Predicting behavior of Staphylococcus aureus, salmonella serovars, and Escherichia coli O157: H7 in pork products during single and repeated temperature abuse periods.

Tools for predicting growth of Staphylococcus aureus, Salmonella, and Escherichia coli O157:H7 (THERM; temperature history evaluation for raw meats) have been developed using ground pork and sausage. THERM tools have been tested with three types of pork sausage but not with other pork products or during sequential temperature abuse periods. We conducted inoculation studies (five strains each of S. aureus and/or Salmonella plus E. coli O157:H7) with simulated cooling of warm sausages, inprocess warming of bratwurst, isothermal temperature abuse of pork frankfurter batter, and two sequential periods of 13, 15.6, or 21.1 degrees C temperature abuse of breakfast sausage, natural (additive-free) chops, and enhanced (phosphate solution-injected) loins. In sequential temperature abuse studies, a temperature abuse period (> or =24 h) occurred before and after either refrigeration (5 degrees C for 24 h), or freezing (-20 degrees C for 24 h) and thawing (24 h at 5 degrees C). Pathogen growth predictions from THERM developed using ground pork and sausage were compared with experimental results of 0 to 3.0 log CFU of growth. Across all temperature abuse conditions, qualitative predictions (growth versus no growth) made using the pork tool (n = 133) and the sausage tool (n = 115) were accurate (51 and 50%, respectively), fail-safe (44 and 50%), or fail-dangerous (5 and 0%). Quantitative predictions from the two tools were accurate (29 and 22% , respectively), fail-safe (59 and 73%), or fail-dangerous (12 and 5%). Pathogen growth was greater during the second sequential temperature abuse period but not significantly so (P > 0.05). Both THERM tools provide useful qualitative predictions of pathogen growth in pork products during isolated or sequential temperature abuse events.

Predicting pathogen growth during short-term temperature abuse of raw sausage.

Lag-phase duration (LPD) and growth rate (GR) values were calculated from experimental data obtained using a previously described protocol (S. C. Ingham, M. A. Fanslau, G. M. Burnham, B. H. Ingham, J. P. Norback, and D. W. Schaffner, J. Food Prot. 70:1445-1456, 2007). These values were used to develop an interval accumulation-based tool designated THERM (temperature history evaluation for raw meats) for predicting growth or no growth of Salmonella serovars, Escherichia coli O157:H7, and Staphylococcus aureus in temperature-abused raw sausage. Data (time-temperature and pathogen log CFU per gram) were obtained from six inoculation experiments with Salmonella, E. coli O157:H7, and S. aureus in three raw pork sausage products stored under different temperature abuse conditions. The time-temperature history from each experiment was entered into THERM to predict pathogen growth. Predicted and experimental results were described as growth (> 0.3 log increase in CFU) or no growth (< or = 0.3 log increase in CFU) and compared. The THERM tool accurately predicted growth or no growth for all 18 pathogen-experiment combinations. When compared with the observed changes in log CFU values for the nine pathogen-experiment combinations in which pathogens grew, the predicted changes in log CFU values were within 0.3 log CFU for three combinations, exceeded observed values by 0.4 to 1.5 log CFU in four combinations, and were 1.2 to 1.4 log CFU lower in two combinations. The THERM tool approach appears to be useful for predicting pathogen growth versus no growth in raw sausage during temperature abuse, although further development and testing are warranted.

Predicting pathogen growth during short-term temperature abuse of raw pork, beef, and poultry products: use of an isothermal-based predictive tool.

A computer-based tool (available at: www.wisc.edu/foodsafety/meatresearch) was developed for predicting pathogen growth in raw pork, beef, and poultry meat. The tool, THERM (temperature history evaluation for raw meats), predicts the growth of pathogens in pork and beef (Escherichia coli O157:H7, Salmonella serovars, and Staphylococcus aureus) and on poultry (Salmonella serovars and S. aureus) during short-term temperature abuse. The model was developed as follows: 25-g samples of raw ground pork, beef, and turkey were inoculated with a five-strain cocktail of the target pathogen(s) and held at isothermal temperatures from 10 to 43.3 degrees C. Log CFU per sample data were obtained for each pathogen and used to determine lag-phase duration (LPD) and growth rate (GR) by DMFit software. The LPD and GR were used to develop the THERM predictive tool, into which chronological time and temperature data for raw meat processing and storage are entered. The THERM tool then predicts a delta log CFU value for the desired pathogen-product combination. The accuracy of THERM was tested in 20 different inoculation experiments that involved multiple products (coarse-ground beef, skinless chicken breast meat, turkey scapula meat, and ground turkey) and temperature-abuse scenarios. With the time-temperature data from each experiment, THERM accurately predicted the pathogen growth and no growth (with growth defined as delta log CFU > 0.3) in 67, 85, and 95% of the experiments with E. coli 0157:H7, Salmonella serovars, and S. aureus, respectively, and yielded fail-safe predictions in the remaining experiments. We conclude that THERM is a useful tool for qualitatively predicting pathogen behavior (growth and no growth) in raw meats. Potential applications include evaluating process deviations and critical limits under the HACCP (hazard analysis critical control point) system.

Using predictive microbiology to evaluate risk and reduce economic losses associated with raw meats and poultry exposed to temperature abuse.

The Department of Defense suffers economic losses when temperature-abused raw meat and poultry are condemned. Current US Army guidance regarding time/temperature limits associated with these foods (RISK-3 category) is ultraconservative, especially at lower temperatures. We have developed a more accurate, yet conservative or "fail-safe" computer-based tool for predicting pathogen growth in raw meat and poultry. In 20 trials of this tool, growth (> 0.3 log colony-forming unit increase) or no growth of Salmonella serovars, Escherichia coli O157:H7, and Staphylococcus aureus was accurately predicted 67% to 95% of the time for inoculated and temperature-abused poultry products and ground beef. Fail-safe predictions were obtained in trials for which the tool was inaccurate. The predictive tool is ready for further validation trials and field testing. Using this tool as a supplement to the current guidance will decrease losses associated with the condemnation of raw meat and poultry products exposed to short-term temperature abuse.

Data Structures for Integrating Quality and Cost Factors in a Foodservice Operation.

The information requirements for proper management of product quality and safety in a foodservice call for organization and manipulation of large amounts of data. This requires a structural organization to the data, which has large capacity while simultaneously being flexible enough to rapidly deliver information developed from the data. A matrix data structure meets these requirements and conveniently integrates new procedures and new products into a well - structured management information system. Such a structure is a convenient computational device, especially when implemented on a computer, and can be used for immediate feedback of information to the manager. Product quality and safety management requires that these functions be integrated into such a system and related to cost. Only in this way will the resource utilization impact of these control functions on the overall foodservice operation be apparent. Although in this paper the data structures are applied to foodservice operations, other applications are possible. In particular, dairy processing operations, meat and other food processing operations as well as food distribution systems, could all benefit from applications of such structures.