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This study was conducted to develop predictive models for the growth of Staphylococcus aureus in kimbab as a function of storage temperatures (7, 10, 12, 14, 16, 20, 25, and 30°C). The growth data were fitted into the modified Gompertz model and the Logistic model, and the goodness-of-fit of primary models was compared using determination of coefficient, mean square error, and Akaike's information criterion. The modified Gompertz model was found to be more suitable to describe the growth data. Therefore, the growth rate (GR) and lag time (LT) obtained from the modified Gompertz model were employed to establish the secondary models. The newly developed models were validated using root mean square error (RMSE), bias factor (B), and accuracy factor (A). The results showed that RMSE<0.20 and B and A values were within the reliable range, which indicated that the presented predictive models can be used to assess the risk of S. aureus infection in kimbab. [ABSTRACT FROM AUTHOR]

The growth of Staphylococcus aureus in sandwich fillings at different incubation temperatures was tested. These growth data were fitted into the Gompertz model, Logistic model, and Baranyi model in order to compare the goodness-of-fit of the 3 primary models using several factors such as coefficient of determination (R2), the standard deviation (Sy.x), and the Akaike’s information criterion (AIC). The Gompertz model showed the best statistical fit. Hence, growth parameters such as specific growth rate (SGR) and lag time (LT) obtained from the Gompertz model were used to construct the secondary models. Further, developed models were evaluated by bias factor (Bf) and accuracy factor (Af). For the SGR, the Bf value was 0.993 and Af value was 1.156 which indicated conservative predictions. While for LT, a clear deviation was observed between predictions and observations (Bf=0.635 and Af=1.592). The results, however, were also considered acceptable after comparing with previous publications. [ABSTRACT FROM AUTHOR]