Persistent Organic pollutants (POPs) are toxic chemicals with a shallow degradation rate and global negative impact. Their physicochemical is combined with the complex effects of long-term POPs accumulation in the environment and transport function through the food chain. That is why POPs have been linked to adverse effects on human health and animals. They circulate globally via different environmental pathways, and could be detected in regions far from their source of origin.

The primary goal of the present study is to carry out classification of various representatives of POPs using different theoretical descriptors (molecular, structural) to develop quantitative structure−properties relationship (QSPR) models for predicting important properties POPs. Multivariate statistical methods such as hierarchical cluster analysis, principal components analysis and self-organizing maps were applied to reach excellent partitioning of 149 representatives of POPs into 4 classes using ten most appropriate descriptors (out of 63) defined by variable reduction procedure.

The predictive capabilities of the defined classes could be applied as a pattern recognition for new and unidentified POPs, based only on structural properties that similar molecules may have.

The additional self-organizing maps technique made it possible to visualize the feature-space and investigate possible patterns and similarities between POPs molecules. It contributes to confirmation of the proper classification into four classes. Based on SOM results, the effect of each variable and pattern formation has been presented.

持久性有机污染物 (POPs) 是一种有毒化学品，降解速度较慢，对全球产生负面影响。它们的物理化学与环境中持久性有机污染物长期积累的复杂影响以及通过食物链的运输功能相结合。这就是为什么持久性有机污染物与对人类健康和动物的不利影响有关的原因。它们通过不同的环境途径在全球范围内传播，并且可以在远离其来源的地区被检测到。

本研究的主要目标是使用不同的理论描述符（分子、结构）对 POPs 的各种代表进行分类，以开发用于预测 POPs 重要特性的定量结构-性质关系（QSPR）模型。应用多变量统计方法，如层次聚类分析、主成分分析和自组织图，使用由变量缩减程序定义的 10 个最合适的描述符（63 个中的），将 149 个 POPs 代表完美地划分为 4 个类别。

已定义类别的预测能力可用作对新的和未识别的持久性有机污染物的模式识别，仅基于类似分子可能具有的结构特性。

额外的自组织映射技术使可视化特征空间和研究持久性有机污染物分子之间可能的模式和相似性成为可能。它有助于确认正确分类为四类。基于 SOM 结果，呈现了每个变量和模式形成的影响。