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Establishment and Application of a Predictive Growth Kinetic Model of Salmonella with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork

To better guide microbial risk management and control, growth kinetic models of Salmonella with the coexistence of two other dominant background bacteria in pork were constructed. Sterilized pork cutlets were inoculated with a cocktail of Salmonella Derby (S. Derby), Pseudomonas aeruginosa (P. aerug...

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Autores principales: Zhao, Ge, Yang, Tengteng, Cheng, Huimin, Wang, Lin, Liu, Yunzhe, Gao, Yubin, Zhao, Jianmei, Liu, Na, Huang, Xiumei, Liu, Junhui, Zhang, Xiyue, Xu, Ying, Wang, Jun, Wang, Junwei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9696609/
https://www.ncbi.nlm.nih.gov/pubmed/36431773
http://dx.doi.org/10.3390/molecules27227673
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author Zhao, Ge
Yang, Tengteng
Cheng, Huimin
Wang, Lin
Liu, Yunzhe
Gao, Yubin
Zhao, Jianmei
Liu, Na
Huang, Xiumei
Liu, Junhui
Zhang, Xiyue
Xu, Ying
Wang, Jun
Wang, Junwei
author_facet Zhao, Ge
Yang, Tengteng
Cheng, Huimin
Wang, Lin
Liu, Yunzhe
Gao, Yubin
Zhao, Jianmei
Liu, Na
Huang, Xiumei
Liu, Junhui
Zhang, Xiyue
Xu, Ying
Wang, Jun
Wang, Junwei
author_sort Zhao, Ge
collection PubMed
description To better guide microbial risk management and control, growth kinetic models of Salmonella with the coexistence of two other dominant background bacteria in pork were constructed. Sterilized pork cutlets were inoculated with a cocktail of Salmonella Derby (S. Derby), Pseudomonas aeruginosa (P. aeruginosa), and Escherichia coli (E. coli), and incubated at various temperatures (4–37 °C). The predictive growth models were developed based on the observed growth data. By comparing R(2) of primary models, Baranyi models were preferred to fit the growth curves of S. Derby and P. aeruginosa, while the Huang model was preferred for E. coli (all R(2) ≥ 0.997). The secondary Ratkowsky square root model can well describe the relationship between temperature and μ(max) (all R(2) ≥ 0.97) or Lag (all R(2) ≥ 0.98). Growth models were validated by the actual test values, with B(f) and A(f) close to 1, and MSE around 0.001. The time for S. Derby to reach a pathogenic dose (10(5) CFU/g) at each temperature in pork was predicted accordingly and found to be earlier than the time when the pork began to be judged nearly fresh according to the sensory indicators. Therefore, the predictive microbiology model can be applied to more accurately predict the shelf life of pork to secure its quality and safety.
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spelling pubmed-96966092022-11-26 Establishment and Application of a Predictive Growth Kinetic Model of Salmonella with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork Zhao, Ge Yang, Tengteng Cheng, Huimin Wang, Lin Liu, Yunzhe Gao, Yubin Zhao, Jianmei Liu, Na Huang, Xiumei Liu, Junhui Zhang, Xiyue Xu, Ying Wang, Jun Wang, Junwei Molecules Article To better guide microbial risk management and control, growth kinetic models of Salmonella with the coexistence of two other dominant background bacteria in pork were constructed. Sterilized pork cutlets were inoculated with a cocktail of Salmonella Derby (S. Derby), Pseudomonas aeruginosa (P. aeruginosa), and Escherichia coli (E. coli), and incubated at various temperatures (4–37 °C). The predictive growth models were developed based on the observed growth data. By comparing R(2) of primary models, Baranyi models were preferred to fit the growth curves of S. Derby and P. aeruginosa, while the Huang model was preferred for E. coli (all R(2) ≥ 0.997). The secondary Ratkowsky square root model can well describe the relationship between temperature and μ(max) (all R(2) ≥ 0.97) or Lag (all R(2) ≥ 0.98). Growth models were validated by the actual test values, with B(f) and A(f) close to 1, and MSE around 0.001. The time for S. Derby to reach a pathogenic dose (10(5) CFU/g) at each temperature in pork was predicted accordingly and found to be earlier than the time when the pork began to be judged nearly fresh according to the sensory indicators. Therefore, the predictive microbiology model can be applied to more accurately predict the shelf life of pork to secure its quality and safety. MDPI 2022-11-08 /pmc/articles/PMC9696609/ /pubmed/36431773 http://dx.doi.org/10.3390/molecules27227673 Text en © 2022 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
Zhao, Ge
Yang, Tengteng
Cheng, Huimin
Wang, Lin
Liu, Yunzhe
Gao, Yubin
Zhao, Jianmei
Liu, Na
Huang, Xiumei
Liu, Junhui
Zhang, Xiyue
Xu, Ying
Wang, Jun
Wang, Junwei
Establishment and Application of a Predictive Growth Kinetic Model of Salmonella with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork
title Establishment and Application of a Predictive Growth Kinetic Model of Salmonella with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork
title_full Establishment and Application of a Predictive Growth Kinetic Model of Salmonella with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork
title_fullStr Establishment and Application of a Predictive Growth Kinetic Model of Salmonella with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork
title_full_unstemmed Establishment and Application of a Predictive Growth Kinetic Model of Salmonella with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork
title_short Establishment and Application of a Predictive Growth Kinetic Model of Salmonella with the Appearance of Two Other Dominant Background Bacteria in Fresh Pork
title_sort establishment and application of a predictive growth kinetic model of salmonella with the appearance of two other dominant background bacteria in fresh pork
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9696609/
https://www.ncbi.nlm.nih.gov/pubmed/36431773
http://dx.doi.org/10.3390/molecules27227673
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