Development of predictive model for the growth of Staphylococcus aureus in Kimbab

Abstract

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 o 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 (Bf), and accuracy factor (Af). The results showed that RMSE<0.20 and Bf and Af 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.