Variation among Batches of Freshly Ground Chicken Breast Meat Complicates the Modeling of
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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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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
Introduction Meat Complicates the Modeling of

  • Pure culture

  • Co-culture

    • Test pathogen

    • Competitor


Introduction1
Introduction Meat Complicates the Modeling of

  • Marker pathogen

    • Fluorescent (e.g. gfp)

    • Luminescent


Introduction2
Introduction Meat Complicates the Modeling of

  • Multiple Antibiotic Resistant (MAR)

    • Salmonella Typhimurium DT104


Objective
Objective Meat Complicates the Modeling of

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


Materials and methods
Materials and Methods Meat Complicates the Modeling of

  • Organism

    • Salmonella Typhimurium DT 104

  • Food

    • Ground chicken breast meat

  • Inoculum

    • BHI broth at 30oC for 23 h


Materials and methods1
Materials and Methods Meat Complicates the Modeling of

  • Initial Density

    • 103.8 CFU/g

  • Temperatures

    • 10 to 40oC

    • 5 replicates

  • Viable Counts

    • Selective media with 4 antibiotics

      • XLH-CATS


Materials and methods predictive modeling
Materials and Methods Meat Complicates the Modeling of Predictive 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 methods acceptable prediction zone apz method
Materials and Methods Meat Complicates the Modeling of Acceptable Prediction Zone (APZ) Method

Performance Factor %RE = REIN/RETOTAL


Results and discussion apz analysis tertiary modeling verification
Results and Discussion Meat Complicates the Modeling of APZ Analysis: Tertiary Modeling (Verification)

%RE = 50.7 (271/534)


Results and discussion primary modeling example
Results and Discussion Meat Complicates the Modeling of Primary Modeling (Example)

Modified Gompertz

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


Results and discussion apz analysis primary modeling goodness of fit
Results and Discussion Meat Complicates the Modeling of APZ Analysis:Primary Modeling (Goodness-of-fit)

%RE = 83.0 (433/534)


Results and discussion secondary modeling for n o
Results and Discussion Meat Complicates the Modeling of Secondary Modeling for No

Quadratic Polynomial

No = 4.023 + 0.024T + 0.0003T2


Results and discussion apz analysis secondary model for n o goodness of fit
Results and Discussion Meat Complicates the Modeling of APZ Analysis: Secondary Model for No (Goodness-of-fit)

%REReplicates = 84.4 (38/45)

%REMean = 100.0 (9/9)


Results and discussion secondary modeling for l
Results and Discussion Meat Complicates the Modeling of Secondary 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 discussion apz analysis secondary model for l goodness of fit
Results and Discussion Meat Complicates the Modeling of APZ Analysis: Secondary Model for l (Goodness-of-fit)

%REReplicates = 57.8 (26/45)

%REMean = 100.0 (9/9)


Results and discussion secondary modeling for m max
Results and Discussion Meat Complicates the Modeling of Secondary Modeling for mmax

Logistic Model

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


Results and discussion apz analysis secondary model for m max goodness of fit
Results and Discussion Meat Complicates the Modeling of APZ Analysis: Secondary Model for mmax (Goodness-of-fit)

%REReplicates = 48.9 (22/45)

%REMean = 77.8 (7/9)


Results and discussion secondary modeling for c
Results and Discussion Meat Complicates the Modeling of Secondary Modeling for C

Logistic Model

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


Results and discussion apz analysis secondary model for c goodness of fit
Results and Discussion Meat Complicates the Modeling of APZ Analysis: Secondary Model for C (Goodness-of-fit)

%REReplicates = 33.3 (15/45)

%REMean = 77.8 (7/9)


Conclusions
Conclusions Meat Complicates the Modeling of

  • 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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