ANSES, Risk Assessment Department, Maisons-Alfort, France.
Food Chem Toxicol. 2013 Sep;59:191-8. doi: 10.1016/j.fct.2013.06.006. Epub 2013 Jun 14.
The identification of the major associations of pesticides to which the population is exposed is the first step for the risk assessment of mixtures. Moreover, the interpretation of the mixtures through the individuals' diet and the characterization of potentially high-risk populations constitute a useful tool for risk management. This paper proposes a method based on Non-Negative Matrix Factorization which allows the identification of the major mixtures to which the French population is exposed and the connection between this exposure and the diet. Exposure data of the French population are provided by the Second French Total Diet Study. The NMF is implemented on consumption data to extract consumption systems which are combined with the residue levels to link dietary behavior with exposure to mixtures of pesticides. A clustering of the individuals is achieved in order to highlight clusters of individuals with similar exposure to pesticides/consumption habits. The model provides 6 main consumption systems, 6 associated mixtures of pesticides and the description of the population which is most exposed to each mixture. Two different ways to estimate the matrix providing the mixtures of pesticides to which the population is exposed are suggested. Their advantages in different contexts of risk assessment are discussed.
确定人群接触的主要农药混合物是进行混合物风险评估的第一步。此外，通过个体饮食来解释混合物，并确定潜在高风险人群，这是风险管理的有用工具。本文提出了一种基于非负矩阵分解的方法，该方法可以识别法国人群接触的主要混合物，以及这种接触与饮食之间的联系。法国人群的暴露数据由第二次法国全膳食研究提供。NMF 应用于消费数据，以提取消费系统，将其与残留水平相结合，将饮食行为与接触农药混合物联系起来。通过对个体进行聚类，突出具有相似农药暴露/饮食习惯的个体聚类。该模型提供了 6 种主要的消费系统、6 种相关的农药混合物以及每种混合物中暴露程度最高的人群描述。本文提出了两种不同的方法来估计提供人群接触的农药混合物的矩阵，并讨论了它们在不同风险评估背景下的优势。
