Bayesian modeling of Clostridium perfringens growth in beef-in-sauce products

Jaloustre, S.; Cornu, Marie; Morelli, E.; Noël, Véronique; Delignette-Muller, Marie Laure

doi:10.1016/j.fm.2010.04.002

Cited by 37 publications

(15 citation statements)

References 61 publications

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“…They have been used to assess microbial prevalence [49] and contamination level [50], inactivation of pathogens in food [51,52], and more generally in microbial exposure or risk assessment [48 ,53,54]. The lack of data is a source of uncertainty.…”

Section: Statistical and Probabilistic Techniques To Support Risk Assmentioning

confidence: 99%

Latest developments in foodborne pathogen risk assessment

Membré

Guillou

2016

Current Opinion in Food Science

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Section: Statistical and Probabilistic Techniques To Support Risk Assmentioning

confidence: 99%

Latest developments in foodborne pathogen risk assessment

Membré

Guillou

2016

Current Opinion in Food Science

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“…These stages can be stages along a supply chain or any other stages that are linked in one direction. For instance, Bayesian networks have been set up to study Clostridium perfringens in beef-in-sauce (Jaloustre et al 2011) and Listeria monocytogenes in cold-smoked salmon (Delignette-Muller et al 2006) as well as courgette puree (Rigaux Ancelet et al 2013). Bayesian belief networks have been combined with other approaches such as Monte Carlo simulation modeling (Smid et al 2010) and have been compared to Markov Chain Monte Carlo (MCMC) models in the context of a quantitative risk assessment study of poultry meat (Parsons et al 2005).…”

Section: Microbial Growth Models In Food Safety Researchmentioning

confidence: 99%

Modeling microbial growth and dynamics

Esser

Leveau

Meyer

2015

Appl Microbiol Biotechnol

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Modeling has become an important tool for widening our understanding of microbial growth in the context of applied microbiology and related to such processes as safe food production, wastewater treatment, bioremediation, or microbe-mediated mining. Various modeling techniques, such as primary, secondary and tertiary mathematical models, phenomenological models, mechanistic or kinetic models, reactive transport models, Bayesian network models, artificial neural networks, as well as agent-, individual-, and particle-based models have been applied to model microbial growth and activity in many applied fields. In this mini-review, we summarize the basic concepts of these models using examples and applications from food safety and wastewater treatment systems. We further review recent developments in other applied fields focusing on models that explicitly include spatial relationships. Using these examples, we point out the conceptual similarities across fields of application and encourage the combined use of different modeling techniques in hybrid models as well as their cross-disciplinary exchange. For instance, pattern-oriented modeling has its origin in ecology but may be employed to parameterize microbial growth models when experimental data are scarce. Models could also be used as virtual laboratories to optimize experimental design analogous to the virtual ecologist approach. Future microbial growth models will likely become more complex to benefit from the rich toolbox that is now available to microbial growth modelers.

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“…A previous study conducted in a hospital central kitchen by the authors (Jaloustre, Cornu, Morelli, Noel, , & Delignette, 2011), highlighted that temperature of foodstuff were often above 10 C when these are taken out of the blast-chillers. The practices observation in this establishment has highlighted that the temperature recorded at the end of rapid cooling was largely conditioned by the manner in which it was measured by the operators.…”

Section: Introductionmentioning

confidence: 98%