thomas p. oscar, ph.d. usda, ars princess anne, md

General Regression Neural Network General Regression Neural Network Model for Growth of Model for Growth of SalmonellaSalmonella

Serotypes on Chicken Skin for Use in Serotypes on Chicken Skin for Use in Risk AssessmentRisk Assessment

Thomas P. Oscar, Ph.D.Thomas P. Oscar, Ph.D.USDA, ARSUSDA, ARS

Princess Anne, MDPrincess Anne, MD

Risk AssessmentRisk AssessmentData GapsData Gaps

• Strain variationStrain variation • Microbial competitionMicrobial competition

• Initial doseInitial dose • Food matrix Food matrix

Strain VariationStrain Variation

• Salmonella entericaSalmonella enterica serotypes (> 2,300) serotypes (> 2,300)

– Top three in chickens are:Top three in chickens are:

Enteritidis Typhimurium Kentucky

Must have been that

chicken!

Strain VariationStrain Variation

Autoclaved chicken meat at 25C

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

Microbial CompetitionMicrobial Competition

J. Food Prot. (2003) 66(2):200-207; (2006) 69(2):276-281.

Microbial CompetitionMicrobial Competition

• Natural Antibiotic ResistanceNatural Antibiotic Resistance

– Bad for public healthBad for public health

– Good for predictive microbiologyGood for predictive microbiology

Salmonella Typhimurium DT104

J. Food Prot. (2006) 69(9):2048-2057.

J. Food Prot. (2008) 71(6):1135-1144.

J. Food Prot. (2009) 72(2):304-314.

Initial DoseInitial Dose

Food Microbiol. (2007) 24:640-651.

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)

Regression ModelingRegression Modeling

J. Food Prot. (2005) 68(12):2606-2613.

Neural Network ModelingNeural Network Modeling

• General Regression Neural Network (GRNN)General Regression Neural Network (GRNN)

– Better performance than regression modelsBetter performance than regression models

– User-friendly commercial softwareUser-friendly commercial software

• Compatible with Monte Carlo simulation softwareCompatible with Monte Carlo simulation software

Jeyamkondan et. al., 2001

ObjectiveObjective

• To develop a GRNN and simulation model for To develop a GRNN and simulation model for growth of growth of SalmonellaSalmonella on chicken skin as a on chicken skin as a function of serotype for use in risk assessment.function of serotype for use in risk assessment.

– Short-term temperature abuse (0 to 8 h)Short-term temperature abuse (0 to 8 h)

Materials and MethodsMaterials and Methods

• Experimental Design (3 x 10 x 5 x 2 x 2)Experimental Design (3 x 10 x 5 x 2 x 2)

– Serotypes (Typhimurium, Kentucky, Hadar)Serotypes (Typhimurium, Kentucky, Hadar)

• 30C in BHIB for 23 h at 150 opm } prehistory30C in BHIB for 23 h at 150 opm } prehistory

– Temperature (5, 10, 15, 20, 25, 30, 35, 40, 45, 50C)Temperature (5, 10, 15, 20, 25, 30, 35, 40, 45, 50C)

– Time (0, 2, 4, 6, 8 h)Time (0, 2, 4, 6, 8 h)

– Trial (1, 2)Trial (1, 2)

– Sample (a, b)Sample (a, b)

7 cfu

5 l

Materials and MethodsMaterials and Methods

D) Typhimurium; 35 C

0 2 4 6 80

2

4

6

8PredictedObserved

Time (h)

Lo

g n

um

ber

MPN

CFU

Materials and MethodsMaterials and Methods

• Plating MediaPlating Media

– XLH-CATS for TyphimuriumXLH-CATS for Typhimurium

– XLH-NATS for KentuckyXLH-NATS for Kentucky

– XLH-TUGS for HadarXLH-TUGS for Hadar

MPN drop plate

XL = xylose lysineH = HEPESC = chloramphenicolA = ampicillinT = tetracycline

S = streptomycinN = novobiocinU = sulfisoxazoleG = gentamicin

Poultry isolates

Ingredients

General Regression Neural NetworkGeneral Regression Neural Network

T t

… …-0.01 7.37

N(x) D(x)

ŷ

Input Layer

Pattern Layer

Summation Layer

Output

S

Specht, 1991 Serotype Temp. time

Distance Function

Predicted Value

Step 1Enter data

Step 2Define the data set

Step 3Set the training parameters

Step 4Train the GRNN

Step 5Review results

5 10 15 20 25 30 35 40 45 50

-2

-1

0

1

2

Hadar

TyphimuriumKentucky

A) Train (n = 464)

