Variation among Batches of Freshly Ground Chicken Breast Meat Complicates the Modeling of
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Thomas P. Oscar USDA, ARS Microbial Food Safety Research Unit University of Maryland Eastern Shore PowerPoint PPT Presentation


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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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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

    • Competitor


Introduction

  • Marker pathogen

    • Fluorescent (e.g. gfp)

    • Luminescent


Introduction

  • Multiple Antibiotic Resistant (MAR)

    • Salmonella Typhimurium DT104


Objective

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


Materials and Methods

  • Organism

    • Salmonella Typhimurium DT 104

  • Food

    • Ground chicken breast meat

  • Inoculum

    • BHI broth at 30oC for 23 h


Materials and Methods

  • Initial Density

    • 103.8 CFU/g

  • Temperatures

    • 10 to 40oC

    • 5 replicates

  • Viable Counts

    • Selective media with 4 antibiotics

      • XLH-CATS


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


Materials and MethodsAcceptable Prediction Zone (APZ) Method

Performance Factor %RE = REIN/RETOTAL


Results and DiscussionAPZ Analysis: Tertiary Modeling (Verification)

%RE = 50.7 (271/534)


Results and DiscussionPrimary Modeling (Example)

Modified Gompertz

N(t) = No + C.[exp(-exp((2.718.mmax/C).(l-t)+1))]


Results and DiscussionAPZ Analysis:Primary Modeling (Goodness-of-fit)

%RE = 83.0 (433/534)


Results and DiscussionSecondary Modeling for No

Quadratic Polynomial

No = 4.023 + 0.024T + 0.0003T2


Results and DiscussionAPZ Analysis: Secondary Model for No (Goodness-of-fit)

%REReplicates = 84.4 (38/45)

%REMean = 100.0 (9/9)


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


Results and DiscussionAPZ Analysis: Secondary Model for l (Goodness-of-fit)

%REReplicates = 57.8 (26/45)

%REMean = 100.0 (9/9)


Results and DiscussionSecondary Modeling for mmax

Logistic Model

mmax = 0.823/[1+((0.823/0.003502)-1).exp(-0.2127.T)]


Results and DiscussionAPZ Analysis: Secondary Model for mmax (Goodness-of-fit)

%REReplicates = 48.9 (22/45)

%REMean = 77.8 (7/9)


Results and DiscussionSecondary Modeling for C

Logistic Model

C= 6.052/[1+((6.052/0.00573)-1).exp(-0.3376.T)]


Results and DiscussionAPZ Analysis: Secondary Model for C (Goodness-of-fit)

%REReplicates = 33.3 (15/45)

%REMean = 77.8 (7/9)


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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