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Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling
Uncertainty analysis is the process of identifying limitations in scientific knowledge and evaluating their implications for scientific conclusions. In the context of microbial risk assessment, the uncertainty in the predicted microbial behavior can be an important component of the overall uncertain...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798057/ https://www.ncbi.nlm.nih.gov/pubmed/31681187 http://dx.doi.org/10.3389/fmicb.2019.02239 |
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author | Koyama, Kento Aspridou, Zafiro Koseki, Shige Koutsoumanis, Konstantinos |
author_facet | Koyama, Kento Aspridou, Zafiro Koseki, Shige Koutsoumanis, Konstantinos |
author_sort | Koyama, Kento |
collection | PubMed |
description | Uncertainty analysis is the process of identifying limitations in scientific knowledge and evaluating their implications for scientific conclusions. In the context of microbial risk assessment, the uncertainty in the predicted microbial behavior can be an important component of the overall uncertainty. Conventional deterministic modeling approaches which provide point estimates of the pathogen’s levels cannot quantify the uncertainty around the predictions. The objective of this study was to use Bayesian statistical modeling for describing uncertainty in predicted microbial thermal inactivation of Salmonella enterica Typhimurium DT104. A set of thermal inactivation data in broth with water activity adjusted to 0.75 at 9 different temperature conditions obtained from the ComBase database (www.combase.cc) was used. A log-linear microbial inactivation was used as a primary model while for secondary modeling, a linear relation between the logarithm of inactivation rate and temperature was assumed. For comparison, data were fitted with a two-step and a global Bayesian regression. Posterior distributions of model’s parameters were used to predict Salmonella thermal inactivation. The combination of the joint posterior distributions of model’s parameters allowed the prediction of cell density over time, total reduction time and inactivation rate as probability distributions at different time and temperature conditions. For example, for the time required to eliminate a Salmonella population of about 10(7) CFU/ml at 65°C, the model predicted a time distribution with a median of 0.40 min and 5th and 95th percentiles of 0.24 and 0.60 min, respectively. The validation of the model showed that it can describe successfully uncertainty in predicted thermal inactivation with most observed data being within the 95% prediction intervals of the model. The global regression approach resulted in less uncertain predictions compared to the two-step regression. The developed model could be used to quantify uncertainty in thermal inactivation in risk-based processing design as well as in risk assessment studies. |
format | Online Article Text |
id | pubmed-6798057 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-67980572019-11-01 Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling Koyama, Kento Aspridou, Zafiro Koseki, Shige Koutsoumanis, Konstantinos Front Microbiol Microbiology Uncertainty analysis is the process of identifying limitations in scientific knowledge and evaluating their implications for scientific conclusions. In the context of microbial risk assessment, the uncertainty in the predicted microbial behavior can be an important component of the overall uncertainty. Conventional deterministic modeling approaches which provide point estimates of the pathogen’s levels cannot quantify the uncertainty around the predictions. The objective of this study was to use Bayesian statistical modeling for describing uncertainty in predicted microbial thermal inactivation of Salmonella enterica Typhimurium DT104. A set of thermal inactivation data in broth with water activity adjusted to 0.75 at 9 different temperature conditions obtained from the ComBase database (www.combase.cc) was used. A log-linear microbial inactivation was used as a primary model while for secondary modeling, a linear relation between the logarithm of inactivation rate and temperature was assumed. For comparison, data were fitted with a two-step and a global Bayesian regression. Posterior distributions of model’s parameters were used to predict Salmonella thermal inactivation. The combination of the joint posterior distributions of model’s parameters allowed the prediction of cell density over time, total reduction time and inactivation rate as probability distributions at different time and temperature conditions. For example, for the time required to eliminate a Salmonella population of about 10(7) CFU/ml at 65°C, the model predicted a time distribution with a median of 0.40 min and 5th and 95th percentiles of 0.24 and 0.60 min, respectively. The validation of the model showed that it can describe successfully uncertainty in predicted thermal inactivation with most observed data being within the 95% prediction intervals of the model. The global regression approach resulted in less uncertain predictions compared to the two-step regression. The developed model could be used to quantify uncertainty in thermal inactivation in risk-based processing design as well as in risk assessment studies. Frontiers Media S.A. 2019-09-25 /pmc/articles/PMC6798057/ /pubmed/31681187 http://dx.doi.org/10.3389/fmicb.2019.02239 Text en Copyright © 2019 Koyama, Aspridou, Koseki and Koutsoumanis. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Microbiology Koyama, Kento Aspridou, Zafiro Koseki, Shige Koutsoumanis, Konstantinos Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling |
title | Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling |
title_full | Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling |
title_fullStr | Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling |
title_full_unstemmed | Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling |
title_short | Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling |
title_sort | describing uncertainty in salmonella thermal inactivation using bayesian statistical modeling |
topic | Microbiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6798057/ https://www.ncbi.nlm.nih.gov/pubmed/31681187 http://dx.doi.org/10.3389/fmicb.2019.02239 |
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