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A Predictive Model for the Growth of Listeria monocytogenes in Commercial Blue Crab (Callinectes sapidus).
Parveen, Salina; White, Channel; Tamplin, Mark L.
Afiliação
  • Parveen S; Department of Agriculture, Food and Resource Sciences, University of Maryland Eastern Shore, Princess Anne, Maryland 21853, USA.
  • White C; Department of Agriculture, Food and Resource Sciences, University of Maryland Eastern Shore, Princess Anne, Maryland 21853, USA.
  • Tamplin ML; Food Safety Centre, Tasmanian Institute of Agriculture, University of Tasmania, Hobart, Tasmania, Australia 7001.
J Food Prot ; 80(11): 1872-1876, 2017 Nov 01.
Article em En | MEDLINE | ID: mdl-29028360
ABSTRACT
During the processing and handling of commercial blue crab (Callinectes sapidus), Listeria monocytogenes can potentially contaminate cooked meat and grow to hazardous levels. To manage this risk, predictive models are useful tools for designing and implementing preventive controls; however, no model specific for blue crab meat has been published or evaluated. In this study, a cocktail of L. monocytogenes strains was added to pasteurized blue crab meat, which was incubated at storage temperatures from 0 to 35°C. At selected time intervals, L. monocytogenes was enumerated by direct plating onto modified Oxford agar. A primary model was fitted to kinetic data to estimate the lag-phase duration (LPD) and growth rate (GR). Listeria monocytogenes replicated from 0 to 35°C, with GR ranging from 0.004 to 0.518 log CFU/h. Overall, the LPD decreased with increasing temperature, displaying a maximum value of 187 h at 0°C; however, this trend was not consistent. The LPD was not detected at 10°C, and it occurred inconsistently from trial to trial. A secondary GR model (R2 = 0.9892) for pasteurized crab meat was compared with the L. monocytogenes GR in fresh crab meat, demonstrating bias and accuracy factors of 0.98 and 1.36, respectively. The model estimates varied from other published data and models, especially at temperatures ≥5°C, supporting the need for a specific predictive tool for temperature deviations.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Food Prot Ano de publicação: 2017 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: J Food Prot Ano de publicação: 2017 Tipo de documento: Article