Purpose: The objective of this study is to evaluate the performance of a new C. perfringens growth model in IPMP Dynamic Prediction for predicting its growth under dynamic cooling, isothermal, and dynamic heating temperature profiles.
Methods: Computer simulation is used to predict the growth of C. perfringens under different temperature profiles. The residual errors of predictions (observation-prediction) are analyzed, and the root-mean-square errors (RMSE) calculated.
Results: For isothermal and heating profiles, each data point in growth curves is compared. The mean residual errors of predictions (MRE) range from -0.40 to 0.02 log CFU/g, with a RMSE of ~0.6 log CFU/g. For cooling, the end-point predictions are conservative in nature, with an MRE of -1.16 log CFU/g for single rate cooling and -0.66 log CFU/g for dual rate cooling. The RMSE is between 0.6 and 0.7 log CFU/g. Compared with other models reported in Mohr and others (2015), this model makes comparable or mostly better accurate and fail-safe predictions. For cooling, the percentage for accurate and fail-safe predictions is between 97.6% and 100%. Under criterion 1, the percentage of accurate predictions is 47.5% for single rate and 66.7% for dual rate cooling, while the fail-dangerous predictions are between 0 and 2.4%.
Significance: This study demonstrates that IPMP Dynamic Prediction can be used by food processors and regulatory agencies as a tool to evaluate the safety of cooked or heat-treated uncured meat and poultry products exposed to cooling deviations or to develop customized cooling schedules. This study also demonstrates the need for more accurate data collection during cooling.