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

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 approac...

Full description

Saved in:
Bibliographic Details
Published in:Risk analysis Vol. 40; no. 12; pp. 2539 - 2560
Main Authors: Wang, Z., Fels‐Klerx, H. J., Lansink, A. G. J. M. Oude
Format: Journal Article
Language:English
Published: United States Blackwell Publishing Ltd 01.12.2020
John Wiley and Sons Inc
Subjects:
Aflatoxin
Aflatoxin B1
Aflatoxins
Budgets
Chemicals
Contamination
Cost analysis
Dairy farming
Dairy farms
Dairy products
Decision analysis
Dioxins
Disability adjusted life years
Disease burden
economics
Farms
Food
Food chains
Food contamination
Food contamination & poisoning
Food safety
Food supply
Milk
Mixed integer
Monitoring
Nonlinear programming
Optimization
Original
Original s
Plants
Risk assessment
Risk reduction
Sampling
Supply
Supply chains
Trucks
food safety
economics
optimization
Disease burden
sampling
ISSN:0272-4332, 1539-6924, 1539-6924
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:0272-4332
1539-6924
1539-6924
DOI:10.1111/risa.13605