InnovEOX researcher Ardiana Kajtazi gave an oral presentation at the International Chemical Congress of Pacific Basin societies 2021, Honolulu, Hawaii

19^th December 2021, Ardiana Kajtazi (ESR12) gave the oral presentation „In Silico Chromatographic Retention Time Prediction for Enhanced Identification of Unknown Micropollutants in Wastewater“ at the International Chemical Congress of Pacific Basin societies 2021, Honolulu, Hawaii.

Abstract:

Micropollutants, such as pharmaceuticals, pesticides, industrial chemicals, steroid hormones, etc. are defined as anthropogenic chemicals and can be found in water. It is seen as a serious threat, not just to aquatic life but also to humans, which requires the availability of tools allowing structural elucidation and ideally, fast identification of unknowns [1]. Over the past decade, high-performance liquid chromatography, coupled with high-resolution mass spectrometry (HPLC-HRMS), has been increasingly used in the analysis of environmental unknown or treated wastewater samples. However, HRMS prediction software cannot always reliably predict the elemental composition of (larger) molecules while structural information obtained by MS remains limited. This hinders the identification and structural characterization of unknowns in water [2].

In this research, a Quantitative Structure- Retention Relationship (QSRR) approach is used to build predictive retention time (t_R) models to assist in the identification of unknowns with an emphasis on small molecules comprising only carbon, oxygen, and hydrogen atoms. Development of algorithms based on LC retention time allows confirmation or invalidation of the 1) suggested elemental compositions by HRMS data treatment software and/or 2) the ensuing hypothesized structural formulas.

The predictive ability of these models is enhanced through parallelization of the work into two HPLC modes. Therefore, a novel approach of the highly orthogonal normal phase (NP) and reversed-phase (RP) LC modes are combined for an enhanced cumulated predictive strength. A comprehensive off- and online NPLCxRPLC-HRMS platform is also proposed allowing obtaining the combined chromatographic and HRMS data in a single analysis.

 

References:

[1] G. M. Randazzo et al., “Prediction of retention time in reversed-phase liquid chromatography as a tool for steroid identification,” Anal. Chim. Acta, 2016, doi: 10.1016/j.aca.2016.02.014.

[2] P. R. Haddad, M. Taraji, and R. Szücs, “Prediction of Analyte Retention Time in Liquid Chromatography,” Analytical Chemistry. 2021, doi: 10.1021/acs.analchem.0c04190.