Title of article :
Evaluation of different approaches for modeling Escherichia coli O157:H7 survival on field lettuce
Author/Authors :
McKellar، نويسنده , , Robin C. and Peréz-Rodrيguez، نويسنده , , Fernando and Harris، نويسنده , , Linda J. and Moyne، نويسنده , , Anne-laure and Blais، نويسنده , , Burton and Topp، نويسنده , , Ed and Bezanson، نويسنده , , Greg and Bach، نويسنده , , Susan and Delaquis، نويسنده , , Pascal، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2014
Abstract :
The ability to predict the behavior of Escherichia coli O157:H7 on contaminated field lettuce is essential for the development of accurate quantitative microbial risk assessments. The survival pattern of the species was assessed from several data sets derived from field-based experiments, which were analyzed by regression analysis fitting one monophasic model (log-linear) and two biphasic (Weibull and Cerf´s model) models. Probabilistic models were also simulated with @RISK™, integrating the fitted monophasic and biphasic models in order to analyze their impact on the estimate of the extent of die-off subsequent to a contamination event in the field. Regression analysis indicated that E. coli O157:H7 followed a biphasic decay pattern in most cases, with the Weibull and Cerf´s model showing similar good fit to individual and pooled survival data. Furthermore, results from the stochastic analysis demonstrated that using the log-linear model could lead to different risk estimates from those obtained with biphasic models, with a lower prevalence in the former scenario as no tailing is assumed in this model. The models and results derived from this work provide the first suitable mathematical base upon which to build probabilistic models to predict the fate of E. coli O157:H7 on field-grown leafy green vegetable.
Keywords :
Quantitative risk assessment , Biphasic model , predictive model , field lettuce , E. coli O157:H7
Journal title :
International Journal of Food Microbiology
Journal title :
International Journal of Food Microbiology