Describing Uncertainty in

Bayesian bacterial inactivation global regression model predictive microbiology uncertainty

Journal

Frontiers in microbiology
ISSN: 1664-302X
Titre abrégé: Front Microbiol
Pays: Switzerland
ID NLM: 101548977

Informations de publication

Date de publication:
2019
Historique:
received: 08 04 2019
accepted: 12 09 2019
entrez: 5 11 2019
pubmed: 5 11 2019
medline: 5 11 2019
Statut: epublish

Résumé

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

Identifiants

pubmed: 31681187
doi: 10.3389/fmicb.2019.02239
pmc: PMC6798057
doi:

Types de publication

Journal Article

Langues

eng

Pagination

2239

Informations de copyright

Copyright © 2019 Koyama, Aspridou, Koseki and Koutsoumanis.

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Auteurs

Kento Koyama (K)

Laboratory of Food Microbiology and Hygiene, Department of Food Science and Technology, School of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Graduate School of Agricultural Science, Hokkaido University, Sapporo, Japan.

Zafiro Aspridou (Z)

Laboratory of Food Microbiology and Hygiene, Department of Food Science and Technology, School of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki, Greece.

Shige Koseki (S)

Graduate School of Agricultural Science, Hokkaido University, Sapporo, Japan.

Konstantinos Koutsoumanis (K)

Laboratory of Food Microbiology and Hygiene, Department of Food Science and Technology, School of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki, Greece.

Classifications MeSH