T4-04Predictive Model for Growth of Salmonella Typhimurium

DT104 on Ground Chicken Breast Meat

Thomas P. Oscar, Ph.D.USDA-ARS, Microbial Food Safety Research Unit

and USDA, Center of Excellence Program

University of Maryland Eastern Shore

Princess Anne, MD

Ground Chicken Survey 1996

• Natural Microflora

– 100% (25-g sample)

– 4.6 log CFU/g

• Salmonella

– 45% (25-g sample)

– 0.1 log MPN/g

Hurdles for modeling Salmonella growth on chicken with a natural microflora

• Use of a low initial density

• Strain with a proper phenotype

Salmonella Typhimurium DT104

• Occurs in nature

• Low prevalence on chicken

• Resistant to multiple antibiotics

• Stable phenotype

• Growth similar to other strains

Growth of Salmonella Typhimurium DT104 (ATCC 700408) from High Initial Density (103.8 CFU/g) on Ground Chicken Breast Meat

with a Natural Microflora

Oscar, T. P. 2006. (unpublished data)

10 15 20 25 30 35 40

0.0

0.2

0.4

0.6

0.8

1.0ReplicatesMean

Temperature (C)

(h

-1)

Objective

• To overcome the hurdles for developing and

validating a predictive model for growth of

Salmonella on ground chicken with a natural

microflora.

Challenge Study

• S. Typhimurium DT104

– ATCC 700408

• Stationary phase cells

– BHI broth at 30oC for 23 h

• Initial Density

– 0.6 log MPN or CFU/g

• Ground chicken breast meat

– 1 gram portionsJacquelyn B. Ludwig

Experimental Design

• Model development

– 10, 12, 14, 22, 30, 40oC

• Model evaluation

– 11, 18, 26, 34oC

• Replication

– 5 batches per temperature

To assess variation of pathogen growth

Pathogen Enumeration

• MPN (0 to 3.28 log MPN/g)

– 3 x 4 assay in BPW

– Spot (2 l) onto XLH-CATS

• CFU (> 3 log CFU/g)

– Direct plating on XLH-CATS

Xylose-lysine agar base with 25 mM HEPES (buffering agent) plus 25 g/ml of the following antibiotics: chloramphenicol (C), ampicillin (A), tetracycline (T)

and streptomycin (S).

Primary Modeling

95% PI

MPN & CFU

0 10 20 30 40 50 60 700

1

2

3

4

5

6

7

8

Time (h)

log

MPN

or

CFU

/g

N(t) = [Nmax/(1 + ((Nmax/No) – 1) * exp (- * t))]

Comparison of MPN and CFU

Sample T (oC) Time (h) log MPN/g log CFU/g

1 11 175.9 3.28 3.082 14 38.7 3.09 3.303 14 68.0 3.28 3.384 26 8.7 2.95 3.655 26 9.7 3.28 3.666 30 6.0 2.95 3.007 30 6.8 3.00 3.518 40 4.4 3.09 3.56

Mean 3.12 3.39ab

Means with different superscripts differ at P < 0.05

0 100 200 3000

2

4

6

8

10

12 10C

Time (h)

log

MPN

or

CFU

/g

0 50 100 150 2000

2

4

6

8

10

12 12C

Time (h)

log

MPN

or

CFU

/g

0 50 100 150 2000

2

4

6

8

10

12 14C

Time (h)

log

MPN

or

CFU

/g

0 10 20 30 40 50 60 700

2

4

6

8

10

12 22C

Time (h)

log

MPN

or

CFU

/g

0 10 20 30 40 500

2

4

6

8

10

12 30C

Time (h)

log

MPN

or

CFU

/g

0 10 20 30 40 500

2

4

6

8

10

12 40 C

Time (h)

log

MPN

or

CFU

/g

PrimaryModeling

DependentData

Temp. Nmax (log/g)

10oC 1.63

12oC 2.70

14oC 4.98

22oC 6.43

30oC 8.49

40oC 9.36

0 50 100 150 2000

2

4

6

8

10

12 11C

Time (h)

log

MP

N o

r C

FU

/g

0 25 50 75 100 125 1500

2

4

6

8

10

12 18C

Time (h)

log

MPN

or

CFU

/g0 25 50 75 100

0

2

4

6

8

10

12 26C

Time (h)

log

MPN

or

CFU

/g

0 10 20 30 400

2

4

6

8

10

12 34C

Time (h)lo

g M

PN o

r C

FU/g

Primary Modeling

IndependentData

Temp. Nmax (log/g)

11oC 2.28

18oC 5.34

26oC 7.63

34oC 9.29

Performance EvaluationSecondary Models

• Relative Error (RE)

– and Nmax = (O – P) / P

– 95% PI = (P – O) / P

• Acceptable Prediction Zone

– = -0.3 to 0.15

– Nmax and PI = -0.8 to 0.40

• % RE

– REIN / RETOTAL

– > 70% = acceptable

"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

1. Oscar, T. P. 2005. J. Food Sci. 70:M129-M137. 2. Oscar, T. P. 2005. J. Food Prot. 68:2606-2613.

Secondary Model for

5 10 15 20 25 30 35 40 450.0

0.1

0.2

0.3

0.4

0.5

IndependentDependent

Temperature (C)

(h

-1)

%RE83

100

= i if T <= To

= opt/[1 + ((opt/i) - 1)* exp (-rate (T – To)] if T > To

i = 0.047 h-1

To = 15.6oC

rate = 0.22 h-1/oC

opt = 0.41 h-1

Secondary Model for Nmax

5 10 15 20 25 30 35 40 450

2

4

6

8

10

12

IndependentDependent

Temperature (C)

Nm

ax (

log

MP

N o

r C

FU

/g)

%RE8375

Nmax = exp[(a * [(T – Tmin)/(T – Tsubmin)])]

a = 2.47

Tmin = 9.11oC

Tsubmin = 5.66oC

Secondary Model for 95% Prediction Interval

5 10 15 20 25 30 35 40 450.0

0.5

1.0

1.5

2.0

2.5

3.0

IndependentDependent

Temperature (C)

PI

(log

/g)

%RE10050

PI1 = 1.33 log/g

PI2 = 2.58 log/g

PI3 = 1.94 log/g

T1 = 10oC

T2 = 14.8oC

T3 = 26.9oC

Secondary Models

PrimaryModel

PrimaryModel

Nmax

Model

Model

PIModel

Model

Observed Predicted

Observed PI Predicted PI

Observed Predicted

Observed Nmax Predicted Nmax

PredictedN(t)

ObservedN(t)

TertiaryModel

PredictedN(t)

Tertiary Modeling

Performance Evaluation Tertiary Model

• 90% Concordance

– N(t)IN / N(t)TOTAL > 90%

• Dependent Data

– 93% (322/344)

• Independent Data

– 94% (223/236)

Oscar, T. P. 2006. J. Food Prot. (in press)

Summary

• MPN and CFU data can be used in tandem to model pathogen growth from a low initial density.

• 95% PI provides a simple stochastic method for modeling variation of pathogen growth among batches of food with natural microflora.

• 90% concordance is a simple method for validating stochastic models.