Implementing effective environmental management strategies requires a comprehensive understanding of the chemical composition of environmental pollutants, particularly in complex mixtures. Utilizing innovative analytical techniques, such as high-resolution mass spectrometry and predictive retention index models, can provide valuable insights into the molecular structures of environmental contaminants. Liquid Chromatography-High-Resolution Mass Spectrometry is a powerful tool for the identification of isomeric structures in complex samples. However, there are some limitations that can prevent accurate isomeric structure identification, particularly in cases where the isomers have similar mass and fragmentation patterns. Liquid chromatographic retention, determined by the size, shape, and polarity of the analyte and its interactions with the stationary phase, contains valuable 3D structural information that is vastly underutilized. Therefore, a predictive retention index model is developed which is transferrable to LC-HRMS systems and can assist in the structural elucidation of unknowns. The approach is currently restricted to carbon, hydrogen, and oxygen-based molecules <500 g mol⁻¹. The methodology facilitates the acceptance of accurate structural formulas and the exclusion of erroneous hypothetical structural representations by leveraging retention time estimations, thereby providing a permissible tolerance range for a given elemental composition and experimental retention time. This approach serves as a proof of concept for the development of a Quantitative Structure-Retention Relationship model using a generic gradient LC approach. The use of a widely used reversed-phase (U)HPLC column and a relatively large set of training (101) and test compounds (14) demonstrates the feasibility and potential applicability of this approach for predicting the retention behaviour of compounds in complex mixtures. By providing a standard operating procedure, this approach can be easily replicated and applied to various analytical challenges, further supporting its potential for broader implementation.

实施有效的环境管理策略需要全面了解环境污染物的化学成分，特别是复杂混合物中的化学成分。利用高分辨率质谱和预测保留指数模型等创新分析技术，可以为环境污染物的分子结构提供有价值的见解。液相色谱-高分辨率质谱是鉴定复杂样品中异构体结构的强大工具。然而，存在一些限制，可能会妨碍准确的异构体结构识别，特别是在异构体具有相似的质量和碎片模式的情况下。液相色谱保留由分析物的大小、形状和极性及其与固定相的相互作用决定，包含宝贵的 3D 结构信息，但尚未得到充分利用。因此，开发了一个预测保留指数模型，该模型可转移到 LC-HRMS 系统，并有助于未知物的结构阐明。^{该方法目前仅限于<500 g mol -1}的碳、氢和氧基分子。该方法通过利用保留时间估计，有助于接受准确的结构式并排除错误的假设结构表示，从而为给定的元素组成和实验保留时间提供允许的公差范围。该方法可作为使用通用梯度 LC 方法开发定量结构保留关系模型的概念证明。使用广泛使用的反相 (U)HPLC 色谱柱和相对较大的一组训练化合物 (101) 和测试化合物 (14) 证明了该方法用于预测复杂混合物中化合物保留行为的可行性和潜在适用性。通过提供标准操作程序，该方法可以轻松复制并应用于各种分析挑战，进一步支持其更广泛实施的潜力。