# Presentation: 833018

##  Escherichia coli O157:H7 in Apple Cider: A Quantitative Risk Assessment 

- Don Schaffner, PhD Siobain Duffy Food Risk Analysis InitiativeRutgers University

## What is QRA?

- A blend of published scientific literature, and expert opinion linked together by computer simulation
- An organized warehouse of data collected on a certain topic

- A summary of the influence of specific factors on the overall safety of a product
- A science-based, cost-effective way to estimate risk

## Why QRA?

- Quantitative results
- Combines data from many different labs, experiments
- Incorporates variability and uncertainty
- Customizable for individual producer’s needs
- QRA can help to identify HACCP Critical Control Points

## What can be part of a QRA?

- Pre-harvest conditions
- manure, animal contamination, drops, fruit fly transmission, cultivars
- Processing
- flume water, washing, brushing, equipment contamination, pasteurization, human and storage bin contamination
- Storage Conditions
- preservatives, temperature, freeze/thaw cycles, time to sale

## The end results of a QRA

- Conceptual framework for thinking about the problem
- Dynamic model of a particular food processing and storage system
- Sensitivity analysis, i.e. what factors are important
- Avenues of future research

## The User Interfacepull down menushidden modelresult button

## The Modules

- Birds contaminate tree-picked apples
- Animals in the orchard influence CFUs on drops
- Flume water, chlorine rinses vary the pre-pressing microbial counts
- Use of sanitizers on equipment control O157
- Pasteurization, freeze-thaw and preservatives all reduce bacterial counts

## A look under the hood, part 1

- D.W. Dingman,  *J.Food Protect.*  **62**, 567 (1999).
- L. Garland-Miller, C.W. Kaspar,  *J.Food Protect.*  **57**, 460 (1994).
- G.J. Leyer, L.-L. Wang, E.A. Johnson,  *Appl.Environ.Microbiol.*  **61**, 3752 (1995).
- A.M. Roering*, et al, *  *Int.J.Food Microbiol.*  **46**, 263 (1999).
- T. Zhao, M.P. Doyle, R.E. Besser,  *Appl.Environ.Microbiol.*  **59**, 2526 (1993).

- Refrigeration (4-8 °C) of cider contaminated with *E. coli* O157:H7
- Decreases (and occasionally increases) in O157 counts per day from all papers
- Summarized as a histogram
- Fit with a statistical distribution

## A look under the hood, part 1

- Uses Excel and Bestfit software programs
- Distribution describes the log change occurring in a single day
- Change per day is simulated over the shelf life of the cider

## A look under the hood, part 2

- Freeze-Thaw Cycles
- Uljas and Ingham   (JFP, 5/99)
- Polynomial regression (SAS) to create model
- freeze/thaw, holding temperature, time and pH on log reduction of O157:H7

**Variable****	****	****Parameter****	****P value**
- INTERCEPT	69.59985789	0.0036
- TEMP	-0.04081142	0.0003
- PH	-44.94493941	0.0007
- HOURS	-0.36421373	0.0011
- PH2 	6.77622727	0.0002
- HOURS2	0.01875504	0.0317
- R2 = 0.8914

## Simulation

- Analytica uses Monte Carlo simulation to run a user-defined number of iterations on the conditions specified
- Graphical output or statistics on CFU *E. coli* O157:H7 on day of sale in a gallon of cider
- Can be run by any person who could download the free Analytica reader and our simulation

## *Assuming birds infected with O157:H7, animal manure used, no chlorine rinse, No freeze-thaw cycle, no preservative used, no cleaning or sanitizing of equipment

## Future Research	

- Real-life studies to ascertain realistic levels of contamination
- More accurate distributions for all variables, as more data are collected
- Validation?

## Summary

- A risk assessment is only as good as the data it models
- O157: H7 in cow manure vs.
- Brushing of apples
- This risk assessment is a good start, but it’s only the first step
- Peer review
- More data, better data

## “All models are wrong... but some are useful.”

 - G. Cox

-  - G. Cox