1. Variation among Batches of Freshly Ground Chicken Breast Meat Complicates the Modeling of Salmonella Growth Kinetics Thomas P. Oscar USDA, ARS Microbial Food Safety Research Unit University of Maryland Eastern Shore Princess Anne, MD

2. Introduction Pure culture Co-culture Test pathogen Competitor

3. Introduction Marker pathogen Fluorescent (e.g. gfp) Luminescent

4. Introduction Multiple Antibiotic Resistant (MAR) Salmonella Typhimurium DT104

5. Objective To determine the feasibility of using an MAR strain to model growth in naturally contaminated food

6. Materials and Methods Organism Salmonella Typhimurium DT 104 Food Ground chicken breast meat Inoculum BHI broth at 30 o C for 23 h

7. Materials and Methods Initial Density 10 3.8 CFU/g Temperatures 10 to 40 o C 5 replicates Viable Counts Selective media with 4 antibiotics XLH-CATS

8. Secondary Models Primary Model Primary Model C Model  max Model Model N o Model Observed N o Predicted N o Observed Predicted Observed  max Predicted  max Observed CPredicted C Predicted N(t) Observed N(t) Tertiary Model Predicted N(t) Materials and Methods Predictive Modeling

9. Materials and Methods Acceptable Prediction Zone (APZ) Method Performance Factor %RE = RE IN /RE TOTAL

10. Results and Discussion APZ Analysis: Tertiary Modeling (Verification) %RE = 50.7 (271/534)

11. Results and Discussion Primary Modeling (Example) Modified Gompertz N(t) = N o + C. [exp(-exp((2.718.  max /C). ( -t)+1))]

12. Results and Discussion APZ Analysis: Primary Modeling (Goodness-of-fit) %RE = 83.0 (433/534)

13. Results and Discussion Secondary Modeling for N o Quadratic Polynomial N o = 4.023 + 0.024T + 0.0003T 2

14. Results and Discussion APZ Analysis: Secondary Model for N o (Goodness-of-fit) %RE Replicates = 84.4 (38/45) %RE Mean = 100.0 (9/9)

15. Results and Discussion Secondary Modeling for Reverse, Two-phase Linear Model = 1.841 – [2.529. (T-22.64)] if T < 22.64 = 1.841 if T => 22.64

16. Results and Discussion APZ Analysis: Secondary Model for  (Goodness-of-fit) %RE Replicates = 57.8 (26/45) %RE Mean = 100.0 (9/9)

17. Results and Discussion Secondary Modeling for  max Logistic Model  max = 0.823/[1+((0.823/0.003502)-1). exp(-0.2127. T)]

18. Results and Discussion APZ Analysis: Secondary Model for  max (Goodness-of-fit) %RE Replicates = 48.9 (22/45) %RE Mean = 77.8 (7/9)

19. Results and Discussion Secondary Modeling for C Logistic Model C  = 6.052/[1+((6.052/0.00573)-1). exp(-0.3376. T)]

20. Results and Discussion APZ Analysis: Secondary Model for C (Goodness-of-fit) %RE Replicates = 33.3 (15/45) %RE Mean = 77.8 (7/9)

21. Conclusions Biological variation was responsible for unacceptable performance of the tertiary model. MAR strains can be used to develop models in naturally contaminated food. Stochastic modeling methods are needed to account for biological variation.