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

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

doi:10.1016/j.chemosphere.2023.139361

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

Chemosphere. 2023 Oct:337:139361. doi: 10.1016/j.chemosphere.2023.139361. Epub 2023 Jun 29.

Authors

Ardiana Kajtazi¹, Giacomo Russo², Kristina Wicht¹, Hamed Eghbali³, Frédéric Lynen⁴

Affiliations

¹ Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium.
² School of Applied Sciences, Sighthill Campus, Edinburgh Napier University, 9 Sighthill Ct, EH11 4BN, Edinburgh, United Kingdom.
³ Packaging and Specialty Plastics R&D, Dow Benelux B.V., Terneuzen, 4530 AA, the Netherlands.
⁴ Separation Science Group, Department of Organic and Macromolecular Chemistry, Ghent University, Krijgslaan 281 S4bis, B-9000 Ghent, Belgium. Electronic address: frederic.lynen@ugent.be.

PMID: 37392796
DOI: 10.1016/j.chemosphere.2023.139361

Abstract

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.

Keywords: HPLC-HRMS; In silico prediction; Quantitative structure- retention relationship model; Retention index; Structural elucidation.

MeSH terms

Chromatography, High Pressure Liquid / methods
Chromatography, Liquid / methods
Complex Mixtures*
Isomerism
Mass Spectrometry / methods

Substances

Complex Mixtures