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首页> 外文期刊>International Journal of Food Microbiology >Development of a dynamic growth-death model for Escherichia coli O157:H7 in minimally processed leafy green vegetables.
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Development of a dynamic growth-death model for Escherichia coli O157:H7 in minimally processed leafy green vegetables.

机译:在最少加工的绿叶蔬菜中建立大肠埃希氏菌O157:H7动态生长死亡模型。

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摘要

Escherichia coli O157:H7, an occasional contaminant of fresh produce, can present a serious health risk in minimally processed leafy green vegetables. A good predictive model is needed for Quantitative Risk Assessment (QRA) purposes, which adequately describes the growth or die-off of this pathogen under variable temperature conditions experienced during processing, storage and shipping. Literature data on behaviour of this pathogen on fresh-cut lettuce and spinach was taken from published graphs by digitization, published tables or from personal communications. A three-phase growth function was fitted to the data from 13 studies, and a square root model for growth rate ( mu) as a function of temperature was derived: mu = (0.023*(Temperature-1.20))2. Variability in the published data was incorporated into the growth model by the use of weighted regression and the 95% prediction limits. A log-linear die-off function was fitted to the data from 13 studies, and the resulting rate constants were fitted to a shifted lognormal distribution (Mean: 0.013; Standard Deviation, 0.010; Shift, 0.001). The combined growth-death model successfully predicted pathogen behaviour under both isothermal and non-isothermal conditions when compared to new published data. By incorporating variability, the resulting model is an improvement over existing ones, and is suitable for QRA applications
机译:大肠杆菌O157:H7是新鲜农产品的偶发污染物,在加工最少的带叶绿色蔬菜中会严重危害健康。定量风险评估(QRA)的目的需要一个良好的预测模型,该模型可以充分描述该病原体在加工,存储和运输过程中遇到的温度变化条件下的生长或死亡。关于这种病原体在鲜切生菜和菠菜上的行为的文献数据,是通过数字化出版的图表,出版的表格或通过个人交流获得的。将三相生长函数拟合到来自13个研究的数据,并得出了生长速率(mu)作为温度函数的平方根模型:mu =(0.023 *(Temperature-1.20)) 2 < / sup>。通过使用加权回归和95%的预测限制,将已发布数据的可变性纳入增长模型。对数线性死亡函数拟合了来自13个研究的数据,并将所得的速率常数拟合为移位的对数正态分布(平均值:0.013;标准偏差为0.010;移位为0.001)。与新发布的数据相比,组合的生长死亡模型成功地预测了等温和非等温条件下的病原体行为。通过合并可变性,所得模型是对现有模型的改进,适用于QRA应用

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