Modeling cost-effective monitoring schemes for food safety contaminants: Case study for dioxins in the dairy supply chain

Food safety monitoring is essential for hazard identification in food chain, but its application may be limited due to costly analytical methods and (inefficient) sampling procedures. The objective of this study was to design costeffective monitoring schemes for food safety contaminants along the food production chain, given restricted monitoring budgets. As a case study, we focused on dioxins in the dairy supply chain with feed mills, dairy farms, dairy trucks and storage silos in dairy plants as possible control points. The cost-effectiveness of monitoring schemes was assessed using a model consisting of a simulation module and an optimization module. In the simulation module, the probability to collect at least one contaminated sample was computed for different sampling strategies (simple random sampling, stratified random sampling and systematic sampling) at each control point. The optimization module maximized the effectiveness of a monitoring scheme to identify the contaminated sample by determining the optimal sampling strategies, the optimal number of incremental samples collected, and the pooling rate (number of collected samples mixed into one aggregated sample) at each control point. The modelling approach was applied to two cases with different types of contamination. Results of these cases showed that, to identify the same contaminated sample, monitoring schemes with systematic sampling were more cost-effective at feed mills and dairy farms. The combination of simulation and optimization methods showed to be useful for developing cost-effective food safety monitoring schemes along the food supply chain.