Publication: Bayesian modelling of bacterial growth for multiple populations
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2012-06
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Abstract
Bacterial growth models are commonly used for the prediction of microbial safety and
the shelf life of perishable foods. Growth is affected by several environmental factors
such as temperature, acidity level and salt concentration. In this study, we develop two
models to describe bacterial growth for multiple populations under both equal and
different environmental conditions. Firstly, a semi-parametric model based on the
Gompertz equation is proposed. Assuming that the parameters of the Gompertz
equation may vary in relation to the running conditions under which the experiment is
performed, we use feed forward neural networks to model the influence of these
environmental factors on the growth parameters. Secondly, we propose a more general
model which does not assume any underlying parametric form for the growth function.
Thus, we consider a neural network as a primary growth model which includes the
influencing environmental factors as inputs to the network. One of the main
disadvantages of neural networks models is that they are often very difficult to tune
which complicates fitting procedures. Here, we show that a simple, Bayesian approach
to fitting these models can be implemented via the software package WinBugs. Our
approach is illustrated using real experimental Listeria Monocytogenes growth data.
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Keywords
Bacterial population modeling, Growth functions, Neural networks, Bayesian inference