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

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 B 1 /M 1 (AFB 1 /M 1) 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 AFB 1 /M 1 showed to be more effective than for dioxins in most of the considered scenarios, because AFB 1 /M 1 could result into more DALYs than dioxins when both chemicals are in same contamination fraction, and costs for analyzing one AFB 1 /M 1 sample are lower than for one dioxins sample. The results suggest that relatively more resources be spent on monitoring AFB 1 /M 1 when both chemicals' contamination fractions are low; when both contamination fractions are higher, relatively more budget should be addressed to monitoring dioxins.

Designing optimal food safety monitoring schemes using Bayesian network and integer programming: The case of monitoring dioxins and DL‐PCBs

“…Optimization of food safety monitoring from cost‐effectiveness perspective is a relevant topic of food safety economics. These problems aim to minimize monitoring costs or maximize effectiveness given some constraints and can be formulated by mathematical programming (Focker et al., 2019b; Lascano‐Alcoser et al., 2013; Lascano‐Alcoser et al., 2014; Wang et al., 2020). In this study, we use integer programming (IP) to minimize food safety monitoring costs by given certain constraints, and generally IP problem could be expressed as follows: $$$$\begin{equation}{\rm{Objective\; function\; Min}}:\,f\left( {{x}_1 \ldots \ldots {x}_n} \right),\end{equation}$$$$ $$$$\begin{equation}{\rm{Constraints }}{\bm{G}}\left( {{x}_{1} \ldots \ldots {x}_n} \right) {\le} {\bm{b}},\end{equation}$$$$ $$$$\begin{equation}{x}_{{\rm{min}}} \le {x}_1 \ldots \ldots {x}_n \le {x}_{{\rm{max}}},\end{equation}$$$$ $$$$\begin{equation}{x}_1 \ldots \ldots {x}_n \in {Z}^n,\end{equation}$$$$…”

“…Optimization of food safety monitoring from costeffectiveness perspective is a relevant topic of food safety economics. These problems aim to minimize monitoring costs or maximize effectiveness given some constraints and can be formulated by mathematical programming (Focker et al, 2019b;Lascano-Alcoser et al, 2013;Lascano-Alcoser et al, 2014;Wang et al, 2020). In this study, we use integer programming (IP) to minimize food safety monitoring costs by given certain constraints, and generally IP problem could be expressed as follows:…”

“…Lascano-Alcoser et al (2013) and Lascano-Alcoser et al (2014) optimized monitoring schemes in milk and pork supply chains, focusing on maximizing the probability of identifying dioxins contamination given preset financial resources for sampling and analysis. Recently, some new models have been developed to optimize food safety monitoring from cost-effectiveness perspective: Focker et al (2019b) optimized the costs and accuracy of aflatoxin monitoring in the maize supply chain by using nonlinear programming, and Wang et al (2020) optimized the monitoring of multiple chemical hazards along the dairy supply chain to reduce potential public health impacts. Wang et al (2021) optimized spatial sampling strategies of dioxins monitoring along the Dutch dairy supply chain by combing stochastic simulation and linear programming.…”

“…(2019b) optimized the costs and accuracy of aflatoxin monitoring in the maize supply chain by using nonlinear programming, and Wang et al. (2020) optimized the monitoring of multiple chemical hazards along the dairy supply chain to reduce potential public health impacts. Wang et al.…”

Public health risks arising from food supply chains: Challenges and opportunities

“…There are many open research problems in this area, including studying how tests should be allocated at various supply chain points and how to incentivize safety through the formulation of optimization frameworks. One of the first examples of this work is in Wang et al (2020), where testing for aflatoxins and dioxins was evaluated across the Dutch dairy supply chain.…”