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 Strain variation Microbial competition Microbial competition Initial dose Initial dose Food matrix Food matrix

Strain Variation Salmonella enterica serotypes (> 2,300) Salmonella enterica serotypes (> 2,300) –Top three in chickens are: EnteritidisTyphimurium Kentucky Must have been that chicken!

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

Microbial Competition J. Food Prot. (2003) 66(2): ; (2006) 69(2):

Microbial Competition Natural Antibiotic Resistance Natural Antibiotic Resistance –Bad for public health –Good for predictive microbiology Salmonella Typhimurium DT104 J. Food Prot. (2006) 69(9): J. Food Prot. (2008) 71(6): J. Food Prot. (2009) 72(2):

Initial Dose Food Microbiol. (2007) 24:

Secondary Models Primary Model Primary Model N max Model  Model PI Model  Model Observed  Predicted  Observed PI Predicted PI Observed  Predicted  Observed N max Predicted N max Predicted N(t) Observed N(t) Tertiary Model Predicted N(t) Regression Modeling J. Food Prot. (2005) 68(12):

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

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. 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)

Materials and Methods Experimental Design (3 x 10 x 5 x 2 x 2) 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 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) 7 cfu 5  l

Materials and Methods  MPN  CFU

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

General Regression Neural Network Tt …… N(x)D(x) ŷ Input Layer Pattern Layer Summation Layer Output S Specht, 1991 SerotypeTemp. time Distance Function Predicted Value

Step 1 Enter data

Step 2 Define the data set

Step 3 Set the training parameters

Step 4 Train the GRNN

Step 5 Review results

J. Food Prot. (2006) 69(9):

Step 6 Predict

Step 7 Integrate with risk assessment

Serotype (%) Temperature (  C) Time (h) Log change ScenarioT_K_HMin_ML_MaxMin_ML_MaxCorrelationMin_50%_Max A31_58_115_20_500_2_ _0.09_4.8 B31_58_115_20_500_2_8-0.16_0.04_0.5

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

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

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

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