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开发一种用于最小处理的叶类绿色蔬菜中大肠杆菌 O157:H7 的动态生长-死亡模型。

Development of a dynamic growth-death model for Escherichia coli O157:H7 in minimally processed leafy green vegetables.

机构信息

AAFC Research Associate, Central Experimental Farm, 960 Carling Ave., Ottawa, Ontario, Canada K1N0C6.

出版信息

Int J Food Microbiol. 2011 Nov 15;151(1):7-14. doi: 10.1016/j.ijfoodmicro.2011.07.027. Epub 2011 Jul 29.

DOI:10.1016/j.ijfoodmicro.2011.07.027
PMID:21872959
Abstract

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 (μ) as a function of temperature was derived: μ=(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.

摘要

大肠杆菌 O157:H7 偶尔会污染新鲜农产品,对经过轻微加工的绿叶蔬菜构成严重的健康威胁。定量风险评估 (QRA) 需要一个良好的预测模型,该模型能够充分描述在加工、储存和运输过程中经历的各种温度条件下,这种病原体的生长或死亡情况。从已发表的图表、表格或个人通讯中,对鲜切生菜和菠菜中这种病原体行为的文献数据进行了数字化处理。对 13 项研究的数据进行了拟合,得到了一个三段式生长函数,并推导出了一个平方根模型来描述温度对生长率 (μ) 的影响:μ=(0.023*(Temperature-1.20))(2)。通过使用加权回归和 95%预测限,将发表数据中的变异性纳入生长模型中。对 13 项研究的数据进行了拟合,得到了一个对数线性衰减函数,并将得到的速率常数拟合到一个移位的对数正态分布中(均值:0.013;标准差:0.010;移位:0.001)。与新发表的数据相比,组合的生长-死亡模型成功预测了等温和非等温条件下病原体的行为。通过纳入变异性,得到的模型优于现有模型,适用于 QRA 应用。

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