A mathematical risk model for Escherichia coli O157:H7 in ready to eat vegetables Departamento de...

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A mathematical risk model for A mathematical risk model for Escherichia coli Escherichia coli O157:H7 in ready O157:H7 in ready

to eat vegetablesto eat vegetables

Departamento de Bromatología y Tecnología de los Alimentos. Universidad de Córdoba

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Pérez Rodríguez, Fernando1; Todd, Ewen2

1. Food Science and Technology, University of Córdoba, Córdoba, Spain.2. Todd Ewen Consulting, Okemos, MI, USA.

Summary

• Framework• Scope and assumptions• Factors at factory• Food Distribution Chain• Simulation settings• Implenting model into an on-line object-

orientated software

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Completing the leafy green Picture

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E. coli VTEC transmission via vegetable products is a serious concern :

Large outbreak in Germany associated with O104:H4 (Bielaszewska et al. 2011).

Unknown transmission routes and contamination sources.

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•Composting Methodology (CU)

•Internalization/Surface Contamination

Methodology (UGA and CU

•Cross-Contamination Methodology (MSU)

•Processing Water Detection Methodology (IIT)

•Long-wave X-ray Intervention Methodology (MSU)

•Sanitizing and Physical Interventions Methodology (IIT)

•Storage Studies Methodology (IIT)

Project Context and Goals

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RISK ASSESSMENTRISK ASSESSMENT

Completing the leafy green Picture

Context & Scope– According to available data, E. coli O157:H7 is rarely able to survive in

– The model will evaluate the effect of different processing steps in ONE factory and subsequent stages during the Distribution Chain until the Consumer phase.

– As contamination by E. coli O157:H7 is sporadic event (Doyle & Eriksson, 2007), it is assumed that only one contaminated batch could came in the processing line

– The model will trace the changes in concentration and prevalence of the contaminated batch and batches cross contaminated during production.

– The model estimates the risk by E. coli O157:H7 associated with consumption of RTE leafy vegetables coming from ONE factory.

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NO MODEL FOR PRIMARY PRODUCTINO MODEL FOR PRIMARY PRODUCTION

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Model OverviewE coli O157:H7 transmission and risk estimation associated with a contaminated batch

Factory ModelMain inputs and models included in the cross-contamination model

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Input Model Parameters Source

Survival on equipment *log(cfu)s=log(cfu)s-1-(ts/b)a a= 0.17; b=0.02unpublished data

Removal by washing Uniform (min,max) min=1 ; max=1.5 Han et al. (2000) ;Singh et al.

(2002ª);Reduction in water

by chlorine**log(cfu) f=µ· log(cfu)i+1 µ=-0.02 unpublished data

Reduction on produceby chlorine

Uniform (min,max) min=0.20; max=0.80

Singh et al. (2002b);Marks et al. (2009)

Reduction on produceby irradiation

log(cfu) f=-(1/D)· log(cfu)i

D =0.11 KGy Unpublished

Bacterial distribution in processed lettuce

Negative Binomial(∑binomial(C*S))

S= bagged sizedC= cfuf/g

Perez-Rodriguez et al. (2007)

*log(cfu)s: concentration on equipment at any time (ts); a and b are fitting parameters based on Weibull model; ts is the time that microorganisms are on equipment surfaces (h).

**µ is the reduction rate of Escherichia coli O157:H7 in chlorinated water; log(cfu) f: concentration on lettuce after treatment; log(cfu) i: concentration on lettuce before treatment

Extending a previous model

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• An Exposure assessment models was developed (Pérez-Rodriguez et al., 2010) to describe cross contamination during processing of leafy grean at factory. Also, the impact of different control measures and decontamination steps on the contamination by E. coli O157:H7 on final product were assessed

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Non contaminated batches

SHREDDERSHREDDER

Tr_1(a) (%)Tr_1(a) (%) Tr_2(a) (%)Tr_2(a) (%) Tr_3(a) (%)Tr_3(a) (%)

Tr_1(b) (%)Tr_1(b) (%) Tr_2(b) (%)Tr_2(b) (%) Tr_3(b) (%)Tr_3(b) (%)

Concentration and Prevalence in bags

S1=0.01 cfu/g S2 = 1 cfu/gContaminated batch

Transfer type Distribution (%)

Tr_1 (a) Beta(0.12;0.19)

Tr_2(a) Beta (0.18;0.21)

Tr_3(c) Beta (0.20;0.22)

Tr_1(b)+Tr_2(b)+Tr_3(b) 10 Normal(-0.83; 0.15)

Simulated scenarios

Transfer: distributons

Packaging= Poisson D.

Lettuce Processing line Inputs

Output

Packaging

Number of batches processes: 22 ( ≈ 3 batches/h)Batch size = 1000 kgNumber of bags per batch: 10000Bag size: 100 g

FLUMEFLUME SHAKERSHAKER

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Transfer routes

Loop Bidirectional Interrelated

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•Data for E. coli O157:H7 lettuce and also spinach

•Initial concentration on artificially-inoculated Lettuce entering processing line.

