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A. AMÉZQUITA1, C. L. Weller1, L. Wang1, H. Thippareddi2, and D. Burson3. (1) Department of Biological Systems Engineering, University of Nebraska Lincoln, 157 L.W. Chase Hall, Lincoln, NE 68583-0726, (2) Dept. of Food Science & Technology, Univ. of Nebraska, Lincoln, 236 Food Industry Complex, Lincoln, NE 68583-0919, (3) Department of Animal Science, University of Nebraska Lincoln, A213d Animal Science Building, Lincoln, NE 68583-0908 Since 1999, meat processors have been required to meet stabilization performance standards for preventing growth of Clostridium perfringens by 1 log10 cycle during cooling of ready-to-eat (RTE) products. Numerous small meat processors have difficulties complying with these guidelines. Several attempts have been made to develop predictive models for growth of C. perfringens within the range of cooling temperatures. These studies mainly focus on microbiological aspects, using hypothesized cooling rates. Conversely, studies dealing with heat transfer models to predict cooling rates in meat products, do not address microbial growth. Integration of heat transfer relationships with C. perfringens growth relationships during cooling of meat products has been very limited. Our objective was to integrate a heat transfer model and a C. perfringens growth model into a user-friendly computer program for accurate prediction of cooling rates and potential growth of the microorganism during cooling of RTE meat products. The heat transfer component was developed in Matlab® 6.5 using finite element analysis to model two-dimensional axisymmetric transient heat diffusion. Validation used experimental data collected in commercial meat-processing facilities. For C. perfringens growth, a dynamic model was constructed using Baranyi’s non-autonomous differential equation. The bacterium’s growth model was integrated into the computer program by using predicted temperature histories as input values. For cooling cooked hams from 150°F to 40°F using forced air, maximum deviation between predicted and experimental core temperature was within 2%. Maximum specific growth rate of C. perfringens was detected at 113°F. Predicted growth curves obtained from dynamic modeling were in good agreement with validated results for three different cooling scenarios. Maximum difference between predicted and experimental cell counts was within 0.5 log10 CFU/g. The integrated model of heat transfer and C. perfringens growth can be used to provide valuable insights into air chilling of RTE meats for risk assessment and HACCP plans.
Session 111, Food Engineering: Modeling heat transfer and microbial inactivation
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