本文描述了建立定量结构属性的第一步 关系（QSPR）模型系统地选择一组代表 微污染物，将作为开发水的QSPR模型的培训集 治疗过程。采用了一种完善的优化选择策略 主成分分析（PCA）和统计实验设计相结合。在里面 在这项研究中，最初的数据集包含183种微污染物，大部分是新兴的 污染物，选自同行评审文献。每种化合物都是 以858个分子描述符为特征（即这些是QSPR中使用的变量 建模）。这导致了一个复杂的多变量数据集，主成分分析被应用于该数据集 用于以主成分的形式总结信息。第一个 四个主成分在初始数据集中占62.9%的变化 使用D-最优洋葱设计选择代表性化合物 方法使用这种设计，22种物质被选为结构上具有代表性的物质 覆盖化学域的化合物（指化学特性 在所有化合物中）以一种平衡良好的方式，并捕获了大多数 信息这里采用的系统选择方法确保未来 QSPR模型适用于各种化学品，只要它们 特征属于原始化学领域。包括15个参考文献、表格和图表。

This paper describes the first step towards building quantitative structure-property relationship (QSPR) models to systematically select a group of representative micropollutants, which will serve as a training set to develop QSPR models for water treatment processes. A well developed optimized selection strategy was applied, which combined principal component analysis (PCA) and statistical experimental design. In this research, the initial dataset contained 183 micropollutants, mostly emerging contaminants, selected from the peer-reviewed literature. Each compound was characterized by 858 molecular descriptors (i.e. these are variables used in QSPR modeling). This resulted in a large complex multivariate dataset to which PCA was applied to summarize the information in the form of principal components. The first four principal components which captured 62.9% of the variation in the initial dataset were used to select representative compounds using a D-optimal onion design approach. Using this design, 22 substances were selected as structurally representative compounds which covered the chemical domain (meaning the chemical characteristics of all compounds) in a well-balanced manner and captured the majority of the information. The systematic selection approach employed here ensures that future QSPR models are applicable to a wide range of chemicals as long as their characteristics fall within the original chemical domain. Includes 15 references, tables, figure.