•Final concentration on cross contaminated Lettuce

•Contamination level on specific sampling zones in the equipment suface before uninoculated lettuce come in the processing line.

Experimental data

Factory model: Transfer

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Different experiment was developed in Pilot- Scale to generate data

ShredderConveyor

Flume tank

Shaker tableCentrifugal drier

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Factory model: Transfer

Factory model: transfer

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ROUTE 1: Contaminated leafy green product to Equipment:

Using data from experiments with contaminated lettuce and spinach processed at pilot-scale plant and sampling different locations

ROUTE 2: Contaminated equipment to non-contaminated product

Similar experiments tracing contamination from a contaminated batch to non-contaminated through equipment and process water.

ROUTE 3: Distribution in bags of the contaminated pieces (trims) derived from the originally contaminated batch.

Experiments performed using two differentiated lettuces (Iceberg and Radicchio). From this, contamination contribution to each bag was quantified based on number of Radicchio pieces (originally contaminated batch)

Factory model: transferTransfer (at low level) Maximum Mininum Mean Ditribution

Spinach-Centrifuge 0.08 0.01 0.04 Beta

Spinach-Flume 0.24 0.00 0.06 Beta

Spinach-Shaker 0.12 0.00 0.06 Beta

Spinach-Water 52.65 0.00 10.78 Beta

Lettuce-Shredder 0.02 0.00 0.02 Log-Normal

Lettuce-Flume 0.02 0.00 0.01 Log-Normal

Lettuce-Shaker 0.02 0.00 0.01 Log-Normal

Lettuce-Conveyor 0.24 0.00 0.10 Log-Normal

Lettuce-Water 10.46 0.00 8.79 Beta

Equipment-Lettuce 18.83 9.90 15.33 Log-Normal

Transfer data expressed as porcentage (%)

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Factory model: transferBeta distribution describing transfer rates:

Contaminated spinachShaker

Beta (0.18045,0.21231)

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Storage and Consumption

CONSUMPTION: 25 g SERVING

Modeling E. coli O157:H7 Growth

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MODEL by Koseki & Isobe (2000)

unpackaged –Lettuce: 5-25 C

Data in packaged spinach inoculated with non-virulent O157 strains (IIT ):

CO2/O2 and 5-15º COnly growth at 12 and 15º C

Bioscreen experiments (turbity) developed in vegetable juices (parsley, spinach, chard, etc.) inoculated with a cocktail NaR O157 strain

8-20 º C

Growth in LETTUCE: Koseki

& Isobe

Validation of Bioscreen for

SPINACH model using ITT´s data

Few suitable models in scientific literature:

Modeling E. coli O157:H7 Growth

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Growth in LETTUCE: Koseki

& Isobe

Validation of Bioscreen for

SPINACH model using ITT´s data

ALGORITHM TO DETERMINE GROWTH OR NO GROWTH:

BOUNDARY MODEL

NO GROWTH

GROWTHY/N

Salter et al. 2000

Although lag time was also modeled; predictions were realized assuming no lag.

Storage and consumption model

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LEAVING FACTORY

ARRIVING WAREHOUSE

LEAVING WAREHOUSE

RETAIL

TIME (H)

SPANISH DATA: CHILL CHAIN

Modeling retail temperature

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in the botton (33%) in the middle (33%)in the top (33%)

Time-Temperature profiles in refrigerated cabinets at retail in the U.S. (Nunes et al., 2009)

Temperature Variability was added thorugh a Uniform distribution (shaded zones)

0 10 20 30 40 50 60 70 80

Temperature(C)

Time (h)

Modeling Household temperature

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5.0% 90.0% 5.0%

-0.40 9.42

-10-505101520

Temperature (ºC)

Probability

TEMPERATURE were taken from ECOSURE study household refrigerated deli-salad items. Levels can be as high as 15 C.

TIME were modeled through a exponential distribution, and considering consumption after expiry date. Carrasco et al, 2008 determined that 9.9 % consumers use salad bags after expiry date.

This study also confirms that 46 % consumers use part of the salad bag keeping the rest in refrigeration for next use. No data about storage time once bags are opened.

Modeling Dose-response

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• Various D-R models were simulated (Strachan et al., 2005; Teunis et al., 2008) :

– Beta-Poisson D-R Model (Strachan et al., 2005)P_ill =1-(1+(Dose/2.22) -0.057)

– Exponential D-R model (Strachan et al. 2005 )P_ill= 1-exp(-Dose*0.0011)

Simulation Parameters• Simulation:

– The simulation consisted in 10 uncertainty realizations and 5,000 variability iterations.

– Simulation Seed used by RNG was fixed to 1 to make comparable results from different uncertainty realizations.

– Outputs:• prevalence and concentration of E. Coli O157:H7 at the end of the

processing line (bags).• Burden of illness: number of cases per batch.

– Results were plotted in graphs and statistics were calculated.

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Simulation Parameters

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Thank you

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