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Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling
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| Title: | Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling |
| Authors: | Koyama, Kento Browse this author | | Aspridou, Zafiro Browse this author | | Koseki, Shige Browse this author →KAKEN DB | | Koutsoumanis, Konstantinos Browse this author |
| Keywords: | uncertainty | | predictive microbiology | | Bayesian | | bacterial inactivation | | global regression model |
| Issue Date: | 25-Sep-2019 |
| Publisher: | Frontiers Media |
| Journal Title: | Frontiers in microbiology |
| Volume: | 10 |
| Start Page: | 2239 |
| Publisher DOI: | 10.3389/fmicb.2019.02239 |
| PMID: | 31681187 |
| Abstract: | 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 10⁷ CFU/ml at 65℃, 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. |
| Rights: | https://creativecommons.org/licenses/by/4.0/ |
| Type: | article |
| URI: | http://hdl.handle.net/2115/76041 |
| Appears in Collections: | 農学院・農学研究院 (Graduate School of Agriculture / Faculty of Agriculture) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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