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ARS Home » Northeast Area » Wyndmoor, Pennsylvania » Eastern Regional Research Center » Residue Chemistry and Predictive Microbiology Research » Research » Publications at this Location » Publication #351131

Research Project: Development of Predictive Microbial Models for Food Safety using Alternate Approaches

Location: Residue Chemistry and Predictive Microbiology Research

Title: Interactive effect of temperature and fat content on thermal resistance of Escherichia coli 0157:H7 salmonella spp., and Listeria monocytogenes in meat and poultry-a global analysis

Author
item Huang, Lihan
item Hwang, Cheng-an - Andy
item Fang, Ting - Fujian Agricultural & Forestry University

Submitted to: Food Control
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/22/2018
Publication Date: N/A
Citation: N/A

Interpretive Summary: Escherichia coli O157: H7, Salmonella spp., and Listeria monocytogenes are three major foodborne pathogens that can cause serious human infections and are significant public health hazards. Thermal processing is one of the most effective methods to inactivate these pathogens. This study was conducted to explore the interactive effect of heating temperature and fat on thermal resistance of these pathogens in meat. More accurate mathematical models were developed to estimate the thermal resistance without considering the difference in the sources, serotypes, and isolates. These models can be used to design and evaluate thermal processes for inactivating these pathogens in meat and poultry products.

Technical Abstract: Escherichia coli O157: H7, Salmonella spp., and Listeria monocytogenes are three major foodborne pathogens that can cause serious human infections and are significant public health hazards. These pathogens may contaminate meat and poultry products. Thermal processing is one of the most effective methods to inactivate these pathogens. Many studies have reported the thermal resistance of these pathogens. Although showing some general trends, the published data vary considerably from one study to another. This study aimed to develop generalized models for predicting thermal resistance by evaluating the interactive effect of temperature and fat content using both simple (temperature only) and multiple (temperature and fat) linear regression models. The results showed that temperature and fat content can be good predictors for thermal resistance. For E. coli O157:H7 and L. monocytogenes, more than 95% of the variations in the log D values can be attributed to the interactive effects. For Salmonella spp. in non-poultry meats, close to 90% of the variations of the log D values can be attributed to the interactive effects, which is significantly higher than 58.3% if only temperature is included as the predictor. For Salmonella spp. in poultry meats, both simple and multiple linear regression models can be used, achieving > 93% accuracy in predicting the log D values. Including the interactive effect of temperature and fat content improves the accuracy of the models describing the thermal resistance without considering the difference in the sources, serotypes, and isolates. The generalized models can be used to design and evaluate thermal processes for inactivating these pathogens in meat and poultry products.