Novel structure-based profiling and annotation workflow—high-throughput analysis of flavonoids using the Thermo Scientific Orbitrap ID-X Tribrid MS

Significance of the Topic

The structural diversity of flavonoids presents a major challenge for untargeted metabolomics, as authentic standards are scarce and manual spectral interpretation is time-consuming. Flavonoids play vital roles as antioxidants and in immune regulation, and rapid, comprehensive profiling is essential for food quality control, plant metabolomics, and health research.

Objectives and Study Overview

This study introduces a structure-based MSn workflow on the Orbitrap ID-X Tribrid mass spectrometer to enhance high-throughput flavonoid profiling. By combining higher-energy and lower-energy fragmentation (HCD and CID) in multiple MSn stages and intelligent product-ion triggers, the method aims to improve unknown compound annotation and enable simultaneous quantitation.

Methodology

Three commercial juices were filtered, diluted in methanol, and analyzed by UHPLC using a Hypersil Gold column with a water/methanol gradient containing 0.1% formic acid. Data were acquired on an Orbitrap ID-X Tribrid MS operated with a 1.2 s cycle time to capture full-scan and MSn spectral trees (MS2–MS4) triggered by predefined sugar neutral losses (pentose, deoxyhexose, hexose, glucuronide). HCD MS2 covered m/z 150–420, while a CID-based product-ion-dependent MS3 and MS4 approach was applied for m/z 420–1200.

Used Instrumentation

• Thermo Scientific Vanquish UHPLC
• Thermo Scientific Orbitrap ID-X Tribrid MS
• Thermo Scientific Mass Frontier 8.0 software
• Thermo Scientific Compound Discoverer 3.0 software
• mzCloud MSn spectral library

Main Results and Discussion

• Structure-based MSn doubled flavonoid annotation compared to MS2 only, identifying 129 compounds versus 62.
• Subtree search of MSn spectral trees in Mass Frontier 8.0 enabled exact and partial matches, providing substructure annotation for unknowns.
• FISh scoring in Compound Discoverer 3.0 ranked isomeric candidates, successfully identifying compounds such as Narirutin 4′-glucoside.
• The 1.2 s cycle enabled sufficient data points for precise quantitation and statistical analysis. Hierarchical clustering and PCA clearly differentiated the three juice samples based on their flavonoid profiles.

Benefits and Practical Applications

The workflow reduces reliance on authentic standards and expert interpretation, boosts annotation throughput, and integrates quantitation with structural identification. It is applicable to other compound classes such as steroids and phospholipids.

Future Trends and Applications

Advances may include expansion of MSn spectral libraries, integration of machine learning for automated structure elucidation, and application to large-scale food, plant, and clinical metabolomics studies. The approach could be extended to novel bioactive metabolites and environmental analyses.

Conclusion

This structure-based MSn workflow on the Orbitrap ID-X streamlines flavonoid profiling by combining targeted fragmentation strategies, automated subtree and FISh scoring, and simultaneous quantitation, delivering higher coverage and throughput for complex samples.

References

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