Residue Chemistry and Predictive Microbiology Products and Services

1. Can I use the models for situations where the product temperature changes over time?

Currently, there are dynamic temperature models for C. botulinum and C. perfringens, and static temperature models for other pathogens. We are in the process of producing dynamic temperature models for a variety of other pathogens, and expect these to be available in future versions of the PMP.

To use a static model for situations where temperature changes over time, you will need to calculate the growth at specific temperatures, and then add these individual growth calculations to determine the total predicted growth over the entire time-temperature range. For example, suppose you want to predict the growth of L. monocytogenes in a product that has the following time-temperature profile:
0.0 hours 98.6°F
0.5 hours 71.2°F
1.0 hours 63.4°F
2.0 hours 50.1°F

For fail-safe predictions, we will assume that the product was at 98.6°F for 0.5 hours. At 71.2°F for 1 hour, and at 63°F for 2 hours. More conservative estimations can be made if you collect time-temperature data in shorter time intervals.

First, set the environmental conditions to match your product. In this example we will use the aerobic broth culture L. monocytogenes model for NaCl. Set the conditions to: pH=6.5, NaCl=1.0%, 0 ppm nitite.

Set the "Initial Level" to 3.0.

Next, set the temperature to 98.6°F (37°C), then click the box "Calculate Growth Data". Next, click the "No Lag" box (for more fail-safe predictions). Since we are assuming that the product was at 98.6°F for 0.5 hours, we subtract log 3.0 (from the Initial Level) from the "log(CFU/ml)" value at 0.5 hours. However, you’ll notice that there is no value given at 0.5 hours. Therefore, average the count for 0.4 (3.64 log[CFU/ml]) and 0.6 hours (3.76 log[CFU/ml]). This would equal (3.64 + 3.76)/2 = 3.70.

Subtract 3.0 (starting level) from 3.7 and this equals 0.70 log(CFU/ml). Record this number.

Next do the same calculation at 71.2°F for 1 hour, keeping the initial level at 3.0. The prediction at 1.0 hour is 3.63 log(CFU/ml). Therefore, subtract 3.0 from 3.63, and this equals 0.63 log(CFU/ml).

Record this number. Finally, repeat this procedure at 63°F. At 2 hours the prediction is 3.67 log (CFU/ml). Subtract 3.0 again from 3.67, and this equals 0.67 log(CFU/ml). Record this number. Now add all three numbers: 0.70+0.63+0.67 = 2.0. Therefore, the prediction is for 2.0 logs of growth with this cooling profile. As with all PMP models, you would need to validate the predictions for food types and formulations that are different from the model.

2. How can I get a more Fail-Safe prediction?

For growth models, chose the "No Lag" prediction option. This option does not add a Lag Time to the growth scenario. Also, choose models that were developed in a sterile broth system. Typically, the Generation Times/growth rates observed in broth media under optimum conditions for NaCl/water activity and pH are equal to or greater than that observed in food.

For inactivation models, more fail-safe predictions can be attained by setting the environmental parameters to values that predict a greater inactivation rate.