 0 2 4 6

80 2 4 6

80 2 4 6

80 2 4 6

80 2 4 6

80 2 4 6

80 2 4 6

80 2 4 6

80 2 4 6

80 2 4 6

8

Ch

Independent variables

Res

idual

(log

)

J. Food Prot. (2006) 69(9):2048-2057.

5 10 15 20 25 30 35 40 45 50

-2

-1

0

1

2

Hadar

B) Test (n = 116)

TyphimuriumKentucky

0 2 4 680 2 4 6

80 2 4 6

80 2 4 6

80 2 4 6

80 2 4 6

80 2 4 6

80 2 4 6

80 2 4 6

80 2 4 6

8

Ch

 Independent variables

Res

idual

(log

)

J. Food Prot. (2006) 69(9):2048-2057.

5 10 15 20 25 30 35 40 45 500

1

2

3

4

5

6

TyphimuriumKentuckyHadar

B) 5.3 h

Temperature (C)

Salmonella

(log)

0 1 2 3 4 5 6 7 80

1

2

3

4

5

6

TyphimuriumKentuckyHadar

A) 37C

Time (h)

Salmonella

(log)

Step 6Predict

D) Typhimurium; 35 C

0 2 4 6 80

2

4

6

8PredictedObserved

Time (h)

Log

num

ber

E) Kentucky; 35 C

0 2 4 6 80

2

4

6

8PredictedObserved

Time (h)Lo

g nu

mbe

r

F) Hadar; 35 C

0 2 4 6 80

2

4

6

8PredictedObserved

Time (h)

Log

num

ber

G) Typhimurium; 45 C

0 2 4 6 80

2

4

6

8PredictedObserved

Time (h)

Log

num

ber

H) Kentucky; 45 C

0 2 4 6 80

2

4

6

8PredictedObserved

Time (h)

Log

num

ber

I) Hadar; 45 C

0 2 4 6 80

2

4

6

8PredictedObserved

Time (h)Lo

g nu

mbe

r

Step 7Integrate with risk assessment

-1 0 1 2 3 4 50

2

4

6

8

10

Output dataDistribution fit

B) RiskLogistic(0.047094,0.056875)

Log change

Freq

uen

cy

-1 0 1 2 3 4 50

1

2

3

4A) RiskPearson5(4.4594,1.5797,RiskShift(-0.26825))

Output dataDistribution fit

Log change

Freq

uen

cy

Serotype Serotype (%)(%)

Temperature Temperature ((C)C)

Time Time (h)(h)

Log changeLog change

ScenarioScenario T_K_HT_K_H Min_ML_MaxMin_ML_Max Min_ML_MaxMin_ML_Max CorrelationCorrelation Min_50%_MaxMin_50%_Max

AA 31_58_1131_58_11 5_20_505_20_50 0_2_80_2_8 00 -0.21_0.09_4.8-0.21_0.09_4.8

BB 31_58_1131_58_11 5_20_505_20_50 0_2_80_2_8 -1-1 -0.16_0.04_0.5-0.16_0.04_0.5

Conclusion #1Conclusion #1

• Easy to developEasy to develop

• Low cost Low cost

• Flexible predictionsFlexible predictions

• Superior performanceSuperior performance

Neural network modeling outperforms regression modeling in predictive

microbiology applications

Conclusion #2Conclusion #2

• Cocktail of Typhimurium_Kentucky_HadarCocktail of Typhimurium_Kentucky_Hadar

– Overly ‘fail-safe’ predictions for Kentucky.Overly ‘fail-safe’ predictions for Kentucky.

Conclusion #3Conclusion #3

GRNN model was successfully validated for risk GRNN model was successfully validated for risk assessmentassessment

model?

-1 0 1 2 3 4 50

1

2

3

4A) RiskPearson5(4.4594,1.5797,RiskShift(-0.26825))

Output dataDistribution fit

Log change

Fre

quen

cy

Data Gaps

Strain variation

Microbial competition

Initial dose

Food matrix

AcknowledgementsAcknowledgements

• Thank you for your attention!Thank you for your attention!

• Thanks to Thanks to Jaci LudwigJaci Ludwig of ARS and of ARS and Celia Celia WhyteWhyte and and Olabimpe OlojoOlabimpe Olojo of UMES for of UMES for their outstanding technical assistance on their outstanding technical assistance on this project.this project.

I hope it was

Kentucky!