Modeling the Survival and Growth of Modeling the Survival and Growth of SalmonellaSalmonella on Chicken Skin Stored on Chicken Skin Stored

at 4 to 12at 4 to 12CC

Thomas P. Oscar, Ph.D.Thomas P. Oscar, Ph.D.U.S. Department of AgricultureU.S. Department of AgricultureAgricultural Research ServiceAgricultural Research Service

Princess Anne, MDPrincess Anne, MD

IntroductionIntroduction

SalmonellaSalmonella & poultry & poultry

1 - 2 cases per 100,0001 - 2 cases per 100,000

Initial contaminationInitial contamination

< 30 CFU per chicken carcass< 30 CFU per chicken carcass

Illness doseIllness dose

101055 to 10 to 1077 CFU CFU

Int. J. Food Microbiol. 2004. 93:231-247.

IntroductionIntroduction

Risk AssessmentRisk Assessment Hazard IdentificationHazard Identification

Hazard CharacterizationHazard Characterization

Exposure AssessmentExposure Assessment

Risk CharacterizationRisk Characterization

PackagingContamination

Cold StorageTemp. Abuse

Meal Prep.Temp. Abuse

CookingUnder-cooking

ConsumptionExposure

Meal Prep.Cross-contamination

Risk Pathway

IntroductionIntroduction

Predictive microbiologyPredictive microbiology

Support risk assessmentsSupport risk assessments

Data gapsData gaps

Low initial doseLow initial dose

Microbial competitionMicrobial competition

Low temperaturesLow temperatures

IntroductionIntroduction

Another data gapAnother data gap

Variation among Variation among

serotypesserotypes

Autoclaved chicken meat at 25C

J. Food Safety. 2000. 20:225-236.

ObjectiveObjective

Develop a predictive modelDevelop a predictive model Survival & growthSurvival & growth

SalmonellaSalmonella Typhimurium & Typhimurium &

KentuckyKentucky

Low initial dose (0.9 log)Low initial dose (0.9 log)

Chicken thigh skin (2.14 cmChicken thigh skin (2.14 cm22) )

with microbial competitionwith microbial competition

Low temperature (4 to 12Low temperature (4 to 12C)C)

Materials & MethodsMaterials & Methods

Experimental designExperimental design

Model development Model development ((SalmonellaSalmonella serotype Typhimurium serotype Typhimurium

DT104)DT104)

5 x 5 full factorial5 x 5 full factorial

Temperature (4, 6, 8, 10, 12Temperature (4, 6, 8, 10, 12C)C)

Time (0, 1, 3, 6, 10 days)Time (0, 1, 3, 6, 10 days)

4 replicates4 replicates

Materials & MethodsMaterials & Methods

Experimental designExperimental design

Model validation Model validation ((SalmonellaSalmonella serotype Typhimurium serotype Typhimurium

DT104)DT104)

4 x 5 full factorial4 x 5 full factorial

Temperature (5, 7, 9, 11Temperature (5, 7, 9, 11C)C)

Time (0, 1, 3, 6, 10 days)Time (0, 1, 3, 6, 10 days)

2 replicates2 replicates

Materials & MethodsMaterials & Methods

Experimental designExperimental design

Model validation (Model validation (SalmonellaSalmonella serotype serotype

Kentucky)Kentucky)

4 x 5 full factorial4 x 5 full factorial

Temperature (5, 7, 9, 11Temperature (5, 7, 9, 11C)C)

Time (0, 1, 3, 6, 10 days)Time (0, 1, 3, 6, 10 days)

2 replicates2 replicates

Materials & MethodsMaterials & Methods

SalmonellaSalmonella enumeration enumeration

Combined MPN & CFU methodCombined MPN & CFU method

D) Typhimurium; 35 C

0 2 4 6 80

2

4

6

8PredictedObserved

Time (h)

Lo

g n

um

ber

MPN CFU

J. Food Prot. 2006. 69:2048-2057.

Materials & MethodsMaterials & Methods

Plating mediaPlating media

SalmonellaSalmonella serotype Typhimurium DT104 serotype Typhimurium DT104

XLH-CATSXLH-CATS

SalmonellaSalmonella serotype Kentucky serotype Kentucky

XLH-NATSXLH-NATS

J. Food Prot. 2006. 69:2048-2057.

Materials & MethodsMaterials & Methods General Regression Neural Network ModelGeneral Regression Neural Network Model

T t

… …-0.71 4.13

N(x) D(x)

ŷ

Input Layer

Pattern Layer

Summation Layer

Output (Δ)

Temp. time

Distance Function

Predicted Value

IEEE Trans. Neural. Netw. 1991. 2:568-576

Materials & MethodsMaterials & Methods

Model performanceModel performance

ResidualResidual

Observed - predictedObserved - predicted

Acceptable prediction zone (APZ)Acceptable prediction zone (APZ)

