An Accurate Method for Studying Individual Microbial Lag: Experiments and Computations

Variability in the behavior of microbial foodborne pathogens and spoilers causes difficulties in predicting the safety and quality of food products during their shelf life. Therefore, the quantification of the individual microbial lag phase distribution is of high relevance to the field of quantitat...

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Autores principales:	Simen Akkermans, Jan F. M. Van Impe
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	individual lag method development maximum likelihood estimation Monte Carlo simulation Escherichia coli Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/b18bc41adb0247478b938ccfbcc4c38a
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spelling	oai:doaj.org-article:b18bc41adb0247478b938ccfbcc4c38a2021-11-04T09:17:23ZAn Accurate Method for Studying Individual Microbial Lag: Experiments and Computations1664-302X10.3389/fmicb.2021.725499https://doaj.org/article/b18bc41adb0247478b938ccfbcc4c38a2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fmicb.2021.725499/fullhttps://doaj.org/toc/1664-302XVariability in the behavior of microbial foodborne pathogens and spoilers causes difficulties in predicting the safety and quality of food products during their shelf life. Therefore, the quantification of the individual microbial lag phase distribution is of high relevance to the field of quantitative microbial risk assessment. To construct models that predict the effect of changes in environmental conditions on the individual lag, an accurate determination of these distributions is required. Therefore, the current research focuses on the development of an experimental and computational method for accurate determination of individual lag phase distribution. The experimental method is unique in the sense that full liquid volumes are sampled without using dilutions to detect the final population, thereby minimizing experimental errors. Moreover, the method does not aim at the isolation of single cells but at a low number of cells. The fact that several cells can be present in the initial samples instead of having a single cell is considered by the computational method. This method relies on Monte Carlo simulation to predict the individual lag phase distribution for a given set of distribution parameters and maximum likelihood estimation to find the parameters that describe the experimental data best. The method was validated both through simulation and experiments and was found to deliver a desired accuracy.Simen AkkermansSimen AkkermansSimen AkkermansJan F. M. Van ImpeJan F. M. Van ImpeJan F. M. Van ImpeFrontiers Media S.A.articleindividual lagmethod developmentmaximum likelihood estimationMonte Carlo simulationEscherichia coliMicrobiologyQR1-502ENFrontiers in Microbiology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	individual lag method development maximum likelihood estimation Monte Carlo simulation Escherichia coli Microbiology QR1-502
spellingShingle	individual lag method development maximum likelihood estimation Monte Carlo simulation Escherichia coli Microbiology QR1-502 Simen Akkermans Simen Akkermans Simen Akkermans Jan F. M. Van Impe Jan F. M. Van Impe Jan F. M. Van Impe An Accurate Method for Studying Individual Microbial Lag: Experiments and Computations
description	Variability in the behavior of microbial foodborne pathogens and spoilers causes difficulties in predicting the safety and quality of food products during their shelf life. Therefore, the quantification of the individual microbial lag phase distribution is of high relevance to the field of quantitative microbial risk assessment. To construct models that predict the effect of changes in environmental conditions on the individual lag, an accurate determination of these distributions is required. Therefore, the current research focuses on the development of an experimental and computational method for accurate determination of individual lag phase distribution. The experimental method is unique in the sense that full liquid volumes are sampled without using dilutions to detect the final population, thereby minimizing experimental errors. Moreover, the method does not aim at the isolation of single cells but at a low number of cells. The fact that several cells can be present in the initial samples instead of having a single cell is considered by the computational method. This method relies on Monte Carlo simulation to predict the individual lag phase distribution for a given set of distribution parameters and maximum likelihood estimation to find the parameters that describe the experimental data best. The method was validated both through simulation and experiments and was found to deliver a desired accuracy.
format	article
author	Simen Akkermans Simen Akkermans Simen Akkermans Jan F. M. Van Impe Jan F. M. Van Impe Jan F. M. Van Impe
author_facet	Simen Akkermans Simen Akkermans Simen Akkermans Jan F. M. Van Impe Jan F. M. Van Impe Jan F. M. Van Impe
author_sort	Simen Akkermans
title	An Accurate Method for Studying Individual Microbial Lag: Experiments and Computations
title_short	An Accurate Method for Studying Individual Microbial Lag: Experiments and Computations
title_full	An Accurate Method for Studying Individual Microbial Lag: Experiments and Computations
title_fullStr	An Accurate Method for Studying Individual Microbial Lag: Experiments and Computations
title_full_unstemmed	An Accurate Method for Studying Individual Microbial Lag: Experiments and Computations
title_sort	accurate method for studying individual microbial lag: experiments and computations
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/b18bc41adb0247478b938ccfbcc4c38a
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An Accurate Method for Studying Individual Microbial Lag: Experiments and Computations

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