Literature DB >> 21511129

A mathematical risk model for Escherichia coli O157:H7 cross-contamination of lettuce during processing.

F Pérez Rodríguez1, D Campos, E T Ryser, A L Buchholz, G D Posada-Izquierdo, B P Marks, G Zurera, E Todd.   

Abstract

A stochastic simulation modelling approach was taken to determine the extent of Escherichia coli O157:H7 contamination in fresh-cut bagged lettuce leaving the processing plant. A probabilistic model was constructed in Excel to account for E. coli O157:H7 cross contamination when contaminated lettuce enters the processing line. Simulation of the model was performed using @Risk Palisade© Software, providing an estimate of concentration and prevalence in the final bags of product. Three different scenarios, named S1, S2, and S3, were considered to represent the initial concentration on the contaminated batch entering the processing line which corresponded to 0.01, 1 and 100 cfu/g, respectively. The model was satisfactorily validated based on Standard Error of Prediction (SEP), which ranged from 0.00-35%. ANOVA analysis performed on simulated data revealed that the initial concentration in the contaminated batch (i.e., S1, S2, and S3) did not influence significantly (p=0.4) the E. coli O157:H7 levels in bags derived from cross contamination. In addition, significantly different (p<0.001) prevalence was observed at the different levels simulated (S1; S2 and S3). At the lowest contamination level (0.01 cfu/g), bags were cross-contaminated sporadically, resulting in very low E. coli O157:H7 populations (mean: ≤2 cfu/bag) and prevalence levels (<1%). In contrast, higher average prevalence levels were obtained for S2 and S3 corresponding to 3.05 and 13.39%, respectively. Furthermore, the impact of different interventions on E. coli O157:H7 cross-contamination (e.g., pathogen testing, chlorination, irradiation, and cleaning and disinfection procedures) was evaluated. Model showed that the pathogen was able to survive and be present in the final bags in all simulated interventions scenarios although irradiation (0.5 KGy) was a more effective decontamination step in reducing prevalence than chlorination or pathogen testing under the same simulated conditions.
Copyright © 2010 Elsevier Ltd. All rights reserved.

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Year:  2010        PMID: 21511129     DOI: 10.1016/j.fm.2010.06.008

Source DB:  PubMed          Journal:  Food Microbiol        ISSN: 0740-0020            Impact factor:   5.516


  5 in total

1.  Postharvest Supply Chain with Microbial Travelers: a Farm-to-Retail Microbial Simulation and Visualization Framework.

Authors:  Claire Zoellner; Mohammad Abdullah Al-Mamun; Yrjo Grohn; Peter Jackson; Randy Worobo
Journal:  Appl Environ Microbiol       Date:  2018-08-17       Impact factor: 4.792

2.  Meta-analysis of the effects of sanitizing treatments on Salmonella, Escherichia coli O157:H7, and Listeria monocytogenes inactivation in fresh produce.

Authors:  Leonardo Prado-Silva; Vasco Cadavez; Ursula Gonzales-Barron; Ana Carolina B Rezende; Anderson S Sant'Ana
Journal:  Appl Environ Microbiol       Date:  2015-09-11       Impact factor: 4.792

3.  Development of a microbial dose response visualization and modelling application for QMRA modelers and educators.

Authors:  Mark H Weir; Jade Mitchell; William Flynn; Joanna M Pope
Journal:  Environ Model Softw       Date:  2016-11-24       Impact factor: 5.288

4.  Modeling the Reduction of Salmonella spp. on Chicken Breasts and Wingettes during Scalding for QMRA of the Poultry Supply Chain in China.

Authors:  Xingning Xiao; Wen Wang; Xibin Zhang; Jianmin Zhang; Ming Liao; Hua Yang; Qiaoyan Zhang; Chase Rainwater; Yanbin Li
Journal:  Microorganisms       Date:  2019-06-06

5.  Investigation on the Microbial Diversity of Fresh-Cut Lettuce during Processing and Storage Using High Throughput Sequencing and Their Relationship with Quality.

Authors:  Yeting Sun; Xiaoyan Zhao; Yue Ma; Zhihong Ma; Zhaoying He; Wenting Zhao; Pan Wang; Shuang Zhao; Dan Wang
Journal:  Foods       Date:  2022-06-08
  5 in total

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