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September 2014 Bayesian modeling of bacterial growth for multiple populations
A. Paula Palacios, J. Miguel Marín, Emiliano J. Quinto, Michael P. Wiper
Ann. Appl. Stat. 8(3): 1516-1537 (September 2014). DOI: 10.1214/14-AOAS720

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. 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.

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A. Paula Palacios. J. Miguel Marín. Emiliano J. Quinto. Michael P. Wiper. "Bayesian modeling of bacterial growth for multiple populations." Ann. Appl. Stat. 8 (3) 1516 - 1537, September 2014. https://doi.org/10.1214/14-AOAS720

Information

Published: September 2014
First available in Project Euclid: 23 October 2014

zbMATH: 1304.62137
MathSciNet: MR3271342
Digital Object Identifier: 10.1214/14-AOAS720

Keywords: Bacterial population modeling , Bayesian inference , growth functions , neural networks

Rights: Copyright © 2014 Institute of Mathematical Statistics

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Vol.8 • No. 3 • September 2014
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