Predict Safety

Using data from a recent regulatory authority report on temperature-abused fresh raw meat and poultry items that resulted in the condemnation of several hundred dollars’ worth of product, we used THERM v.2 to evaluate the risk associated with the noted deviation. Several time and temperature measurements were available in the report. Internal (half-inch below the surface) product temperatures at two, four, eight, and 12 hours into the refrigeration failure were 42, 48, 60, and 38ºF, respectively. The lower temperature limit for THERM v.2 is 50ºF (10ºC), so user-entered temperatures below this limit are calculated using the 50ºF (10ºC) lag phase duration and growth rate values, a conservative function of the tool.

For all meat types—beef, pork, and poultry—THERM v.2 predicted that <70% of lag phase had elapsed for E. coli O157:H7, and <60% of lag phase had elapsed for Salmonella serovars. No lag phase duration or growth rate values are given for S. aureus at temperatures below 60ºF; this pathogen did not grow during 24-hour experiments reported by Ingham et al. Therefore, THERM v.2 did not predict any lag phase elapsing for S. aureus during this out-of-temperature situation. Using predictive microbiology in this example may have reduced economic losses associated with this refrigeration failure.

Acceptance of Tools a Necessity

Using predictive microbiology tools in conjunction with managing HACCP systems seems to be a natural fit; however, because HACCP systems have regulatory oversight, regulators must accept predictive microbiology tools. The FDA, with HACCP oversight of the juice and seafood industries, has no formal policy statement on the use of predictive microbiology tools. FDA officials do, however, have a long history of predictive microbiology acceptance within the thermo-stabilization industry and have often used predictive models to make policy decisions.

The USDA, on the other hand, has offered its opinion on predictive microbiology tools, specifically in USDA Food Safety and Inspection Service (FSIS) Notice 25-05, the Listeria compliance guidelines, as well as in USDA FSIS, Appendix B to “Compliance Guidelines for Cooling Heat-treated Meat and Poultry Products (Stabilization).” In these documents, the USDA recognizes that predictive microbiology tools are beneficial in HACCP systems for hazard analysis, development of critical limits, and evaluation of process deviations. They also point out many drawbacks, however, such as the presence/growth of indigenous microbes that affect predictions, stress response reactions that are not properly addressed, and unknown biological variability.

The USDA still accepts predictive microbiology information as one—but not the sole—source of HACCP documentation. The way the model was developed, validated, and used to produce predictions must be considered, however. The USDA also encourages consulting an expert in predictive microbiology modeling to ensure appropriate use.

The USDA has just launched the Predictive Microbiology Information Portal (PMIP, http://portal.arserrc.gov/PMIP Home.aspx) to assist small and very small food companies in the use of predictive models and food microbiology information. The PMIP is especially useful for locating and retrieving predictive models and research data for use in HACCP systems.

State regulators are also accepting predictive microbiology information, along with expert consultations, to resolve many noncompliance issues. Cindy Klug, a meat scientist with the Wisconsin Department of Agriculture, says she is a firm believer in using validated science to determine food safety rather than the “we haven’t killed anyone yet” approach. She recalls using the first version of THERM, which was developed at the University of Wisconsin-Madison, and says it was “a bit difficult to use and interpret.” But after it was revised it was easier to use, she adds. Dr. Klug and Steven C. Ingham, PhD, a Wisconsin professor, used the new online version of THERM to assist an establishment in determining carcass safety when their coolers went down during a 100ºF heat wave.