Optimization of Sampling for Monitoring Chemicals in the Food Supply Chain Using a Risk-Based Approach: The Case of Aflatoxins and Dioxins in the Dutch Dairy Chain

Z. Wang*, Ine van der Fels-Klerx*, A.G.J.M. Oude Lansink

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Food safety monitoring faces the challenge of tackling multiple chemicals along the various stages of the food supply chain. Our study developed a methodology for optimizing sampling for monitoring multiple chemicals along the dairy supply chain. We used a mixed integer nonlinear programming approach to maximize the performance of the sampling in terms of reducing the risk of the potential disability adjusted life years (DALYs) in the population. Decision variables are the number of samples collected and analyzed at each stage of the food chain (feed mills, dairy farms, milk trucks, and dairy processing plants) for each chemical, given a predefined budget. The model was applied to the case of monitoring for aflatoxin B1/M1(AFB1/M1) and dioxins in a hypothetical Dutch dairy supply chain, and results were calculated for various contamination scenarios defined in terms of contamination fraction and concentrations. Considering various monitoring budgets for both chemicals, monitoring for AFB1/M1 showed to be more effective than for dioxins in most of the considered scenarios, because AFB1/M1 could result into more DALYs than dioxins when both chemicals are in same contamination fraction, and costs for analyzing one AFB1/M1 sample are lower than for one dioxins sample. The results suggest that relatively more resources be spent on monitoring AFB1/M1 when both chemicals’ contamination fractions are low; when both contamination fractions are higher, relatively more budget should be addressed to monitoring dioxins.

Original languageEnglish
JournalRisk Analysis
DOIs
Publication statusE-pub ahead of print - 16 Oct 2020

Keywords

  • Disease burden
  • economics
  • food safety
  • optimization
  • sampling

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