-1 log (fail-safe) to 0.5 log (fail-dangerous)-1 log (fail-safe) to 0.5 log (fail-dangerous)

Acceptable performanceAcceptable performance

70% of residuals in APZ70% of residuals in APZ

Prediction bias & accuracy

J. Food Sci. 2005. 70:M129-M137.

ResultsResults

SalmonellaSalmonella serotype Typhimurium DT104 serotype Typhimurium DT104

Model development (Model development (nn = 163) = 163)

4 C

0 2 4 6 8 10-1

0

1

2

3

4ObservedPredicted

Time (d)

(l

og)

10 C

0 2 4 6 8 10-1

0

1

2

3

4ObservedPredicted

Time (d)

(lo

g)

8 C

0 2 4 6 8 10-1

0

1

2

3

4ObservedPredicted

Time (d)

(lo

g)

6 C

0 2 4 6 8 10-1

0

1

2

3

4ObservedPredicted

Time (d)

(lo

g)

12 C

0 2 4 6 8 10-1012345

ObservedPredicted

Time (d)

(lo

g)

ResultsResults

SalmonellaSalmonella serotype Typhimurium DT104 serotype Typhimurium DT104

Model validation for interpolation (Model validation for interpolation (nn = 77) = 77)

5 C

0 2 4 6 8 10-1

0

1

2

3

4ObservedPredicted

Time (d)

(l

og)

7 C

0 2 4 6 8 10-1

0

1

2

3

4ObservedPredicted

Time (d)

(log

)9 C

0 2 4 6 8 10-1

0

1

2

3

4ObservedPredicted

Time (d)

(l

og)

11 C

0 2 4 6 8 10-1

0

1

2

3

4ObservedPredicted

Time (d)

(l

og)

DiscussionDiscussion

SalmonellaSalmonella serotype Typhimurium DT104 serotype Typhimurium DT104

Growth on sterile chicken breast meat at 10Growth on sterile chicken breast meat at 10CC

0 5 10 15 200

2

4

6

8

10

Time (d)

log

CFU

/g

Oscar (unpublished)

ResultsResults

SalmonellaSalmonella serotype Kentucky serotype Kentucky

Model validation for extrapolation (Model validation for extrapolation (nn = 70) = 70)

5 C

0 2 4 6 8 10-1

0

1

2

3

4ObservedPredicted

Time (d)

(l

og)

7 C

0 2 4 6 8 10-1

0

1

2

3

4ObservedPredicted

Time (d)

(log

)9 C

0 2 4 6 8 10-1

0

1

2

3

4ObservedPredicted

Time (d)

(l

og)

11 C

0 2 4 6 8 10-1

0

1

2

3

4ObservedPredicted

Time (d)

(l

og)

DiscussionDiscussion

Variation among serotypesVariation among serotypes

Kentucky grows slower on chicken skin at Kentucky grows slower on chicken skin at

3535CCTyphimurium DT104

0 2 4 6 80

2

4

6

8PredictedObserved

Time (h)

Log

num

ber

Kentucky

0 2 4 6 80

2

4

6

8PredictedObserved

Time (h)

Log

num

ber

J. Food Prot. 2009. 72:2078-2087.

Results & DiscussionResults & Discussion

Model Performance (Development)Model Performance (Development)

-3

-2

-1

0

1

2

3 A) Dependent

0 1 3 6 10

APZ = 85.3%

   0 1 3 6 10 0 1 3 6 10 0 1 3 6 10 0 1 3 6 10

4C 6C 8C 10C 12C

Time (d)

Res

idu

al (

log

)

Results & DiscussionResults & Discussion

Model Performance (Interpolation)Model Performance (Interpolation)

-3

-2

-1

0

1

2

3 B) Interpolation

0 1 3 6 10

APZ = 84.4%

   0 1 3 6 10 0 1 3 6 10 0 1 3 6 10

5C 7C 9C 11C

Time (d)

Re

sid

ua

l (lo

g)

Results & DiscussionResults & Discussion

Model Performance (Extrapolation)Model Performance (Extrapolation)

-3

-2

-1

0

1

2

3 C) Extrapolation

0 1 3 6 10

APZ = 87.1%

   0 1 3 6 10 0 1 3 6 10 0 1 3 6 10

5C 7C 9C 11C

Time (d)

Res

idu

al (

log

)

Results & DiscussionResults & Discussion

ConclusionsConclusions

Model was validatedModel was validated

Microbial competition suppresses growthMicrobial competition suppresses growth

MPD = 1 log vs 8 log @ 10MPD = 1 log vs 8 log @ 10CC

Kentucky grows slowerKentucky grows slower

Compatible with @RiskCompatible with @Risk