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Thomas P. Oscar USDA, ARS Microbial Food Safety Research Unit University of Maryland Eastern Shore

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. Introduction. Pure culture Co-culture Test pathogen

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Thomas P. Oscar USDA, ARS Microbial Food Safety Research Unit University of Maryland Eastern Shore

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  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 30oC for 23 h

  7. Materials and Methods • Initial Density • 103.8 CFU/g • Temperatures • 10 to 40oC • 5 replicates • Viable Counts • Selective media with 4 antibiotics • XLH-CATS

  8. Materials and MethodsPredictive Modeling Secondary Models Tertiary Model No Model Observed No Predicted No Observed N(t) l Model Observed l Predicted l Primary Model Primary Model mmax Model Observed mmax Predicted mmax Predicted N(t) Predicted N(t) C Model Observed C Predicted C

  9. Materials and MethodsAcceptable Prediction Zone (APZ) Method Performance Factor %RE = REIN/RETOTAL

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

  11. Results and DiscussionPrimary Modeling (Example) Modified Gompertz N(t) = No + C.[exp(-exp((2.718.mmax/C).(l-t)+1))]

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

  13. Results and DiscussionSecondary Modeling for No Quadratic Polynomial No = 4.023 + 0.024T + 0.0003T2

  14. Results and DiscussionAPZ Analysis: Secondary Model for No (Goodness-of-fit) %REReplicates = 84.4 (38/45) %REMean = 100.0 (9/9)

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

  16. Results and DiscussionAPZ Analysis: Secondary Model for l (Goodness-of-fit) %REReplicates = 57.8 (26/45) %REMean = 100.0 (9/9)

  17. Results and DiscussionSecondary Modeling for mmax Logistic Model mmax = 0.823/[1+((0.823/0.003502)-1).exp(-0.2127.T)]

  18. Results and DiscussionAPZ Analysis: Secondary Model for mmax (Goodness-of-fit) %REReplicates = 48.9 (22/45) %REMean = 77.8 (7/9)

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

  20. Results and DiscussionAPZ Analysis: Secondary Model for C (Goodness-of-fit) %REReplicates = 33.3 (15/45) %REMean = 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.

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