Bayesian hierarchical modelling of bacteria growth

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.