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机器学习模型用于预测Escherichia coli O157:H7在不同储存温度下生碎牛肉中的生长
Serhat Al1, Fatma Uysal Ciloglu2, Aytac Akcay3
1Erciyes University, Veterinary Faculty, Food Hygiene and Technology Department, Kayseri, Turkey.
Meat science
|January 18, 2024
概括
机器学习模型准确地预测了在不同温度下生碎牛肉中产生Shiga毒素的大肠杆菌 (STEC) 的增长. 随机森林模型表现出卓越的预测性能,提高了食品安全评估.
科学领域:
- 食品安全 食品安全
- 微生物学 微生物学
- 机器学习是机器学习.
背景情况:
- 滋格毒素产生的大肠杆菌 (STEC) 对健康构成重大风险,并导致重大疫情爆发.
- 在整个食物链中采取有效的控制措施对于预防STEC感染至关重要.
研究的目的:
- 为了研究大肠杆菌O157:H7在不同温度下生碎牛肉中的生长性能.
- 应用机器学习 (ML) 模型来预测细菌生长和评估污染风险.
主要方法:
- 生碎牛肉样本被注射了大肠杆菌O157:H7并存储在4°C至37°C的温度下.
- 在96小时内重复进行细菌计数.
- 包括人工神经网络 (ANN),随机森林 (RF),支持向量回归 (SVR) 和多线性回归 (MLR) 在内的机器学习模型被使用和比较.
主要成果:
- 随机森林 (RF) 模型表现出最高的准确性,R2为0.98和MSE为0.08.
- 多重线性回归 (MLR) 的预测准确性较低 (R2 = 0.66,MSE = 2.7).
- ML模型有效地预测了不同温度下大肠杆菌O157:H7的生长动态.
结论:
- 机器学习模型,特别是RF,在预测E. coli O157:H7在磨牛肉中的生长方面非常有效.
- 这些预测能力可以帮助食品安全专业人员和法律当局积极评估风险和制定监管标准.
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