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Bayesian modelling of bacterial growth for multiple populations

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dc.affiliation.dptoUC3M. Departamento de Estadísticaes
dc.contributor.authorPalacios, Ana Paula
dc.contributor.authorMarín Díazaraque, Juan Miguel
dc.contributor.authorQuinto, Emiliano
dc.contributor.authorWiper, Michael Peter
dc.contributor.editorUniversidad Carlos III de Madrid. Departamento de Estadística
dc.date.accessioned2012-06-28T07:44:22Z
dc.date.available2012-06-28T07:44:22Z
dc.date.issued2012-06
dc.description.abstractBacterial 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.
dc.format.mimetypeapplication/pdf
dc.identifier.repecws121610
dc.identifier.urihttps://hdl.handle.net/10016/14739
dc.identifier.uxxiDT/0000000968
dc.language.isoeng
dc.relation.ispartofseriesUC3M Working papers. Statistics and Econometrics
dc.relation.ispartofseries12-10
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subject.ecienciaEstadística
dc.subject.otherBacterial population modeling
dc.subject.otherGrowth functions
dc.subject.otherNeural networks
dc.subject.otherBayesian inference
dc.titleBayesian modelling of bacterial growth for multiple populations
dc.typeworking paper*
dc.type.hasVersionSMUR*
dspace.entity.typePublication
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