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Updated: Jun 6, 2026
14:39
Semi-Targeted Ultra-High-Performance Chromatography Coupled to Mass Spectrometry Analysis of Phenolic Metabolites in Plasma of Elderly Adults
Published on: April 22, 2022
Polyphenol identification based on systematic and robust high-resolution accurate mass spectrometry fragmentation
Justin J J van der Hooft1, Jacques Vervoort, Raoul J Bino
1Laboratory of Biochemistry, Wageningen University, Dreijenlaan 3, 6703 HA, Wageningen, The Netherlands. justin.vanderhooft@wur.nl
Analytical Chemistry
|December 15, 2010
Summary
High-mass resolution multi-stage mass spectrometry (MS(n)) enables metabolite identification by creating reproducible fragmentation trees. This method aids in distinguishing similar compounds in complex samples like plant extracts.
Area of Science:
- Analytical Chemistry
- Metabolomics
- Mass Spectrometry
Background:
- Metabolite identification is crucial for understanding biological processes.
- Distinguishing structurally similar metabolites can be challenging.
- Existing mass spectrometry methods may lack the resolution for complex samples.
Purpose of the Study:
- To evaluate high-mass resolution multi-stage mass spectrometry (MS(n)) for metabolite differentiation.
- To assess the reproducibility and stability of MS(n) fragmentation for metabolite analysis.
- To demonstrate the utility of MS(n) spectral trees for compound identification in complex matrices.
Main Methods:
- Utilized high-mass resolution multi-stage mass spectrometry (MS(n)) with an ion-trap and Orbitrap Fourier transform (FT) MS.
- Employed a chip-based nanoelectrospray ionization source for reproducible fragmentation.
- Generated MS(n) fragmentation trees up to MS(5) for 121 polyphenolic compounds.
Main Results:
- Achieved reproducible MS(n) fragmentation trees up to MS(5).
- Demonstrated stability of results over 5 months, 100-fold concentration changes, and minor energy variations.
- Observed distinct fragmentation patterns reflecting hydroxylation, methoxylation, and glycosylation site variations.
Conclusions:
- MS(n) fragmentation provides a powerful, reproducible tool for metabolite annotation and structure elucidation.
- Spectral tree data effectively distinguishes metabolites with similar elemental formulas.
- This approach significantly aids compound identification in complex biological samples, such as plant extracts.
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