Erciyes University, Veterinary Faculty, Food Hygiene and Technology Department, Kayseri, Turkey.
Erciyes University, Engineering Faculty, Biomedical Engineering Department, Kayseri, Turkey.
Meat Sci. 2024 Apr;210:109421. doi: 10.1016/j.meatsci.2023.109421. Epub 2023 Dec 30.
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 (R) 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 R 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 R 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.
产志贺毒素大肠杆菌（STEC）可危及生命，并导致重大疫情爆发。通过在食物链的各个阶段采取控制措施，可以预防 STEC 相关感染。本研究使用机器学习 (ML) 模型来解决这一问题，研究了不同温度下添加鸡尾酒接种物的生绞碎牛肉中大肠杆菌 O157:H7 的生长性能。接种后，将绞碎牛肉样品在 4、10、20、30 和 37°C 下储存。在 0-96 h 内重复进行大肠杆菌 O157 的计数，重复计数 21 次。使用 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)）对获得的微生物数据进行评估，并对有效预测进行统计比较。决定系数 (R) 和均方误差 (MSE) 是两个重要标准，用于比较模型预测值与观测值之间的模型性能。RF 模型在预测不同温度下大肠杆菌 O157 的生长性能方面表现出优异的性能，其 R 值为 0.98，MSE 值为 0.08。MLR 模型的预测值与观测值相差较大，R 值为 0.66，MSE 值为 2.7。研究结果表明，ML 方法可预测不同温度下生绞碎牛肉中大肠杆菌 O157:H7 的生长情况，有助于食品安全专业人员和法律部门主动评估污染风险，并确定法律限值和标准。
