Flavonoid profiling and annotation using a product ion-dependent MSn data acquisition method on a Tribrid Orbitrap mass spectrometer

Significance of the Topic

Flavonoids are plant secondary metabolites with broad biological activities, including antioxidant, anti-inflammatory and immunomodulatory effects. Profiling these compounds in food, beverage and biological samples is essential for understanding their health benefits and guiding quality control in research and industry. However, the structural diversity of flavonoids and the lack of authentic standards limit confident identification using conventional MS/MS workflows.

Objectives and Study Overview

This study establishes a product ion-dependent LC-MSn workflow on a Tribrid Orbitrap platform to enhance structural information for flavonoid annotation. By triggering higher-order (MS3–MS5) scans upon detection of characteristic sugar neutral losses, the method aims to double the number of confidently annotated flavonoids in fruit and vegetable juices compared to traditional MS/MS-only approaches.

Methodology and Instrumentation

The workflow comprises four key stages:

• Sample Preparation
  • Three commercial juice products (Kale Blazer, Berries Gomega, Red Rhapsody) were filtered and diluted 1:2 with methanol and analyzed in duplicate.
• Chromatographic Conditions
  • UHPLC on a Hypersil Gold column (2.1×150 mm, 1.9 µm) at 45 °C, flow rate 200 µL/min.
  • Gradient: 0 min (99.5 % A), 1 min (90 % A), 10 min (70 % A), 18 min (50 % A), 22 min (1 % A), total 30 min. Mobile phases: water +0.1 % FA (A) and methanol +0.1 % FA (B).
• Mass Spectrometry Conditions
  • Thermo Scientific Orbitrap ID-X Tribrid MS in positive ESI (m/z 150–1200).
  • Full MS at 60 K resolution, cycle time 1.2 s.
  • Data-dependent HCD MS/MS (150–450 m/z) and product ion-triggered CID MSn (up to MS5) for m/z 450–1200 upon neutral sugar losses.
  • MSn resolution set at 15 K; isolation widths 1.6 Da (MS2) and 2.0 Da (higher orders).
• Data Processing
  • Mass Frontier 8.0 for spectral tree handling.
  • Compound Discoverer 3.0 for library searching against mzCloud, Arita flavonoid database and custom lists, including a fragment ion search (FISh) scoring scheme.

Main Results and Discussion

The MSn workflow generated complete fragmentation trees (up to MS4) for juice samples, revealing twice as many annotated flavonoids versus MS/MS only. Structural sub-class annotation against mzCloud reference spectra enabled rapid classification of unknowns (e.g., m/z 581.1861). A dedicated instrument method template simplified setup for non-experts, while automated FISh scoring prioritized candidates based on fragment match counts. The approach addressed two main bottlenecks—complex MSn method setup and labor-intensive data interpretation—by providing an easy-to-use acquisition template and an informative data processing pipeline.

Benefits and Practical Applications

• Increased coverage and confidence in flavonoid profiling from natural products.
• Rapid class-based annotation without needing extensive expert knowledge.
• Applicability to diverse matrices including foods, beverages and biological samples.
• A turnkey solution for QA/QC, nutraceutical research and metabolomics studies.

Future Trends and Opportunities

• Expansion of spectral and structure databases to cover emerging flavonoid derivatives.
• Integration with machine learning for predictive fragmentation and annotation.
• Real-time MSn decision trees for in-line quality control in industrial processes.
• Extension of the workflow to other natural product classes such as alkaloids and terpenoids.

Conclusion

This product ion-dependent MSn workflow on the Orbitrap ID-X platform delivers rapid, high-confidence flavonoid annotation with two-fold improved coverage over MS/MS alone. The combination of an instrument method template and a class-informative data processing pipeline democratizes deep MSn analysis and supports broad applications in analytical chemistry and bioactive compound research.

References

• Aritam M., Suwa K. Search extension transforms Wiki into a relational system: A case for flavonoid metabolite database. BioData Mining, 2008, 1, 7.