Development of an integrated model for heat transfer and dynamic growth of Clostridium perfringens during the cooling of cooked boneless ham

Abstract

The Food Safety and Inspection Service (FSIS) of the U.S. Department of Agriculture requires meat processors to follow time-temperature compliance guidelines to meet stabilization performance standards. The FSIS has proposed that by following these guidelines, allowable growth of Clostridium perfringens is limited to a 1-log₁₀ multiplication during cooling after cooking of ready-to-eat meat products. Numerous small meat processors have difficulties complying with these performance standards. Several attempts, mainly focused on microbiological aspects, have been made to develop predictive models for growth of C. perfringens within the range of cooling temperatures of the guidelines. 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.

The objective of the current study 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 cooked boneless ham.

The heat transfer component was developed in Matlabo^® 6.5 using finite element analysis to model two-dimensional axisymmetric transient heat conduction. 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 66.6°C to 4.4°C using forced air, the maximum deviation between predicted and experimental core temperature data was 2.54°C. Predicted C. perfringens growth curves obtained from dynamic modeling were in good agreement with validated results for three different cooling scenarios. Mean absolute values of relative errors were below 6%, and deviations between predicted and experimental cell counts were within 0.37 log₁₀ CFU/g. For a cooling process which was in exact compliance with the FSIS stabilization performance standards, a mean net growth of 1.37 log₁₀ CFU/g was predicted.

This study introduced the combination of engineering modeling and microbiological modeling as a useful quantitative tool for general food safety applications, such as risk assessment and Hazard Analysis and Critical Control Points plans.