Predictive Modeling of Staphylococcus aureus Growth on Gwamegi (semidry Pacific saury) as a Function of Temperature

Gwamegi (semidry Pacific saury [Cololabis saira]) is a Korean food made by a traditional method of repeated freezing and de-freezing during winter. The present study aimed at developing predictive modeling of S. aureus growth on Gwamegi as a function of temperature (10-35 degrees C). Modified Gompertz, Baranyi, and logistic primary models were fitted to experimental values. Polynomial quadratic, nonlinear Arrhenius and square root models were selected as secondary models and analyzed using specific growth rate (mu(max)) and lag time (lambda) values obtained from the primary models. Based on the optimized models derived from the Baranyi and square root equations for mu(max), its r(2) and mean square error (MSE) were 0.991 and 0.00058, and bias factor (B-f) and accuracy factor (A(f)) were 1.0087 and 1.0801, respectively. The logistic and polynomial quadratic equations for lambda, its r(2) and MSE were 0.989 and 0.22834, B-f and A(f) were 0.9742 and 1.0271, respectively. These predictive models can provide basic information for quantitative microbial risk assessment of Gwamegi and other processed semidried seafood.