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A New Approach to Predict the Fish Fillet Shelf-Life in Presence of Natural Preservative Agents.
Giuffrida, Alessandro; Giarratana, Filippo; Valenti, Davide; Muscolino, Daniele; Parisi, Roberta; Parco, Alessio; Marotta, Stefania; Ziino, Graziella; Panebianco, Antonio.
Afiliação
  • Giuffrida A; Department of Veterinary Sciences, University of Messina, Palermo, Italy.
  • Giarratana F; Department of Veterinary Sciences, University of Messina, Palermo, Italy.
  • Valenti D; Group of Interdisciplinary Theoretical Physics and CNISM, Department of Physics and Chemistry, University of Palermo, Palermo, Italy.
  • Muscolino D; Department of Veterinary Sciences, University of Messina, Palermo, Italy.
  • Parisi R; Department of Veterinary Sciences, University of Messina, Palermo, Italy.
  • Parco A; Department of Veterinary Sciences, University of Messina, Palermo, Italy.
  • Marotta S; Department of Veterinary Sciences, University of Messina, Palermo, Italy.
  • Ziino G; Department of Veterinary Sciences, University of Messina, Palermo, Italy.
  • Panebianco A; Department of Veterinary Sciences, University of Messina, Palermo, Italy.
Ital J Food Saf ; 6(2): 6768, 2017 Apr 13.
Article em En | MEDLINE | ID: mdl-28713795
Three data sets concerning the behaviour of spoilage flora of fillets treated with natural preservative substances (NPS) were used to construct a new kind of mathematical predictive model. This model, unlike other ones, allows expressing the antibacterial effect of the NPS separately from the prediction of the growth rate. This approach, based on the introduction of a parameter into the predictive primary model, produced a good fitting of observed data and allowed characterising quantitatively the increase of shelf-life of fillets.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En 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 Ano de publicação: 2017 Tipo de documento: Article