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Thomas P. Oscar, Ph.D. USDA, ARS Princess Anne, MD

General Regression Neural Network Model for Growth of Salmonella Serotypes on Chicken Skin for Use in Risk Assessment. Thomas P. Oscar, Ph.D. USDA, ARS Princess Anne, MD. Risk Assessment Data Gaps. Strain variation. Microbial competition. Initial dose. Food matrix.

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Thomas P. Oscar, Ph.D. USDA, ARS Princess Anne, MD

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  1. General Regression Neural Network Model for Growth of Salmonella Serotypes on Chicken Skin for Use in Risk Assessment Thomas P. Oscar, Ph.D. USDA, ARS Princess Anne, MD

  2. Risk AssessmentData Gaps • Strain variation • Microbial competition • Initial dose • Food matrix

  3. Must have been that chicken! Strain Variation • Salmonella enterica serotypes (> 2,300) • Top three in chickens are: Enteritidis Typhimurium Kentucky

  4. Strain Variation Autoclaved chicken meat at 25C J. Food Safety (2000) 20:225-236.

  5. Microbial Competition J. Food Prot. (2003) 66(2):200-207; (2006) 69(2):276-281.

  6. Microbial Competition • Natural Antibiotic Resistance • Bad for public health • Good for predictive microbiology Salmonella Typhimurium DT104 J. Food Prot. (2008) 71(6):1135-1144. J. Food Prot. (2006) 69(9):2048-2057. J. Food Prot. (2009) 72(2):304-314.

  7. Initial Dose Food Microbiol. (2007) 24:640-651.

  8. Regression Modeling Tertiary Model Secondary Models Predicted W W Model Observed W Observed N(t) Observed PI Predicted PI PI Model Primary Model Primary Model m Model Observed m Predicted m Predicted N(t) Predicted N(t) Nmax Model Observed Nmax Predicted Nmax J. Food Prot. (2005) 68(12):2606-2613.

  9. Neural Network Modeling • General Regression Neural Network (GRNN) • Better performance than regression models • User-friendly commercial software • Compatible with Monte Carlo simulation software Jeyamkondan et. al., 2001

  10. Objective • To develop a GRNN and simulation model for growth of Salmonella on chicken skin as a function of serotype for use in risk assessment. • Short-term temperature abuse (0 to 8 h)

  11. 5 l 7 cfu Materials and Methods • Experimental Design (3 x 10 x 5 x 2 x 2) • Serotypes (Typhimurium, Kentucky, Hadar) • 30C in BHIB for 23 h at 150 opm } prehistory • Temperature (5, 10, 15, 20, 25, 30, 35, 40, 45, 50C) • Time (0, 2, 4, 6, 8 h) • Trial (1, 2) • Sample (a, b)

  12. Materials and Methods  CFU  MPN

  13. Materials and Methods Poultry isolates • Plating Media • XLH-CATS for Typhimurium • XLH-NATS for Kentucky • XLH-TUGS for Hadar Ingredients XL = xylose lysine H = HEPES C = chloramphenicol A = ampicillin T = tetracycline S = streptomycin N = novobiocin U = sulfisoxazole G = gentamicin MPN drop plate

  14. General Regression Neural Network ŷ Output Summation Layer N(x) D(x) Predicted Value -0.01 … … 7.37 Pattern Layer Distance Function S T t Input Layer Serotype Temp. time Specht, 1991

  15. Step 1 Enter data

  16. Step 2 Define the data set

  17. Step 3 Set the training parameters

  18. Step 4 Train the GRNN

  19. Step 5 Review results

  20. 92% Acceptable J. Food Prot. (2006) 69(9):2048-2057.

  21. 89% Acceptable J. Food Prot. (2006) 69(9):2048-2057.

  22. Step 6 Predict

  23. Step 7 Integrate with risk assessment

  24. -1 Correlation 0 Correlation

  25. Easy to develop Low cost Flexible predictions Superior performance Conclusion #1 Neural network modeling outperforms regression modeling in predictive microbiology applications

  26. Conclusion #2 • Cocktail of Typhimurium_Kentucky_Hadar • Overly ‘fail-safe’ predictions for Kentucky.

  27. Conclusion #3 GRNN model was successfully validated for risk assessment model? Data Gaps Strain variation Microbial competition Initial dose Food matrix

  28. I hope it was Kentucky! Acknowledgements • Thank you for your attention! • Thanks to Jaci Ludwig of ARS and Celia Whyte and Olabimpe Olojo of UMES for their outstanding technical assistance on this project.

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