Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFLA |
Texto Completo: | http://repositorio.ufla.br/jspui/handle/1/29531 |
Resumo: | 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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Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH valuesMeatDeteriorationModelingMeat 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 Microbiologia2018-07-03T11:33:02Z2018-07-03T11:33:02Z2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfGONÇALVES, L. D. dos A. et al. Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values. Brazilian Journal of Microbiology, São Paulo, v. 48, n. 2, Apr./June 2017.http://repositorio.ufla.br/jspui/handle/1/29531Brazilian Journal of Microbiologyreponame:Repositório Institucional da UFLAinstname:Universidade Federal de Lavras (UFLA)instacron:UFLAAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessGonçalves, Letícia Dias dos AnjosPiccoli, Roberta HilsdorfPeres, Alexandre de PaulaSaúde, André Vitaleng2023-05-03T13:04:24Zoai:localhost:1/29531Repositório InstitucionalPUBhttp://repositorio.ufla.br/oai/requestnivaldo@ufla.br || repositorio.biblioteca@ufla.bropendoar:2023-05-03T13:04:24Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA)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 |
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 2018-07-03T11:33:02Z 2018-07-03T11:33:02Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
GONÇALVES, L. D. dos A. et al. Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values. Brazilian Journal of Microbiology, São Paulo, v. 48, n. 2, Apr./June 2017. http://repositorio.ufla.br/jspui/handle/1/29531 |
identifier_str_mv |
GONÇALVES, L. D. dos A. et al. Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values. Brazilian Journal of Microbiology, São Paulo, v. 48, n. 2, Apr./June 2017. |
url |
http://repositorio.ufla.br/jspui/handle/1/29531 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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 reponame:Repositório Institucional da UFLA instname:Universidade Federal de Lavras (UFLA) instacron:UFLA |
instname_str |
Universidade Federal de Lavras (UFLA) |
instacron_str |
UFLA |
institution |
UFLA |
reponame_str |
Repositório Institucional da UFLA |
collection |
Repositório Institucional da UFLA |
repository.name.fl_str_mv |
Repositório Institucional da UFLA - Universidade Federal de Lavras (UFLA) |
repository.mail.fl_str_mv |
nivaldo@ufla.br || repositorio.biblioteca@ufla.br |
_version_ |
1807835125086420992 |