Predictive models for the growth of Cronobacter sakazakii in reconstituted powdered infant formula

摘要

Cronobacter sakazakii has been implicated in foodborne illnesses in neonates and infants resulting from the consumption of contaminated infant formula. The objective of this research was to develop predictive models for the growth of C. sakazakii in infant milk formula (IMF) and infant soy formula (ISF). Growth kinetics for a five strain cocktail of C. sakazakii were obtained at several isothermal conditions at 8.5,10,15,20, 25,28,32,35,37,40,42,45, and 47 °C in reconstituted IMF and ISF. Initial protocol resulted in clumping of colonies leading to difficultly in enumerating C. sakazakii. Protocol was then modified by addition of Tween-80 and stomaching the samples, which resulted in breaking up of colonies effectively. The growth data were fittedto three primary models (Baranyi, Gompertz, and Logistic) to describe the growth of C. sakazakii at each isothermal condition. For IMF, the psuedo-R2 and the root mean square error (RMSE) ranged from 0.96-0.99 and 0.07-0.34 log CFU/ mL, respectively. ForISF, the psuedo-R2 and the RMSE ranged from 0.98-0.99 and 0.08-0.27 log CFU/mL, respectively. Two different secondary models were used to describe the effect of temperature on growth rate of C. sakazakii for each product. For the modified Ratkowsky's equation, psuedo-R2 and the RMSE values were 0.99 and 0.004- 0.0169 (log CFU/mL)/h, respectively.For the Gamma model, psuedo-R2 and the RMSE values were 0.99 and 0.004-0.006 (logCFU/mL)/h, respectively. C. sakazakii grew faster in IMF, when compared to ISF. Primary and secondary models were integrated and solved numerically to determine the growth of C. sakazakii at varying temperature profiles. Six dynamic models were validated with one sinusoidal and three 'real-life' temperature profdes. The dynamic models from Baranyi (RMSE ranging from 0.12-0.39 log CFU/mL) and logistic models (RMSE ranging from 0.25-0.79 log CFU/mL) predicted C. sakazakii growth better, compared to the Gompertz dynamic models (RMSE ranging from 0.46-0.67 log CFU/mL). These predictive models can help improve microbial risk assessment and develop appropriate risk management strategies.