A second-order Monte Carlo simulation model to describe coagulase-positive Staphylococci growth in mayonnaise served in Algerian pizzerias

IF 3 4区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES Microbial Risk Analysis Pub Date : 2022-04-01 DOI:10.1016/j.mran.2021.100187
Mohammed Ziane , Jeanne-Marie Membré
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引用次数: 1

Abstract

To bridge the data gap on food poisoning caused by coagulase-positive staphylococci (CoPS), especially related to mayonnaise sauce served at Algerian pizzerias, this study aimed to assess the concentration of CoPS in mayonnaise and the probability of exceeding a critical concentration of ≥ 5 log CFU/g. The city of Ain Témouchent in West Algeria was taken as a case study.

A probabilistic assessment model was built, taking into account the initial contamination in freshly made mayonnaise and the potential growth before serving. Uncertainty and variability were integrated separately in the model. Uncertainty came from lack of data and model fitting error, variability from natural heterogeneity of biological materials (e.g., microbial strains) and temperature during cold storage.

The second-order Monte Carlo procedure was implemented in R using the mc2d package. The following pieces of data were generated to populate the model: CoPS were enumerated and characterized from 57 samples of mayonnaise served at pizzeria in Ain Témouchent city; challenge tests at 23 °C were performed in mayonnaise using three CoPS isolates. The following existing data were also gathered: meteorological data from Ain Témouchent were analysed to build a realistic scenario of storage, while a set of 43 and 35 values of the minimal and maximal growth limits of CoPS, respectively, were collected from the literature and analysed to inform a secondary predictive model describing the growth rate at various storage temperature conditions. A sensitivity analysis was performed to facilitate the interpretation of the results.

The results revealed a CoPS prevalence in freshly made mayonnaise of 25% [15%; 37%] with concentrations varying from 0.4 [0.3; 0.9] to 2.9 [2.4; 3.0] log CFU/g. The growth rates at 23 °C, based on challenge tests in mayonnaise, had a median value estimated to be 1.41 [1.17; 1.65] h  1.

Concentration levels according to various scenarios of temperature and serving conditions were calculated. For instance, the median contamination of CoPS in mayonnaise after storage in a refrigerated display counter for 14 h during the hottest months of the year was estimated to be zero. However, the 95th percentile was estimated to be 3.6 [2.9; 4.2] log CFU/g. In this scenario, the probability of exceeding a critical concentration of ≥ 5 log CFU/g was estimated to be 1% [0.3%; 2%], which is low but not negligible.

These findings could be used to improve food safety policies and develop a risk management strategy to reduce the food poisoning associated with the consumption of ready-to-use foods in Algerian fast food restaurants.

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描述阿尔及利亚披萨店蛋黄酱中凝固酶阳性葡萄球菌生长的二阶蒙特卡罗模拟模型
为了弥补凝固酶阳性葡萄球菌(CoPS)引起的食物中毒的数据缺口,特别是与阿尔及利亚披萨店供应的蛋黄酱酱有关的数据缺口,本研究旨在评估蛋黄酱中CoPS的浓度以及超过≥5 log CFU/g临界浓度的概率。以阿尔及利亚西部的艾因塔姆森特市为例进行研究。建立了一个概率评估模型,考虑了新鲜蛋黄酱的初始污染和食用前的潜在增长。在模型中,不确定性和可变性分别被集成。不确定性来自数据的缺乏和模型拟合误差、生物材料(如微生物菌株)的自然异质性以及冷藏期间的温度。二阶蒙特卡罗程序在R中使用mc2d包实现。生成了以下数据来填充模型:从Ain tsamuchent市披萨店供应的57份蛋黄酱样本中枚举和表征cop;在23°C的蛋黄酱中使用三个cop分离株进行攻毒试验。本文还收集了以下现有数据:分析Ain ttmochent的气象数据,建立了一个真实的储存场景;从文献中分别收集了cop最小和最大生长极限的43和35个值,并进行了分析,以建立描述不同储存温度条件下生长速率的二次预测模型。进行敏感性分析以促进对结果的解释。结果显示,新鲜制作的蛋黄酱中cop的患病率为25% [15%;37%],浓度从0.4 [0.3;0.9]至2.9 [2.4;3.0] log CFU/g。根据蛋黄酱的攻毒试验,在23°C时的生长速率中值估计为1.41 [1.17;[1.65] h−1。计算了不同温度和服务条件下的浓度水平。例如,在一年中最热的几个月里,蛋黄酱在冷藏展示柜台储存14小时后,其cop污染的中位数估计为零。然而,第95百分位数估计为3.6 [2.9;4.2] log CFU/g。在这种情况下,超过≥5 log CFU/g临界浓度的概率估计为1% [0.3%;(2%),虽然很低,但也不可忽略。这些发现可用于改进食品安全政策和制定风险管理战略,以减少阿尔及利亚快餐店中与食用即食食品有关的食物中毒。
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来源期刊
Microbial Risk Analysis
Microbial Risk Analysis Medicine-Microbiology (medical)
CiteScore
5.70
自引率
7.10%
发文量
28
审稿时长
52 days
期刊介绍: The journal Microbial Risk Analysis accepts articles dealing with the study of risk analysis applied to microbial hazards. Manuscripts should at least cover any of the components of risk assessment (risk characterization, exposure assessment, etc.), risk management and/or risk communication in any microbiology field (clinical, environmental, food, veterinary, etc.). This journal also accepts article dealing with predictive microbiology, quantitative microbial ecology, mathematical modeling, risk studies applied to microbial ecology, quantitative microbiology for epidemiological studies, statistical methods applied to microbiology, and laws and regulatory policies aimed at lessening the risk of microbial hazards. Work focusing on risk studies of viruses, parasites, microbial toxins, antimicrobial resistant organisms, genetically modified organisms (GMOs), and recombinant DNA products are also acceptable.
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