RT Generic
T1 Bayesian modelling of bacterial growth for multiple populations
A1 Palacios, Ana Paula
A1 Marín Díazaraque, Juan Miguel
A1 Quinto, Emiliano
A1 Wiper, Michael Peter
A2 Universidad Carlos III de Madrid. Departamento de Estadística, 
AB Bacterial growth models are commonly used for the prediction of microbial safety andthe shelf life of perishable foods. Growth is affected by several environmental factorssuch as temperature, acidity level and salt concentration. In this study, we develop twomodels to describe bacterial growth for multiple populations under both equal anddifferent environmental conditions. Firstly, a semi-parametric model based on theGompertz equation is proposed. Assuming that the parameters of the Gompertzequation may vary in relation to the running conditions under which the experiment isperformed, we use feed forward neural networks to model the influence of theseenvironmental factors on the growth parameters. Secondly, we propose a more generalmodel 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 theinfluencing environmental factors as inputs to the network. One of the maindisadvantages of neural networks models is that they are often very difficult to tunewhich complicates fitting procedures. Here, we show that a simple, Bayesian approachto fitting these models can be implemented via the software package WinBugs. Ourapproach is illustrated using real experimental Listeria Monocytogenes growth data.
YR 2012
FD 2012-06
LK https://hdl.handle.net/10016/14739
UL https://hdl.handle.net/10016/14739
LA eng
DS e-Archivo
RD 18 may. 2024