Title: PREDICTIVE MODEL FOR VERIFICATION OF CRITICAL CONTROL POINTS FOR GROWTH OF SALMONELLA TYPHIMURIUM DT104 ON CHICKEN FRANKFURTERS AFTER THERMAL PROCESSING Author
Submitted to: International Journal of Food Microbiology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: February 1, 2007
Publication Date: February 1, 2008
Citation: Oscar, T.P. 2008. Predictive model for verification of critical control points for growth of salmonella typhimurium dt104 on chicken frankfurters after thermal processing. International Journal of Food Microbiology. 71(6):1135-1144. Interpretive Summary: Although chicken frankfurters are thermally processed and sold as ready-to-eat, they are not sterile products and on occasion test positive for foodborne pathogens, such as Salmonella. Results of the current study indicated that Salmonella could grow on chicken frankfurters held at 56 to 94F. However, at temperatures below 56F, Salmonella did not grow but survived on chicken frankfurters. A computer model was developed that predicts when Salmonella growth exceeds a “user-defined” critical limit for food safety. Frankfurters exposed to a time and temperature that exceeds this critical limit should be thoroughly reheated to kill all Salmonella or discarded to eliminate the risk of infection and illness from this pathogen.
Technical Abstract: Growth of a single strain (ATCC 700408) of Salmonella Typhimurium definitive phage type 104 (DT104) on chicken frankfurter portions (1-g) with a competitive micro flora was investigated and modeled from 0.4 to 3.28 log MPN/g. The objective was to develop a predictive model for verification of critical control points for growth of the pathogen on chicken frankfurters after thermal processing. A 3 x 4 MPN assay that used a selective medium with four antibiotics (XLH-CATS) enumerated the pathogen over a wide range of temperature (10 to 34C). Data from four replicate challenge studies per temperature were combined and fit to a primary model to determine maximum specific growth rate (SGR) and the 95% prediction interval (PI). Non-linear regression was used to obtain secondary models as a function of temperature for SGR and PI, which ranged from 0 to 0.16 per h and 0.7 to 2.8 log MPN/g, respectively. The pathogen did not grow on chicken frankfurters at 10, 11 or 12C and showed only minor and slow growth at 14C. Secondary models were combined with the primary model to create a tertiary model for predicting variation (PI) of pathogen growth among batches of chicken frankfurter portions. The criterion used for acceptable performance was that 90% of observed MPN data had to be in PI predicted by the tertiary model. For dependent data (n = 236), 94% of observed MPN data were in PI predicted by the tertiary model, whereas for independent data (n = 136), 92% of observed MPN data were in PI predicted by the tertiary model. Thus, the tertiary model was successfully verified against dependent data and validated against independent data for predicting variation of S. Typhimurium DT104 growth among batches of chicken frankfurter portions with a competitive micro flora. The validated tertiary model can be used to verify critical control points for growth of the pathogen that occur after thermal processing of chicken frankfurters.