Bayesian global regression model relating product characteristics of intermediate moisture food products to heat inactivation parameters for Salmonella Napoli and Eurotium herbariorum mould spores.
Inactivation
Modeling
Predictive microbiology
Spoilage
Journal
International journal of food microbiology
ISSN: 1879-3460
Titre abrégé: Int J Food Microbiol
Pays: Netherlands
ID NLM: 8412849
Informations de publication
Date de publication:
02 Jun 2022
02 Jun 2022
Historique:
received:
30
06
2021
revised:
03
03
2022
accepted:
19
03
2022
pubmed:
5
4
2022
medline:
27
4
2022
entrez:
4
4
2022
Statut:
ppublish
Résumé
Thermal inactivation of pathogenic and spoilage organisms in low and intermediate moisture foods is of critical importance for guaranteeing microbiological safety and stability of these products. Producers tendentially reduce salt in low and intermediate moisture foods because of nutritional health considerations, but it is unclear how this affects microbial inactivation rates during pasteurization. In this study we predict the time to achieve a pre-defined 6-log reduction for Salmonella enterica subsp. enterica serovar Napoli (hereafter: S. Napoli) and Eurotium herbariorum mould spores (hereafter: E. herbariorum spores) and the relationship with product characteristics. We tested 31 design products for heat inactivation of S. Napoli and 29 design products for heat inactivation of E. herbariorum spores. We used a global Bayesian regression combining primary Weibull models with a secondary regression model to relate pasteurization temperature and product characteristics (water activity (a
Identifiants
pubmed: 35378381
pii: S0168-1605(22)00109-X
doi: 10.1016/j.ijfoodmicro.2022.109638
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
109638Informations de copyright
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