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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 #306299

Research Project: DEVELOPMENT OF PREDICTIVE MICROBIAL MODELS FOR FOOD SAFETY AND THEIR ASSOCIATED USE IN INTERNATIONAL MICROBIAL DATABASES

Location: Residue Chemistry and Predictive Microbiology Research

Title: Modelling the effect of pH, sodium chloride and sodium pyrophosphate on the thermal resistance of Escherichia coli O157:H7 in ground beef

Author
item Juneja, Vijay
item CADAVEZ, VASCO - De Ciência Animal & Centro De Investigação De Montanha (CIMO)
item GONZALES-BARRON, URSULA - De Ciência Animal & Centro De Investigação De Montanha (CIMO)
item Mukhopadhyay, Sudarsan

Submitted to: Food Research International
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/25/2014
Publication Date: 12/10/2014
Publication URL: http://handle.nal.usda.gov/10113/61096
Citation: Juneja, V.K., Cadavez, V., Gonzales-Barron, U., Mukhopadhyay, S. 2014. Modelling the effect of pH, sodium chloride and sodium pyrophosphate on the thermal resistance of Escherichia coli O157:H7 in ground beef. Food Research International. 69:289-304.

Interpretive Summary: Food poisoning outbreaks due to E. coli O157:H7 have been associated with inadequate cooking of contaminated ground beef in retail food service operations. Thus, there has been a need to better define the heat treatment given to beef products to provide an adequate degree of protection against survival of this pathogen. We developed a mathematical model for predicting the destruction of E. coli O157:H7 in beef with heating temperature, pH, sodium chloride and sodium pyrophosphate as controlling factors. The model can be used to predict the time required at any temperature to kill a specific number of this deadly bacterium. The results will be of immediate use to consumers and to the retail food service operations and regulatory agencies to ensure the safety of the cooked beef.

Technical Abstract: A fractional factorial design was used to assess the combined effects of four internal temperatures (55.0, 57.5, 60.0 and 62.5C) and five concentrations of sodium chloride (NaCl) (0.0, 1.5, 3.0, 4.5 and 6.0 wt/wt%) and sodium pyrophosphate (SPP) (0.0, 0.1, 0.15, 0.2 and 0.3 wt/wt%) on the heat resistance of Escherichia coli O157:H7 in minced beef meat at five levels of pH (4.0, 5.0, 6.0, 7.0 and 8.0). The 38 variable combinations were replicated to provide a total of 76 survivor curves from the experimental data. Given the different shapes of survivor curves observed, a three-parameter modified Weibull model was applied to quantify the combined effect of these parameters on the heat resistance of E. coli O157:H7 cells. The polynomial secondary models, developed to estimate the time to achieve a 3-log and a 5-log reduction, suggested that the addition of NaCl or SPP up to a certain critical concentration (about 2.7-4.7%; about 0.16%, respectively) acts independently to increase the heat resistance of E. coli O157:H7. Beyond such critical concentrations, the thermo-tolerance of E. coli O157:H7 will progressively diminish. A similar pattern was found for pH, with E. coli O157:H7 having its maximum heat resistance at critical pH values between 6.0-6.7, depending upon temperature and NaCl content. These findings were corroborated by a mixed-effect omnibus regression. Such a joint model further revealed that the acidity of the matrix and NaCl content had a greater impact on the inactivation kinetics of E. coli O157:H7 in minced beef than SPP, and both are responsible for the concavity/convexity of the curves. When pH, SPP or NaCl content are far from their critical values, the temperatures needed to reduce E. coli O157:H7 up to a certain log level are much lower than those required when any other environmental condition is at its critical value. The modified three-parameter Weibull model was capable of depicting well the different shapes of experimental inactivation curves encountered in this work. Meat processors can use the model to design lethality treatments in order to achieve specific log reductions of E. coli O157:H7 in ready-to-eat beef products.