Semi-Quantitative LC/ESI/MS Analysis Using Predictive Models of ESI Ionization Efficiencies
Jaanus Liigand (1), Piia Liigand (1), Mari Ojakivi (1), Karl Kaupmees (1), Anneli Kruve (1)(2) (1) University of Tartu, Estonia; (2) Freie Universität Berlin, Germany
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Jaanus Liigand (Presenter) University of Tartu
Presenter Bio: I am third-year analytical chemistry PhD student from University of Tartu, studying standard substance free quantification in LC/ESI/MS analysis
Relevant Financial Disclosures
(within past 24 months, reported on Apr 02, 2021)
No relevant financial relationship(s) to disclose.
Abstract
Until now, in the discovery of metabolites and in the absence of standard substances in LC/MS analyses equal ionization efficiencies are assumed. This may lead to misunderstandings of the processes occurring in organisms as concentrations of some metabolites can be up to 5 orders of magnitude over- or underestimated. By prediction of ionization efficiencies in both positive and negative electrospray ionization and in biological matrices the accuracy of such predictions can be improved, the best prediction being a 4-times mismatch with reality allowing for more accurate semi-quantitative analysis. This prediction method is user-friendly, as it uses 2D structures of the analytes and a small set of calibration compounds incorporated in the analytical run; thus, enabling quicker and more accurate estimation of the abundance of compounds of interest.
= Discovery stage.
= Translation stage.
= Clinically available.
