Literature DB >> 19239977

Development of a hierarchical Bayesian model to estimate the growth parameters of Listeria monocytogenes in minimally processed fresh leafy salads.

Amélie Crépet1, Valérie Stahl, Frédéric Carlin.   

Abstract

The optimal growth rate mu(opt) of Listeria monocytogenes in minimally processed (MP) fresh leafy salads was estimated with a hierarchical Bayesian model at (mean+/-standard deviation) 0.33+/-0.16 h(-1). This mu(opt) value was much lower on average than that in nutrient broth, liquid dairy, meat and seafood products (0.7-1.3 h(-1)), and of the same order of magnitude as in cheese. Cardinal temperatures T(min), T(opt) and T(max) were determined at -4.5+/-1.3 degrees C, 37.1+/-1.3 degrees C and 45.4+/-1.2 degrees C respectively. These parameters were determined from 206 growth curves of L. monocytogenes in MP fresh leafy salads (lettuce including iceberg lettuce, broad leaf endive, curly leaf endive, lamb's lettuce, and mixtures of them) selected in the scientific literature and in technical reports. The adequacy of the model was evaluated by comparing observed data (bacterial concentrations at each experimental time for the completion of the 206 growth curves, mean log(10) increase at selected times and temperatures, L. monocytogenes concentrations in naturally contaminated MP iceberg lettuce) with the distribution of the predicted data generated by the model. The sensitivity of the model to assumptions about the prior values also was tested. The observed values mostly fell into the 95% credible interval of the distribution of predicted values. The mu(opt) and its uncertainty determined in this work could be used in quantitative microbial risk assessment for L. monocytogenes in minimally processed fresh leafy salads.

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Year:  2009        PMID: 19239977     DOI: 10.1016/j.ijfoodmicro.2009.01.028

Source DB:  PubMed          Journal:  Int J Food Microbiol        ISSN: 0168-1605            Impact factor:   5.277


  4 in total

1.  Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling.

Authors:  Kento Koyama; Zafiro Aspridou; Shige Koseki; Konstantinos Koutsoumanis
Journal:  Front Microbiol       Date:  2019-09-25       Impact factor: 5.640

2.  A Bayesian Approach to Describe and Simulate the pH Evolution of Fresh Meat Products Depending on the Preservation Conditions.

Authors:  Ngoc-Du Martin Luong; Louis Coroller; Monique Zagorec; Nicolas Moriceau; Valérie Anthoine; Sandrine Guillou; Jeanne-Marie Membré
Journal:  Foods       Date:  2022-04-13

3.  Bayesian Generalized Linear Model for Simulating Bacterial Inactivation/Growth Considering Variability and Uncertainty.

Authors:  Satoko Hiura; Hiroki Abe; Kento Koyama; Shige Koseki
Journal:  Front Microbiol       Date:  2021-06-24       Impact factor: 5.640

4.  Evaluation of Strain Variability in Inactivation of Campylobacter jejuni in Simulated Gastric Fluid by Using Hierarchical Bayesian Modeling.

Authors:  Kento Koyama; Jukka Ranta; Kohei Takeoka; Hiroki Abe; Shige Koseki
Journal:  Appl Environ Microbiol       Date:  2021-07-13       Impact factor: 4.792

  4 in total

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