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Bayesian modelling of bacterial growth for multiple populations

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  • Palacios, Ana Paula
  • Wiper, Michael Peter
  • Marín Díazaraque, Juan Miguel
  • Quinto, Emiliano

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.

Suggested Citation

  • Palacios, Ana Paula & Wiper, Michael Peter & Marín Díazaraque, Juan Miguel & Quinto, Emiliano, 2012. "Bayesian modelling of bacterial growth for multiple populations," DES - Working Papers. Statistics and Econometrics. WS ws121610, Universidad Carlos III de Madrid. Departamento de Estadística.
  • Handle: RePEc:cte:wsrepe:ws121610
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    References listed on IDEAS

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    1. repec:dau:papers:123456789/1124 is not listed on IDEAS
    2. Robert, Christian P. & Mengersen, Kerrie L., 1999. "Reparameterisation Issues in Mixture Modelling and their bearing on MCMC algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 29(3), pages 325-343, January.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika van der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639.
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    Keywords

    Bayesian inference;

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