Ionisation efficiencies can be predicted in complicated biological matrices
| Autor: | Jaanus Liigand, Rob J. Vreeken, Filip Cuyckens, Anneli Kruve, Piia Liigand |
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| Přispěvatelé: | Imaging Mass Spectrometry (IMS), RS: M4I - Imaging Mass Spectrometry (IMS) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Spectrometry
Mass Electrospray Ionization Analyte Electrospray ELECTROSPRAY-IONIZATION Abundance (chemistry) POSTCOLUMN INFUSION Electrospray ionization Analytical chemistry 010402 general chemistry METABOLOMICS 01 natural sciences Biochemistry Analytical Chemistry Matrix (chemical analysis) Dogs Metabolomics Ionization Metabolites ION INTENSITY Animals Environmental Chemistry MODE ORGANIC-COMPOUNDS Spectroscopy Matrix effect CHROMATOGRAPHY-MASS SPECTROMETRY Chemistry Ionisation efficiency 010401 analytical chemistry Proteins ESI-MS QUANTIFICATION ANALYTES 0104 chemical sciences Flow Injection Analysis Non-target Order of magnitude |
| Zdroj: | Analytica Chimica Acta, 1032, 68-74. Elsevier Science |
| ISSN: | 1873-4324 0003-2670 |
| Popis: | The importance of metabolites is assessed based on their abundance. Most of the metabolites are at present identified based on ESI/MS measurements and the relative abundance is assessed from the relative peak areas of these metabolites. Unfortunately, relative intensities can be highly misleading as different compounds ionise with vastly different efficiency in the ESI source and matrix components may cause severe ionisation suppression. In order to reduce this inaccuracy, we propose predicting the ionisation efficiencies of the analytes in seven biological matrices (neat solvent, blood, plasma, urine, cerebrospinal fluid, brain and liver tissue homogenates). We demonstrate, that this approach may lead to an order of magnitude increase in accuracy even in complicated matrices. For the analyses of 10 compounds, mostly drugs, in negative electrospray ionisation mode we reduce the predicted abundance mismatch compared to the actual abundance on average from 660 to 8 times. The ionisation efficiencies were predicted based on i) the charge delocalisation parameterWAPS and ii) the degree of ionisation a, and the prediction model was subsequently validated based on the cross-validation method 'leave-one-out'. (C) 2018 Elsevier B.V. All rights reserved. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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