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Specific Growth Rate Determines the Sensitivity of Escherichia coli to Lactic Acid Stress: Implications for Predictive Microbiology

This study tested the hypothesis that sensitivity of Escherichia coli to lactic acid at concentrations relevant for fermented sausages (pH 4.6, 150 mM lactic acid, a (w) = 0.92, temperature = 20 or 27°C) increases with increasing growth rate. For E. coli strain 683 cultured in TSB in chemostat or ba...

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Detalles Bibliográficos
Autores principales: Lindqvist, Roland, Barmark, Gunilla
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4109666/
https://www.ncbi.nlm.nih.gov/pubmed/25110680
http://dx.doi.org/10.1155/2014/471317
Descripción
Sumario:This study tested the hypothesis that sensitivity of Escherichia coli to lactic acid at concentrations relevant for fermented sausages (pH 4.6, 150 mM lactic acid, a (w) = 0.92, temperature = 20 or 27°C) increases with increasing growth rate. For E. coli strain 683 cultured in TSB in chemostat or batch, subsequent inactivation rates when exposed to lactic acid stress increased with increasing growth rate at harvest. A linear relationship between growth rate at harvest and inactivation rate was found to describe both batch and chemostat cultures. The maximum difference in T(90), the estimated times for a one-log reduction, was 10 hours between bacteria harvested during the first 3 hours of batch culture, that is, at different growth rates. A 10-hour difference in T(90) would correspond to measuring inactivation at 33°C or 45°C instead of 37°C based on relationships between temperature and inactivation. At similar harvest growth rates, inactivation rates were lower for bacteria cultured at 37°C than at 15–20°C. As demonstrated for E. coli 683, culture conditions leading to variable growth rates may contribute to variable lactic acid inactivation rates. Findings emphasize the use and reporting of standardised culture conditions and can have implications for the interpretation of data when developing inactivation models.