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Monte Carlo
Simulation of Pathogen Behavior during the Sprout Production Process.
Appl Environ Microbiol. 2005 Feb;71(2):746-53.
Montville R, Schaffner D.
Food Risk Analysis Initiative, Rutgers University, 65 Dudley Rd., New Brunswick,
NJ 08901-8520. schaffner@aesop.rutgers.edu.
Food-borne disease outbreaks linked to the consumption of raw sprouts have
become a concern over the past decade. A Monte Carlo simulation model of the
sprout production process was created to determine the most-effective points for
pathogen control. Published literature was reviewed, and relevant data were
compiled. Appropriate statistical distributions were determined and used to
create the Monte Carlo model with Analytica software. Factors modeled included
initial pathogen concentration and prevalence, seed disinfection effectiveness,
and sampling of seeds prior to sprouting, sampling of irrigation water, or
sampling of the finished product. Pathogen concentration and uniformity of seed
contamination had a large effect on the fraction of contaminated batches
predicted by the simulation. The model predicted that sprout sampling and
irrigation water sampling at the end of the sprouting process would be more
effective in pathogen detection than seed sampling prior to production. Day of
sampling and type of sample (sprout or water) taken had a minimal effect on rate
of detection. Seed disinfection reduced the proportion of contaminated batches,
but in some cases it also reduced the ability to detect the pathogen when it was
present, because cell numbers were reduced below the detection limit. Both the
amount sampled and the pathogen detection limit were shown to be important
variables in determining sampling effectiveness. This simulation can also be
used to guide further research and compare the levels of effectiveness of
different risk reduction strategies.
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