The Annals of Applied Statistics

Bayesian modeling of bacterial growth for multiple populations

A. Paula Palacios, J. Miguel Marín, Emiliano J. Quinto, and Michael P. Wiper

Full-text: Open access

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.

Article information

Source
Ann. Appl. Stat., Volume 8, Number 3 (2014), 1516-1537.

Dates
First available in Project Euclid: 23 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.aoas/1414091223

Digital Object Identifier
doi:10.1214/14-AOAS720

Mathematical Reviews number (MathSciNet)
MR3271342

Zentralblatt MATH identifier
1304.62137

Keywords
Bacterial population modeling growth functions neural networks Bayesian inference

Citation

Palacios, A. Paula; Marín, J. Miguel; Quinto, Emiliano J.; Wiper, Michael P. Bayesian modeling of bacterial growth for multiple populations. Ann. Appl. Stat. 8 (2014), no. 3, 1516--1537. doi:10.1214/14-AOAS720. https://projecteuclid.org/euclid.aoas/1414091223


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