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

Salmonella Growth Kinetics

Thomas P. OscarUSDA, ARS

Microbial Food Safety Research UnitUniversity 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

Secondary Models

PrimaryModel

PrimaryModel

CModel

max

Model

Model

No

Model

Observed No Predicted No

Observed Predicted

Observed max Predicted max

Observed C Predicted C

PredictedN(t)

ObservedN(t)

TertiaryModel

PredictedN(t)

Materials and MethodsPredictive Modeling

Materials and MethodsAcceptable Prediction Zone (APZ) Method

"Acceptable"

"Overly Fail-safe"

"Overly Fail-dangerous"

4 5 6 7 8 9 10 11-1.2

-0.8

-0.4

-0.0

0.4

0.8

1.2

1.6

Predicted N(t) (log CFU/g)

Rel

ativ

e er

ror

Performance Factor %RE = REIN/RETOTAL

Results and DiscussionAPZ Analysis: Tertiary Modeling (Verification)

10 15 20 25 30 35 40

-2

0

2

4

6

8

10

Temperature (C)

RE

%RE = 50.7 (271/534)

Results and DiscussionPrimary Modeling (Example)

Modified GompertzN(t) = No + C.[exp(-exp((2.718.max/C).(-t)+1))]

0 25 50 75 100 1250

2

4

6

8

1012C

Time (h)

S. T

yphi

mur

ium

DT

104

(log

CF

U/g

)

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

10 15 20 25 30 35 40

-3

-2

-1

0

1

2

3

Temperature (C)

RE

%RE = 83.0 (433/534)

Results and DiscussionSecondary Modeling for No

Quadratic PolynomialNo = 4.023 + 0.024T + 0.0003T2

10 15 20 25 30 35 40

3

4

5ReplicatesMean

Temperature (C)

No

(log

CF

U/g

)

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

10 15 20 25 30 35 40

-2.0-1.5-1.0-0.50.00.51.01.52.0 Replicates

Mean

Temperature (C)

RE

%REReplicates = 84.4 (38/45)%REMean = 100.0 (9/9)

Results and DiscussionSecondary Modeling for

Reverse, Two-phase Linear Model = 1.841 – [2.529.(T-22.64)] if T < 22.64 = 1.841 if T => 22.64

10 15 20 25 30 35 40

0

10

20

30

40

50

60ReplicatesMean

Temperature (C)

(h

)

10 15 20 25 30 35 40

-2.0-1.5-1.0-0.50.00.51.01.52.0 Replicates

Mean

Temperature (C)

RE

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

%REReplicates = 57.8 (26/45)%REMean = 100.0 (9/9)

Results and DiscussionSecondary Modeling for max

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

10 15 20 25 30 35 40

0.0

0.2

0.4

0.6

0.8

1.0ReplicatesMean

Temperature (C)

max

(h-1

)

10 15 20 25 30 35 40

-2.0-1.5-1.0-0.50.00.51.01.52.0 Replicates

Mean

Temperature (C)

RE

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

%REReplicates = 48.9 (22/45)%REMean = 77.8 (7/9)

Results and DiscussionSecondary Modeling for C

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

10 15 20 25 30 35 40

012345678

ReplicatesMean

Temperature (C)

C (

log

CF

U/g

)

10 15 20 25 30 35 40

-2.0-1.5-1.0-0.50.00.51.01.52.0 Replicates

Mean

Temperature (C)

RE

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.