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Facilitating structural elucidation of small environmental solutes in RPLC-HRMS by retention index prediction

Kajtazi, Ardiana; Russo, Giacomo; Wicht, Kristina; Eghbali, Hamed; Lynen, Frédéric


Ardiana Kajtazi

Kristina Wicht

Hamed Eghbali

Frédéric Lynen


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.

Journal Article Type Article
Acceptance Date Jun 26, 2023
Online Publication Date Jun 29, 2023
Publication Date 2023-10
Deposit Date Jul 4, 2023
Publicly Available Date Jun 30, 2024
Print ISSN 0045-6535
Electronic ISSN 1879-1298
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 337
Article Number 139361
Keywords HPLC-HRMS, Quantitative structure- retention relationship model, In silico prediction, Retention index, Structural elucidation