Publication: Bayesian hierarchical modelling of bacteria growth
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2010-04
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Abstract
Bacterial growth models are commonly used in food safety. Such models permit the
prediction of microbial safety and the shelf life of perishable foods. In this paper, we
study the problem of modelling bacterial growth when we observe multiple
experimental results under identical environmental conditions. We develop a
hierarchical version of the Gompertz equation to take into account the possibility of
replicated experiments and we show how it can be fitted using a fully Bayesian
approach. This approach is illustrated using experimental data from Listeria
monocytogenes growth and the results are compared with alternative models. Model
selection is undertaken throughout using an appropriate version of the deviance
information criterion and the posterior predictive loss criterion. Models are fitted using
WinBUGS via R2WinBUGS.
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Predictive microbiology, Growth models, Gompertz curve, Bayesian hierarchical modelling