  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. First, 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 feedforward neural networks to model the influence of these environmental
factors on the growth parameters. Second, 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.
