modeling the survival and growth of salmonella on chicken skin stored at 4 to 12 c

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 doseMicrobial competitionMicrobial competitionLow temperaturesLow temperatures

IntroductionIntroduction

Another data gapAnother data gap Variation among Variation among

serotypesserotypesAutoclaved 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)

Log

num

ber

MPN CFU

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

Materials & MethodsMaterials & Methods

Plating mediaPlating mediaSalmonellaSalmonella serotype Typhimurium DT104 serotype Typhimurium DT104

XLH-CATSXLH-CATS

SalmonellaSalmonella serotype Kentucky serotype KentuckyXLH-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 10-101234

ObservedPredicted

Time (d)

(l

og)

10 C

0 2 4 6 8 10-101234

ObservedPredicted

Time (d)

(l

og)

8 C

0 2 4 6 8 10-101234

ObservedPredicted

Time (d)

(lo

g)

6 C

0 2 4 6 8 10-101234

ObservedPredicted

Time (d)

(lo

g)

12 C

0 2 4 6 8 10-1012345

ObservedPredicted

Time (d)

(l

og)

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 10-101234

ObservedPredicted

Time (d)

(l

og)

7 C

0 2 4 6 8 10-101234

ObservedPredicted

Time (d)

(log

)9 C

0 2 4 6 8 10-101234

ObservedPredicted

Time (d)

(l

og)

11 C

0 2 4 6 8 10-101234

ObservedPredicted

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 10-101234

ObservedPredicted

Time (d)

(lo

g)

7 C

0 2 4 6 8 10-101234

ObservedPredicted

Time (d)

(log)9 C

0 2 4 6 8 10-101234

ObservedPredicted

Time (d)

(lo

g)

11 C

0 2 4 6 8 10-101234

ObservedPredicted

Time (d)

(l

og)

DiscussionDiscussion Variation among serotypesVariation among serotypes

Kentucky grows slower on chicken skin at Kentucky grows slower on chicken skin at 3535CC

Typhimurium 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

idua

l (lo

g)

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)

Resi

dual

(log

)

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

idua

l (lo

g)

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