DEVELOPMENT OF A PREDICTIVE MODEL DESCRIBING THE GROWTH OF CRONOBACTER SAKAZAKII IN RECONSTITUTED POWDERED INFANT MILK FORMULA

Abstract

ABSTRACTCronobacter sakazakii has been associated most frequently with illness in neonates, so a mathematical model was developed to predict the growth rate of C. sakazakii in infant milk based on temperature. Reconstituted powdered infant milk formulas (RIMFs) inoculated with C. sakazakii were incubated at 10, 20, 30 and 40C. The primary model showed a good fit (r2 = 0.9613–0.999) to a Gompertz equation to obtain growth rates and lag times (LTs) at each temperature. The specific growth rate (SGR) of C. sakazakii in the RIMF increased, and the LT decreased with increasing temperature. A secondary polynomial model was developed using SAS general linear analysis software. The secondary model was “ln SGR = −0.06581 + (0.00575 × temperature) + (0.00039 × temperature2).” The SGR predicted using this model increased with an increasing temperature. This secondary polynomial model was judged as appropriate based on the mean square error (MSE of the SGR model = 0.0002), the coefficient of determination (r2 of the SGR model = 0.9975), the bias factor (Bf of the SGR model = 1.0375) and the accuracy factor (Af of the SGR model = 1.0724). Reliable prediction of C. sakazakii growth rates in RIMF was based on temperature.PRACTICAL APPLICATIONSPredictive models allow quantitative estimation of microorganism growth. Predicted specific growth rates (SGRs) using our secondary model were similar to measured SGRs, and evaluation of mathematical/statistical adequacy of the predictive model showed reliable results (r2 = 0.9975, MSE = 0.0002, Bf = 1.0375, Af = 1.0724). This model may be of use to dairy producers for manufacture of safe products by controlling Cronobacter sakazakii growth without the need for detection of the organism.