Evaluating the Performance of a New Model for Predicting the Growth of Clostridium perfringens in Cooked, Uncured Meat and Poultry Products under Isothermal, Heating, and Dynamically Cooling Conditions

Abstract

Clostridium perfringens type A is a significant public health threat and its spores may germinate, outgrow, and multiply during cooling of cooked meats. This study applies a new C. perfringens growth model in the USDA Integrated Pathogen Modeling Program-Dynamic Prediction (IPMP Dynamic Prediction) Dynamic Prediction to predict the growth from spores of C. perfringens in cooked uncured meat and poultry products using isothermal, dynamic heating, and cooling data reported in the literature. The residual errors of predictions (observation-prediction) are analyzed, and the root-mean-square error (RMSE) calculated. For isothermal and heating profiles, each data point in growth curves is compared. The mean residual errors (MRE) of predictions range from -0.40 to 0.02 Log colony forming units (CFU)/g, with a RMSE of approximately 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 the literature, this model makes more 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 cooling and 66.7% for dual-rate cooling, while the fail-dangerous predictions are between 0% and 2.4%. This study demonstrates that IPMP Dynamic Prediction can be used by food processors and regulatory agencies as a tool to predict the growth of C. perfringens in uncured cooked meats and 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.

Bacterial Contaminants of Poultry Meat: Sources, Species, and Dynamics

“…The prevalence of Staphylococcus aureus on poultry meat products has been addressed, although most of the literature has focused on antibiotic resistance and typing of the isolates [ 63 , 64 , 65 ]. Although there are a few reports on the detection of Clostridium perfringens on poultry meat [ 61 ], most of the literature focuses on assessing and modeling its growth on meat after spore germination following the slaughtering process [ 66 , 67 , 68 ]. Lastly, the emergence of Aeromonas from poultry meat products as a vector of human infection has also been reported [ 10 ].…”

Lipid metabolism and lipophagy in cancer

“…SCD1 has been shown to be highly expressed in lung adenocarcinoma and was found to be important for cell proliferation, migration and invasion. Moreover, the study suggested SCD1 as a potential biomarker of lung adenocarcinoma [34]. A separate study showed delayed tumor growth in colorectal cancer mouse model on SCD1 inhibitor administration [35].…”

On optimum dynamic temperature profiles for thermal inactivation kinetics determination

“…It should be stressed out, however, that calculating the inactivation parameters from dynamic temperature data requires more complicated numerical algorithms instead of standard non‐linear regression techniques (Corradini et al., 2008). Several studies, in the recent literature, have demonstrated the advantages of estimating kinetic parameters using non‐isothermal temperature conditions either during inactivation (Cattani et al., 2016; Corradini et al., 2005; Goula et al., 2018; Greiby et al., 2017; Huang, 2013; Kubo et al., 2018; Valdramidis et al., 2008) or microbial growth (Cornet et al., 2010; Huang, 2016; Huang & Vinyard, 2016; Longhi et al., 2017; Van Derlinden et al., 2008).…”