Monte Carlo simulation of pathogen behavior during the sprout production process

Abstract

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.

FIG. 1.

Pathogen partitioning between sprouts and irrigation water in the sprout production process. The difference in microbial loads on sprouts and in irrigation water was calculated by subtracting log₁₀ numbers of counts per milliliter of water from log₁₀ numbers of counts per gram of sprouts. A total of 80 observations from four publications (15, 19, 47, 49) were fit to a triangular distribution with parameters (−0.75, 0.5, and 2.25).

FIG. 2.

Influence of the number of subsamples on the probability of detection of contaminated sprouts where (i) 1 out of every 10 samples of 25 g was positive at levels that varied uniformly between 1 and 5 CFU/g, (ii) batches of sprouts were made from 36.3 kg of seed, (iii) preproduction samples were composed of 160 subsamples of 25 g each, and (iv) the pathogen detection limit was 1 log₁₀ CFU/g.

FIG. 3.

Influence of detection limit on the probability of detection of contaminated sprouts where (i) 1 out of every 100 samples of 25 g was positive at a level that varied uniformly between 1 and 5 CFU/g, (ii) batches of sprouts were made from 36.3 kg of seed, and (iii) preproduction samples were composed of 160 subsamples of 25 g each.