Prediction of Retention Indices in LC-HRMS for Enhanced Structural Identification of Organic Micropollutants in Water: Selectivity-Based Filtration

Addressing the global challenge of ensuring access to safe drinking water, especially in developing countries, demands cost-effective, eco-friendly, and readily available technologies. The persistence, toxicity, and bioaccumulation potential of organic pollutants arising from various human activities pose substantial hurdles. While high-performance liquid chromatography coupled with high-resolution mass spectrometry (HPLC-HRMS) is a widely utilized technique for identifying pollutants in water, the multitude of structures for a single elemental composition complicates structural identification. While current HRMS and MS/MS databases often can provide hits for known molecules, these are often erroneous or misleading when authentic standards are unavailable. In this research, a machine-learning algorithm is developed to support the structural elucidation of small organic pollutants in water, with a focus on (carbon, oxygen, and hydrogen-based) molecules weighing less than 500 Da. The approach relies on a comparison of the experimental and predicted retention of the possible structures of unknowns for which an elemental composition was obtained by HRMS. A promising novelty is thereby the improved removal of erroneous structures via the combination of the retention information obtained from two reversed-phase-based stationary phases, depicting different selectivities (octadecylsilica, C18 and pentafluorphenylsilica, F5). The study translates retention times into retention indices for instrument independence and transferability across diverse HPLC-HRMS systems. The predictive algorithm, utilizing retention data and molecular descriptors, accurately predicts retention indices and proves its utility by eliminating incorrect structural formulas through a 2-stationary phase intersection-based filtration. Using a data set of 100 training compounds and 16 external test set compounds, two Multiple Linear Regression (MLR), MLR-C18 and MLR-F5 models were developed, employing the 16 most influential descriptors, out of 5666 screened. MLR-C18 achieves precise RI predictions, R 2 = 0.97, RMSE = 36, MAE = 26, while MLR-F5, though slightly less accurate, maintains a performance with R 2 = 0.96, RMSE = 44, MAE = 34. The intersection-based filtration (within +/- 1.5 sigma) showed the elimination of more than 70% of impossible structures for a given elemental composition. The model was further implemented in the identification of a drinking water sample to prove its potential. This tool holds significant promise for supporting water quality management and sustainable practices, contributing to faster structural identification of unknown organic micropollutants in water.