Application of Metabolomics Techniques using LC/MS and GC/MS Profiling Analysis of Green Tea Leaves

Significance of the Topic

The comprehensive analysis of small-molecule metabolites is central to metabolomics and finds applications in food quality assessment, drug development, and biomarker discovery. Combining liquid chromatography–mass spectrometry (LC/MS) and gas chromatography–mass spectrometry (GC/MS) enables coverage of a broad range of metabolites, delivering high sensitivity, resolution, and throughput in profiling complex matrices such as green tea leaves.

Objectives and Study Overview

This study aimed to profile metabolites in nine high-grade green tea leaf samples ranked in a quality contest and to construct a predictive model for tea quality. Complementary LC/MS and GC/MS analyses were performed to identify compounds correlating with ranking, and multivariate statistics were applied to highlight key quality markers.

Methodology and Instrumentation Used

• Sample preparation: Leaf samples (10–30 mg) were extracted with water/methanol/chloroform, followed by derivatization for GC/MS (methoxyamination and silylation) and acidified methanol extraction for LC/MS.
• LC/MS analysis: Prominence UFLC coupled to LCMS-IT-TOF, reversed-phase column (Shim-pack XR-ODS), gradient elution (0.1 % formic acid/methanol), ESI ± switching, m/z 100–1000, MSn for unknown identification.
• GC/MS analysis: GCMS-QP2010 Plus with Rtx-5MS column, split injection, EI mode, m/z 50–1000, spectral matching against Shimadzu metabolite database and NIST library.
• Data processing: Peak detection and alignment using LCMSsolution and GCMSsolution; multivariate analysis by SIMCA-P (PCA) to link metabolite patterns to tea ranking.

Main Results and Discussion

LC/MS resolved ten major catechins and methylxanthines in a 10-minute gradient with high retention time and peak area reproducibility (<0.2 % RSD). GC/MS identified 71 saccharides, amino acids, organic acids, and polyols. PCA demonstrated that high-rank teas were enriched in catechins (eg, epigallocatechin gallate, theogallin) and specific organic acids, while low-rank teas showed distinct profiles of other metabolites. MS3 data allowed prediction and database matching of an unknown marker (Peak X) as theogallin (C14H16O10) based on its accurate mass and fragmentation pattern.

Benefits and Practical Applications

Integrating GC/MS and LC/MS delivers a rapid, robust platform for comprehensive metabolite profiling. The approach supports food quality control by identifying chemical markers that discriminate product grades. High throughput and reproducible analysis facilitate routine screening in industrial QC, authentication, and research laboratories.

Future Trends and Possibilities

• Integration of metabolomics with other omics (proteomics, genomics) for systems-level insights.
• Expansion of spectral and retention index libraries to improve unknown identification.
• High-resolution MS and machine learning for automated marker discovery.
• Development of portable platforms for on-site quality assessment.

Conclusion

The combined LC/MS and GC/MS metabolomics workflow effectively profiled green tea metabolites, enabling quality prediction based on key chemical markers. Accurate mass fragmentation and multivariate analysis revealed catechins and organic acids as primary contributors to tea ranking, demonstrating the method’s value for food quality control applications.

References

• Fukusaki E. Possibilities and Technological Obstacles in Metabolomics. Biotechnology 2006, 84, 231–234.
• Pongsuwan W., Fukusaki E., Bamba T., Yonetani T., Yamahara T., Kobayashi A. Prediction of Japanese Green Tea Ranking by GC/MS-Based Hydrophilic Metabolite Fingerprinting. J. Agric. Food Chem. 2007, 55, 231–236.