Analysis and Application of Key Modeling Concepts Utilized in Predictive Microbiology for Food Processing

The use of modeling techniques for safety and risk prediction in the food supply is a common practice. Factors affecting microbial heat resistance include those inherent to the organism, environmental conditions and the intrinsic properties of the heating menstruum. Varying physiological states of microorganisms could affect the measured response and add uncertainty to results from predictive models. Inactivation tests were performed using Escherichia coli strain K12 and E. coli O157:H7 for various growth conditions: traditionally or statically grown cells, chemostat‐grown cells, and chemostatgrown cells with buffered feed media. Heating menstruum was non‐buffered 0.1% peptone, 0.1 M phosphate buffer (pH 7.0), a simulated beef broth (pH 5.9) and actual beef broth obtained from 93% lean ground beef. Thermal inactivation of the cells was carried out at 58, 59, 60, 61 and 62°C and recovery was on a non‐selective tryptic soy agar. Chemostat cells were significantly less heat resistant than the traditional or buffered chemostat cells at 58°C. Shape response was also significantly different, with traditionally‐grown cells exhibiting reducing thermal resistance over time and chemostat cells showing the opposite effect. Buffering the heating menstruum to ca. pH 7 for both traditionally‐grown and chemostat cells resulted in inactivation curves which showed less variability or scatter of data points. Non log‐linear regression analysis resulted in the most accurate fit in most cases. There were significant differences in thermal resistance when cells were thermally treated in either simulated or actual beef broth mixtures compared to laboratory diluent.