Modeling the Survival and Growth of Salmonella on Chicken Skin Stored at 4 to 12 C

1. Modeling the Survival and Growth of Salmonella on Chicken Skin Stored at 4 to 12C Thomas P. Oscar, Ph.D. U.S. Department of Agriculture Agricultural Research Service Princess Anne, MD

2. Introduction • Salmonella & poultry • 1 - 2 cases per 100,000 • Initial contamination • < 30 CFU per chicken carcass • Illness dose • 105 to 107 CFU Int. J. Food Microbiol. 2004. 93:231-247.

3. Introduction • Risk Assessment • Hazard Identification • Hazard Characterization • Exposure Assessment • Risk Characterization Packaging Contamination Risk Pathway Cold Storage Temp. Abuse Meal Prep. Temp. Abuse Meal Prep. Cross-contamination Cooking Under-cooking Consumption Exposure

4. Introduction • Predictive microbiology • Support risk assessments • Data gaps • Low initial dose • Microbial competition • Low temperatures

5. Introduction • Another data gap • Variation among serotypes Autoclaved chicken meat at 25C J. Food Safety. 2000. 20:225-236.

6. Objective • Develop a predictive model • Survival & growth • Salmonella Typhimurium & Kentucky • Low initial dose (0.9 log) • Chicken thigh skin (2.14 cm2) • with microbial competition • Low temperature (4 to 12C)

7. Materials & Methods • Experimental design • Model development (Salmonella serotype Typhimurium DT104) • 5 x 5 full factorial • Temperature (4, 6, 8, 10, 12C) • Time (0, 1, 3, 6, 10 days) • 4 replicates

8. Materials & Methods • Experimental design • Model validation (Salmonella serotype Typhimurium DT104) • 4 x 5 full factorial • Temperature (5, 7, 9, 11C) • Time (0, 1, 3, 6, 10 days) • 2 replicates Interpolation

9. Materials & Methods • Experimental design • Model validation (Salmonella serotype Kentucky) • 4 x 5 full factorial • Temperature (5, 7, 9, 11C) • Time (0, 1, 3, 6, 10 days) • 2 replicates Extrapolation

10. Materials & Methods • Salmonella enumeration • Combined MPN & CFU method  CFU  MPN J. Food Prot. 2006. 69:2048-2057.

11. Materials & Methods • Plating media • Salmonella serotype Typhimurium DT104 • XLH-CATS • Salmonella serotype Kentucky • XLH-NATS J. Food Prot. 2006. 69:2048-2057.

12. Materials & Methods • General Regression Neural Network Model ŷ Output (Δ) N(x) D(x) Summation Layer Predicted Value -0.71 … … 4.13 Pattern Layer Distance Function T t Input Layer Temp. time IEEE Trans. Neural. Netw. 1991. 2:568-576

13. Materials & Methods • Model performance • Residual • Observed - predicted • Acceptable prediction zone (APZ) • -1 log (fail-safe) to 0.5 log (fail-dangerous) • Acceptable performance •  70% of residuals in APZ Prediction bias & accuracy J. Food Sci. 2005. 70:M129-M137.

14. Results • Salmonella serotype Typhimurium DT104 • Model development (n = 163)

15. Results • Salmonella serotype Typhimurium DT104 • Model validation for interpolation (n = 77)

16. Discussion • Salmonella serotype Typhimurium DT104 • Growth on sterile chicken breast meat at 10C Oscar (unpublished)

17. Results • Salmonella serotype Kentucky • Model validation for extrapolation (n = 70)

18. Discussion • Variation among serotypes • Kentucky grows slower on chicken skin at 35C J. Food Prot. 2009. 72:2078-2087.

19. Results & Discussion • Model Performance (Development)

20. Results & Discussion • Model Performance (Interpolation)

21. Results & Discussion • Model Performance (Extrapolation)

22. Results & Discussion

23. Conclusions • Model was validated • Microbial competition suppresses growth • MPD = 1 log vs 8 log @ 10C • Kentucky grows slower • Compatible with @Risk