Bayesian global regression model relating product characteristics of intermediate moisture food products to heat inactivation parameters for Salmonella Napoli and Eurotium herbariorum mould spores.

Smid, J H; van der Swaluw-Dekker, C D M; Ueckert, J; de Vries, E; Pielaat, A

Smid, J H; van der Swaluw-Dekker, C D M; Ueckert, J; de Vries, E; Pielaat, A.

Afiliação

Smid JH; Unilever R&D, Wageningen, the Netherlands. Electronic address: joost.smid@unilever.com.
van der Swaluw-Dekker CDM; Unilever R&D, Wageningen, the Netherlands.
Ueckert J; Unilever R&D, Wageningen, the Netherlands.
de Vries E; Unilever R&D, Wageningen, the Netherlands.
Pielaat A; Unilever R&D, Wageningen, the Netherlands.

Int J Food Microbiol ; 370: 109638, 2022 Jun 02.

Article em En | MEDLINE | ID: mdl-35378381

Assuntos

Microbiologia de Alimentos; Temperatura Alta; Aspergillus; Teorema de Bayes; Contagem de Colônia Microbiana; Salmonella/fisiologia; Esporos Fúngicos

Palavras-chave

Inactivation; Modeling; Predictive microbiology; Spoilage

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Microbiologia de Alimentos / Temperatura Alta Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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