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Omnibus Modeling of Listeria monocytogenes Growth Rates at Low Temperatures

Listeria monocytogenes is a pathogen of considerable public health importance with a high case fatality. L. monocytogenes can grow at refrigeration temperatures and is of particular concern for ready-to-eat foods that require refrigeration. There is substantial interest in conducting and modeling sh...

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Autores principales: Pennone, Vincenzo, Barron, Ursula-Gonzales, Hunt, Kevin, Cadavez, Vasco, McAuliffe, Olivia, Butler, Francis
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8156314/
https://www.ncbi.nlm.nih.gov/pubmed/34063480
http://dx.doi.org/10.3390/foods10051099
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author Pennone, Vincenzo
Barron, Ursula-Gonzales
Hunt, Kevin
Cadavez, Vasco
McAuliffe, Olivia
Butler, Francis
author_facet Pennone, Vincenzo
Barron, Ursula-Gonzales
Hunt, Kevin
Cadavez, Vasco
McAuliffe, Olivia
Butler, Francis
author_sort Pennone, Vincenzo
collection PubMed
description Listeria monocytogenes is a pathogen of considerable public health importance with a high case fatality. L. monocytogenes can grow at refrigeration temperatures and is of particular concern for ready-to-eat foods that require refrigeration. There is substantial interest in conducting and modeling shelf-life studies on L. monocytogenes, especially relating to storage temperature. Growth model parameters are generally estimated from constant-temperature growth experiments. Traditionally, first-order and second-order modeling (or primary and secondary) of growth data has been done sequentially. However, omnibus modeling, using a mixed-effects nonlinear regression approach, can model a full dataset covering all experimental conditions in one step. This study compared omnibus modeling to conventional sequential first-order/second-order modeling of growth data for five strains of L. monocytogenes. The omnibus model coupled a Huang primary model for growth with secondary models for growth rate and lag phase duration. First-order modeling indicated there were small significant differences in growth rate depending on the strain at all temperatures. Omnibus modeling indicated smaller differences. Overall, there was broad agreement between the estimates of growth rate obtained by the first-order and omnibus modeling. Through an appropriate choice of fixed and random effects incorporated in the omnibus model, potential errors in a dataset from one environmental condition can be identified and explored.
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spelling pubmed-81563142021-05-28 Omnibus Modeling of Listeria monocytogenes Growth Rates at Low Temperatures Pennone, Vincenzo Barron, Ursula-Gonzales Hunt, Kevin Cadavez, Vasco McAuliffe, Olivia Butler, Francis Foods Article Listeria monocytogenes is a pathogen of considerable public health importance with a high case fatality. L. monocytogenes can grow at refrigeration temperatures and is of particular concern for ready-to-eat foods that require refrigeration. There is substantial interest in conducting and modeling shelf-life studies on L. monocytogenes, especially relating to storage temperature. Growth model parameters are generally estimated from constant-temperature growth experiments. Traditionally, first-order and second-order modeling (or primary and secondary) of growth data has been done sequentially. However, omnibus modeling, using a mixed-effects nonlinear regression approach, can model a full dataset covering all experimental conditions in one step. This study compared omnibus modeling to conventional sequential first-order/second-order modeling of growth data for five strains of L. monocytogenes. The omnibus model coupled a Huang primary model for growth with secondary models for growth rate and lag phase duration. First-order modeling indicated there were small significant differences in growth rate depending on the strain at all temperatures. Omnibus modeling indicated smaller differences. Overall, there was broad agreement between the estimates of growth rate obtained by the first-order and omnibus modeling. Through an appropriate choice of fixed and random effects incorporated in the omnibus model, potential errors in a dataset from one environmental condition can be identified and explored. MDPI 2021-05-15 /pmc/articles/PMC8156314/ /pubmed/34063480 http://dx.doi.org/10.3390/foods10051099 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Pennone, Vincenzo
Barron, Ursula-Gonzales
Hunt, Kevin
Cadavez, Vasco
McAuliffe, Olivia
Butler, Francis
Omnibus Modeling of Listeria monocytogenes Growth Rates at Low Temperatures
title Omnibus Modeling of Listeria monocytogenes Growth Rates at Low Temperatures
title_full Omnibus Modeling of Listeria monocytogenes Growth Rates at Low Temperatures
title_fullStr Omnibus Modeling of Listeria monocytogenes Growth Rates at Low Temperatures
title_full_unstemmed Omnibus Modeling of Listeria monocytogenes Growth Rates at Low Temperatures
title_short Omnibus Modeling of Listeria monocytogenes Growth Rates at Low Temperatures
title_sort omnibus modeling of listeria monocytogenes growth rates at low temperatures
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8156314/
https://www.ncbi.nlm.nih.gov/pubmed/34063480
http://dx.doi.org/10.3390/foods10051099
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