100A-6 |
Development of predictive models to estimate growth of Bacillus cereus at various temperatures in a broth medium and in boiled rice |
T. J. FANG, Food Science, National Chung Hsing University, 250 Kuokuang Road, Taichung, 40227, Taiwan and M. J. Hung. Bacillus cereus is a recognized pathogen and has been linked to approximately one third of the outbreaks of foodborne illness in Taiwan. Poor preparation practices and cross contamination between raw materials and cooked foods are major causes of B. cereus foodborne illness. Predictive modeling of microbial growth responses is becoming accepted as a means of reducing the amount of challenge testing required determining product safety. The objective was mathematical models, which allow the prediction of B. cereus growth in nutrient broth and in boiled rice, were investigated in this study. Growth curves of B. cereus were obtained at various temperatures (broth system: 10, 14, 18, 22.5, 25, 30, 34.5, and 37oC; boiled rice system; 12, 18, 24, 30, 36, and 42oC), followed by application of modified Gompertz equation to fit the growth curves. Ratkowsky and Zwietering model were used to describe maximum specific growth rate (mm). On the other hand, lag time (l) was described by Adair and Smith model. These models were then compared using the mathematical and graphical parameters such as regression coefficient (R2), scatter plot and residual plot. The results showed that the mean R2 values of Gompertz model for both broth medium and boiled rice were all above 0.98. The lag time modeled with Adair and Smith functions exhibited R2 values of above 0.92. Same pattern was found when mm modeled with Ratkowsky and Zwietering functions. The predicted mms, which described by Pathogen Modeling Program (PMP), in broth and in rice were lower than the observed values. On the other hand, the predicted values of l by PMP were higher than the observed values both in nutrient broth and in rice. These results suggest that growth of B. cereus in nutrient broth or in rice could be predicted by using the models described above, except for the PMP.
Session 100A, Food Microbiology: General II
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