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Survival of Listeria monocytogenes and Staphylococcus aureus in Synthetic Brines. Studying the Effects of Salt, Temperature and Sugar through the Approach of the Design of Experiments

The fermentation of table olives relies on a complex microbiota of lactic acid bacteria (LAB), yeasts, and enterobacteria. Producers often add sugar to increase the growth rate of LAB, “but this practice could also increase the survival rate of some pathogens. Therefore, the main topic of this paper...

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Detalles Bibliográficos
Autores principales: Bevilacqua, Antonio, Campaniello, Daniela, Speranza, Barbara, Sinigaglia, Milena, Corbo, Maria R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5819215/
https://www.ncbi.nlm.nih.gov/pubmed/29497414
http://dx.doi.org/10.3389/fmicb.2018.00240
Descripción
Sumario:The fermentation of table olives relies on a complex microbiota of lactic acid bacteria (LAB), yeasts, and enterobacteria. Producers often add sugar to increase the growth rate of LAB, “but this practice could also increase the survival rate of some pathogens. Therefore, the main topic of this paper was to study the effect of sugar, salt and temperature on the survival of Staphylococcus aureus and Listeria monocytogenes in a synthetic brine through the theory of the Design of Experiments (simplex centroid). The addition of sugar could prolong the survival time of L. monocytogenes by 40 days, whereas an increase of the temperature caused a decrease of survival from 18 to 3 days. The survival time of S. aureus was prolonged by 50 days by combining sugar (2–4 g/l) and low temperatures (5–15°C). The use of desirability approach and prediction profiles suggests that the prolongation of the survival time of L. monocytogenes could be related to a shift in the geometrical shape of the death kinetic. This paper offers a structured statistical approach on the variables acting on the survival of two pathogens in brines and represents the first step to set up and design a predictive approach for olive producers.