Because of the substantial role of Staphylococcus aureus in hygiene and safety of raw milk consumption and artisanal cheese production, a characterisation of lag phase (λ) and maximum specific growth rate (µrate) of fifty Staphylococcus aureus strains conserved at 11.5 °C in milk is ongoing. Preliminary results on nineteen strains shown that µrate ranges from 0.041 to 0.047 LogCFU/ml*h-1, but high variability in λ is observed. Estimated values ranges from a minimum value of 0 to a maximum value of 109,9h. Lag phase is an important parameter in Quantitative Microbial Risk Assessment (QMRA) models and its variability should be considered, specially in case of multiple strain approach. The inclusion of λ variability in QMRA models can be indirectly achieved through the variability in physiological state of the cells (a0). From fitted cumulative distribution of a0 89% of observations are below 0.3, and 73% ranges from 0 to 0,1.
Predictive microbiology tools in multiple strain risk assessment approach, staphylococcus aureus in milk / M. Crotta, E. Cosciani Cunico, E. Dalzini, M.N. Losio, B. Bertasi, F. Paterlini, M. Luini, R. Rizzi, G. Varisco. ((Intervento presentato al convegno Food micro tenutosi a Nantes nel 2014.
Predictive microbiology tools in multiple strain risk assessment approach, staphylococcus aureus in milk
M. CrottaPrimo
;R. RizziPenultimo
;
2014
Abstract
Because of the substantial role of Staphylococcus aureus in hygiene and safety of raw milk consumption and artisanal cheese production, a characterisation of lag phase (λ) and maximum specific growth rate (µrate) of fifty Staphylococcus aureus strains conserved at 11.5 °C in milk is ongoing. Preliminary results on nineteen strains shown that µrate ranges from 0.041 to 0.047 LogCFU/ml*h-1, but high variability in λ is observed. Estimated values ranges from a minimum value of 0 to a maximum value of 109,9h. Lag phase is an important parameter in Quantitative Microbial Risk Assessment (QMRA) models and its variability should be considered, specially in case of multiple strain approach. The inclusion of λ variability in QMRA models can be indirectly achieved through the variability in physiological state of the cells (a0). From fitted cumulative distribution of a0 89% of observations are below 0.3, and 73% ranges from 0 to 0,1.Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.