Purpose: The current study develops a Monte Carlo-based simulation model to identify postharvest parameters may influence the prevalence and levels of Salmonella in floater and sinker pistachios.
Methods: Time estimates for transport from the orchard to the hulling facility (transportation delay) and for posthulling holding before drying (drying delay) were derived from industry data. The lag, log and maximum population changes of Salmonella in inoculated inhull and hulled floater and sinker pistachios were determined in laboratory studies. Estimated reductions of Salmonella during pistachio drying were also analyzed and translated to appropriate distributions to build the model. Initial contamination was assumed to be confined to 100 g in a 25,000 kg truckload with a localized (limited to 1,000 g) contamination pattern. The model was used to predict final levels of Salmonella in 100,000 truckloads of harvested pistachios.
Results: Simulation results using initial levels of 0.2 CFU/100 g and assuming localized distribution closely matched survey outcomes. Significantly more Salmonella were predicted on floaters (mean 0.62 MPN/100 g, 95% CI, 0.00017 to 100,000 MPN/100 g) than on sinkers (mean 0.16 MPN/100 g, 95% CI, 0.00013 to 10,000 MPN/100 g). Transportation delays significantly impacted the model outcomes [correlation coefficient (CC) 0.97 and 0.87 for sinkers and floaters, respectively] but drying delays were more strongly correlated with final Salmonella levels in floaters (CC 0.40) than sinkers (CC 0.11). Predicted Salmonella levels decreased significantly by reducing delays in transportation.
Significance: This model has potential as a pistachio harvest management tool to enhance the microbial safety of pistachios.