Optimization of the Aflatoxin Monitoring Costs along the Maize Supply Chain

Abstract

An optimization model was used to gain insight into cost‐effective monitoring plans for aflatoxins along the maize supply chain. The model was based on a typical Dutch maize chain, with maize grown in the Black Sea region, and transported by ship to the Netherlands for use as an ingredient in compound feed for dairy cattle. Six different scenarios, with different aflatoxin concentrations at harvest and possible aflatoxin production during transport, were used. By minimizing the costs and using parameters such as the concentration, the variance of the sampling plan, and the monitoring and replacement costs, the model optimized the control points (CPs; e.g., after harvest, before or after transport by sea ship), the number of batches sampled at the CP, and the number of samples per batch. This optimization approach led to an end‐of‐chain aflatoxin concentration below the predetermined limit. The model showed that, when postharvest aflatoxin production was not possible, it was most cost‐effective to collect samples from all batches and replace contaminated batches directly after the harvest, since the replacement costs were the lowest at the origin of the chain. When there was aflatoxin production during storage, it was most cost‐effective to collect samples and replace contaminated batches after storage and transport to avoid the duplicate before and after monitoring and replacement costs. Further along the chain a contaminated batch is detected, the more stakeholders are involved, the more expensive the replacement costs and possible recall costs become.

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

“…Focker et al. (2019b) showed that uncertainty of analytical method increases in analyzing maize samples and the sampling plan would also have a worse performance, which is consistent with our results.…”

“…For example, a lower sensitivity of both analytical methods (from 100% to 98%) would reduce the DALYs saved from 0.0306 DALYs/100,000 population to 0.0293 DALYs/100,000 population in S1, and a decrease in the decision limits (from 0.05 µg/kg in milk and 2 pg of TEQ/g in milk fat to 0.04 µg/kg in milk and 1.6 pg of TEQ/g in milk fat separately) will improve the performance of optimal sampling to 0.0355 DALYs/100,000 population in S1. Focker et al (2019b) showed that uncertainty of analytical method increases in analyzing maize samples and the sampling plan would also have a worse performance, which is consistent with our results.…”

“…This conclusion is consistent with Trevisani et al (2014) who found that collecting milk samples in transport tankers was more economical than in upstream stages. However, Lascano-Alcoser et al (2014) and Focker et al (2019b) showed that it is cost-effective to monitor dioxins at early stages (e.g., FM) of the food chain, which may be explained by the fact they included tracing costs in the monitoring programs. In terms of using different budgets for different scenarios, the effectiveness of optimal sampling for monitoring AFB 1 /M 1 accounted for the major parts of the total effectiveness, except for monitoring S6 and S8 ( Figure 2).…”

“…Related equations are described below, see Equations ()–(), with parameters and values shown in Table IV. The sampling procedure were optimized within a preset budget of 10,000 euros (estimated from the results of Focker, Van der Fels‐Klerx, & Oude Lansink, 2019b and Lascano‐Alcoser et al., 2013) and followed by a cost‐effectiveness analysis. The effectiveness is defined as fraction of DALYs saved by optimal monitoring for each contamination scenario under different monitoring budgets (2,000–12,000 €).…”

Designing cost-effective monitoring schemes for chemical hazards in feed using machine learning

“…Several studies have explored such risk-based approaches for food safety (Lascano Alcoser et al, 2018;Focker et al 2019;Wang et al, 2021). used descriptive statistical analyses -focusing on the occurrence of aflatoxin B1 (AFB1) in different feed products -to prioritize feed products for AFB1 monitoring.…”