Validation of a predictive model for salmonella growth in ground beef under multiple cycles of temperature change

The advances of microbial predictive modeling techniques facilitate Quantitative Microbial Risk Assessment (QMRA), which can extensively contribute to the food safety in meat industry in the United States. However, the challenges of predictive modeling cannot be neglected, an example of which is the intermediately induced lag phase. This research sought to expose this problem by validation of a predictive model for Salmonella growth on ground beef. Salmonella is an important pathogen related to beef consumption. ComBase is a predictive database that can be used to predict microbial behaviors with a variety of environmental conditions in a dynamic manner. The literature review part of this thesis discusses general properties of Salmonella, the implication of Salmonella in beef and beef products, the applications of Hazard Analysis and Critical Control Points (HACCP) and QMRA in meat industry, methods of predictive modeling, challenges and validation of predictive modeling. The laboratory work of this thesis exposed the fail-safe phenomenon under a fluctuating temperature profile. Ground beef (20% fat) was inoculated with a cocktail composed of five strains of Salmonella. The inoculated ground beef was subjected to programmable water bath with different temperature profiles. Enumeration of Salmonella was conducted with proper time intervals. The observed Salmonella concentrations were compared ComBase predictions, accuracy and bias factors, as well as final differences between the observed and the predicted were calculated. The results showed that the model was very fail-safe. According to literature review and previous study in our laboratory, we excluded background microflora from the major reasons causing the high deviation. And we speculated that the deviation was mainly resulted from temperature-induced intermediate lag phase.M.S.Includes bibliographical referencesby Zishuo Zen