Title: Quantifying the Effect of Slow-Cooking Operations on the Thermal Resistance of Salmonella in Whole-Muscle Pork Products - NPB #09-065 Investigator:

The overall goal of this project was to improve the reliability of thermal process validation tools for Salmonella in pork products, by accounting for stress adaptation that can occur during slow cooking processes. The specific objectives were: (1) To modify, for ground and whole-muscle pork products, a model recently developed at MSU to predict the rate of Salmonella thermal inactivation as a function of both product temperature and prior (sub-lethal) thermal history, and (2) To validate this model via pilot-scale challenge studies using ground and whole-muscle pork products inoculated with Salmonella. The project entailed thermal treatment (cooking) trials in four different systems, ranging from 1 g samples in a highly controlled laboratory system to ground pork patties and whole-muscle chops and roasts in two pilot-scale oven systems. The key results were: (1) The thermal resistance of Salmonella in pork products increases when subjected to sublethal injury during slow cooking processes, and an improved inactivation model accounts for that effect; (2) The thermal resistance of Salmonella is significantly greater (~50%) in whole-muscle pork than in ground pork; (3) Application of laboratory-based inactivation data for commercial process validations carries inherent uncertainty that is generally underreported and that increases with scale-up; and (4) Although slow cooking processes can allow Salmonella to adapt and become more thermally resistant, moist-air cooking to an endpoint 160 F generally results in sufficient cumulative lethality to overcome this effect. These findings mean that producers of ready-to-eat pork products need to ensure that process validations are based on inactivation data and models that are appropriate to their specific product and process (e.g., whole-muscle vs. ground) and that thermal processes are designed with a sufficient margin of safety to account for the inherent uncertainty associated with the application of inactivation models to thermal process validations.