Business Economics, Wageningen University, Hollandseweg 1, 6706 KN, Wageningen, the Netherlands.
J Dairy Sci. 2013 Jul;96(7):4125-41. doi: 10.3168/jds.2012-5898. Epub 2013 Apr 28.
Dioxins are environmental pollutants, potentially present in milk products, which have negative consequences for human health and for the firms and farms involved in the dairy chain. Dioxin monitoring in feed and food has been implemented to detect their presence and estimate their levels in food chains. However, the costs and effectiveness of such programs have not been evaluated. In this study, the costs and effectiveness of bulk milk dioxin monitoring in milk trucks were estimated to optimize the sampling and pooling monitoring strategies aimed at detecting at least 1 contaminated dairy farm out of 20,000 at a target dioxin concentration level. Incidents of different proportions, in terms of the number of contaminated farms, and concentrations were simulated. A combined testing strategy, consisting of screening and confirmatory methods, was assumed as well as testing of pooled samples. Two optimization models were built using linear programming. The first model aimed to minimize monitoring costs subject to a minimum required effectiveness of finding an incident, whereas the second model aimed to maximize the effectiveness for a given monitoring budget. Our results show that a high level of effectiveness is possible, but at high costs. Given specific assumptions, monitoring with 95% effectiveness to detect an incident of 1 contaminated farm at a dioxin concentration of 2 pg of toxic equivalents/g of fat [European Commission's (EC) action level] costs €2.6 million per month. At the same level of effectiveness, a 73% cost reduction is possible when aiming to detect an incident where 2 farms are contaminated at a dioxin concentration of 3 pg of toxic equivalents/g of fat (EC maximum level). With a fixed budget of €40,000 per month, the probability of detecting an incident with a single contaminated farm at a dioxin concentration equal to the EC action level is 4.4%. This probability almost doubled (8.0%) when aiming to detect the same incident but with a dioxin concentration equal to the EC maximum level. This study shows that the effectiveness of finding an incident depends not only on the ratio at which, for testing, collected truck samples are mixed into a pooled sample (aiming at detecting certain concentration), but also the number of collected truck samples. In conclusion, the optimal cost-effective monitoring depends on the number of contaminated farms and the concentration aimed at detection. The models and study results offer quantitative support to risk managers of food industries and food safety authorities.
二恶英是环境污染物，可能存在于奶制品中，对人类健康以及奶制品供应链中的企业和农场造成负面影响。为了检测其存在并估计食物链中的水平，已经对饲料和食品中的二恶英进行了监测。然而，这些计划的成本和效果尚未得到评估。在这项研究中，估算了奶罐车中散装牛奶中二恶英监测的成本和效果，以优化旨在检测 20,000 个奶牛场中至少 1 个受污染奶牛场的抽样和混合监测策略，目标二恶英浓度水平。模拟了不同比例的污染农场数量和浓度的事件。假设采用了一种组合测试策略，包括筛选和确认方法，以及混合样本的测试。使用线性规划建立了两个优化模型。第一个模型旨在在最小化监测成本的前提下，实现检测事件的最小要求效果，而第二个模型旨在在给定的监测预算内最大化效果。我们的研究结果表明，虽然可以实现高水平的效果，但成本也很高。在特定假设下，每月花费 260 万欧元进行监测，可达到 95%的效果来检测到浓度为 2 皮克毒性当量/克脂肪的单个受污染农场的事件[欧盟委员会（EC）行动水平]。在相同的效果水平下，当目标是检测到浓度为 3 皮克毒性当量/克脂肪的 2 个农场污染的事件时，可降低 73%的成本。在每月固定预算为 40,000 欧元的情况下，检测到浓度等于 EC 行动水平的单个污染农场事件的概率为 4.4%。当目标是检测到浓度等于 EC 最大水平的相同事件时，该概率几乎翻了一番（8.0%）。本研究表明，检测事件的效果不仅取决于混合到混合样本中的采集卡车样本的比例（旨在检测特定浓度），还取决于采集卡车样本的数量。总之，最具成本效益的监测取决于受污染农场的数量和目标检测浓度。该模型和研究结果为食品行业的风险管理者和食品安全当局提供了定量支持。
