine legal limits and criteria proactively.
摘要：
产生志贺毒素的大肠杆菌（STEC）可能危及生命并导致重大爆发。可以通过食物链所有阶段的控制措施来预防STEC相关感染。使用机器学习（ML）模型研究了大肠杆菌O157：H7在不同温度下加有鸡尾酒接种物的生牛肉中的生长性能，以解决此问题。加标后，将碎牛肉样品储存在4、10、20、30和37°C。重复大肠杆菌O157计数在0-96小时进行,重复计数21次。用ML方法(人工神经网络(ANN)，随机森林(RF),支持向量回归(SVR)和多元线性回归(MLR))，并对有效预测进行统计比较。确定系数（R2）和均方误差（MSE）是用于评估模型性能的两个基本标准，这些标准涉及观察值与模型进行的预测之间的比较。RF模型表现出优异的性能与0.98R2和0.08MSE值预测大肠杆菌O157在不同温度下的生长性能。进一步从0.66R2和2.7MSE值的观测值获得MLR模型预测。我们的结果表明，ML方法可以预测不同温度下碎牛肉中大肠杆菌O157：H7的生长，以加强食品安全专业人员和法律当局评估污染风险并主动确定法律限制和标准。
