Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values

Detalhes bibliográficos
Autor(a) principal: Gonçalves,Letícia Dias dos Anjos
Data de Publicação: 2017
Outros Autores: Piccoli,Roberta Hilsdorf, Peres,Alexandre de Paula, Saúde,André Vital
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Brazilian Journal of Microbiology
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-83822017000200352
Resumo: Abstract Meat is one of the most perishable foods owing to its nutrient availability, high water activity, and pH around 5.6. These properties are highly conducive for microbial growth. Fresh meat, when exposed to oxygen, is subjected to the action of aerobic psychrotrophic, proteolytic, and lipolytic spoilage microorganisms, such as Pseudomonas spp. The spoilage results in the appearance of slime and off-flavor in food. In order to predict the growth of Pseudomonas fluorescens in fresh meat at different pH values, stored under refrigeration, and temperature abuse, microbial mathematical modeling was applied. The primary Baranyi and Roberts and the modified Gompertz models were fitted to the experimental data to obtain the growth parameters. The Ratkowsky extended model was used to determine the effect of pH and temperature on the growth parameter µmax. The program DMFit 3.0 was used for model adjustment and fitting. The experimental data showed good fit for both the models tested, and the primary and secondary models based on the Baranyi and Roberts models showed better validation. Thus, these models can be applied to predict the growth of P. fluorescens under the conditions tested.
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spelling Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH valuesMeatDeteriorationModelingAbstract Meat is one of the most perishable foods owing to its nutrient availability, high water activity, and pH around 5.6. These properties are highly conducive for microbial growth. Fresh meat, when exposed to oxygen, is subjected to the action of aerobic psychrotrophic, proteolytic, and lipolytic spoilage microorganisms, such as Pseudomonas spp. The spoilage results in the appearance of slime and off-flavor in food. In order to predict the growth of Pseudomonas fluorescens in fresh meat at different pH values, stored under refrigeration, and temperature abuse, microbial mathematical modeling was applied. The primary Baranyi and Roberts and the modified Gompertz models were fitted to the experimental data to obtain the growth parameters. The Ratkowsky extended model was used to determine the effect of pH and temperature on the growth parameter µmax. The program DMFit 3.0 was used for model adjustment and fitting. The experimental data showed good fit for both the models tested, and the primary and secondary models based on the Baranyi and Roberts models showed better validation. Thus, these models can be applied to predict the growth of P. fluorescens under the conditions tested.Sociedade Brasileira de Microbiologia2017-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-83822017000200352Brazilian Journal of Microbiology v.48 n.2 2017reponame:Brazilian Journal of Microbiologyinstname:Sociedade Brasileira de Microbiologia (SBM)instacron:SBM10.1016/j.bjm.2016.12.006info:eu-repo/semantics/openAccessGonçalves,Letícia Dias dos AnjosPiccoli,Roberta HilsdorfPeres,Alexandre de PaulaSaúde,André Vitaleng2017-05-11T00:00:00Zoai:scielo:S1517-83822017000200352Revistahttps://www.scielo.br/j/bjm/ONGhttps://old.scielo.br/oai/scielo-oai.phpbjm@sbmicrobiologia.org.br||mbmartin@usp.br1678-44051517-8382opendoar:2017-05-11T00:00Brazilian Journal of Microbiology - Sociedade Brasileira de Microbiologia (SBM)false
dc.title.none.fl_str_mv Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values
title Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values
spellingShingle Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values
Gonçalves,Letícia Dias dos Anjos
Meat
Deterioration
Modeling
title_short Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values
title_full Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values
title_fullStr Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values
title_full_unstemmed Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values
title_sort Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values
author Gonçalves,Letícia Dias dos Anjos
author_facet Gonçalves,Letícia Dias dos Anjos
Piccoli,Roberta Hilsdorf
Peres,Alexandre de Paula
Saúde,André Vital
author_role author
author2 Piccoli,Roberta Hilsdorf
Peres,Alexandre de Paula
Saúde,André Vital
author2_role author
author
author
dc.contributor.author.fl_str_mv Gonçalves,Letícia Dias dos Anjos
Piccoli,Roberta Hilsdorf
Peres,Alexandre de Paula
Saúde,André Vital
dc.subject.por.fl_str_mv Meat
Deterioration
Modeling
topic Meat
Deterioration
Modeling
description Abstract Meat is one of the most perishable foods owing to its nutrient availability, high water activity, and pH around 5.6. These properties are highly conducive for microbial growth. Fresh meat, when exposed to oxygen, is subjected to the action of aerobic psychrotrophic, proteolytic, and lipolytic spoilage microorganisms, such as Pseudomonas spp. The spoilage results in the appearance of slime and off-flavor in food. In order to predict the growth of Pseudomonas fluorescens in fresh meat at different pH values, stored under refrigeration, and temperature abuse, microbial mathematical modeling was applied. The primary Baranyi and Roberts and the modified Gompertz models were fitted to the experimental data to obtain the growth parameters. The Ratkowsky extended model was used to determine the effect of pH and temperature on the growth parameter µmax. The program DMFit 3.0 was used for model adjustment and fitting. The experimental data showed good fit for both the models tested, and the primary and secondary models based on the Baranyi and Roberts models showed better validation. Thus, these models can be applied to predict the growth of P. fluorescens under the conditions tested.
publishDate 2017
dc.date.none.fl_str_mv 2017-06-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-83822017000200352
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1517-83822017000200352
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1016/j.bjm.2016.12.006
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Microbiologia
publisher.none.fl_str_mv Sociedade Brasileira de Microbiologia
dc.source.none.fl_str_mv Brazilian Journal of Microbiology v.48 n.2 2017
reponame:Brazilian Journal of Microbiology
instname:Sociedade Brasileira de Microbiologia (SBM)
instacron:SBM
instname_str Sociedade Brasileira de Microbiologia (SBM)
instacron_str SBM
institution SBM
reponame_str Brazilian Journal of Microbiology
collection Brazilian Journal of Microbiology
repository.name.fl_str_mv Brazilian Journal of Microbiology - Sociedade Brasileira de Microbiologia (SBM)
repository.mail.fl_str_mv bjm@sbmicrobiologia.org.br||mbmartin@usp.br
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