GC-MS Combined with Chemometric Method for Analysis of Rapid Aged White Tea Compared with Natural Aged and Fresh White Tea

Significance of the Topic

Volatile aroma compounds are pivotal in defining the sensory quality of white tea. As a minimally processed tea, white tea acquires distinctive flavor profiles through aging, resembling transformations observed in red wine and Pu-erh tea. Rapid and natural aging methods can yield diverse aromatic signatures, underlining the importance of analytical techniques to characterize and control tea quality.

Objectives and Study Overview

This study aimed to compare volatile profiles of four white tea treatments: fresh white tea (FWT), control‐aged white tea (CKWT), rapid aged white tea (RAWT), and naturally aged white tea (NAWT). By integrating gas chromatography–mass spectrometry (GC-MS) with chemometric analysis, the work sought to identify key aroma compounds that differentiate rapid aging from conventional and long-term aging processes.

Methodology and Instrumentation

Sample Preparation and Extraction:

• 3.5 g white tea infused with 10 mL boiling water and spiked with ethyl decanoate internal standard.
• SPME extraction performed at 60 °C for 40 min using a DVB/CAR/PDMS fiber, followed by desorption at 270 °C for 4.5 min.

Chromatography and Detection:

• GC system: Agilent 7890B with DB-5MS column (60 m × 0.32 mm × 0.25 μm).
• Oven program: 50 °C (3 min), ramp 5 °C/min to 250 °C (hold 5 min).
• Carrier gas: Helium at 1.0 mL/min; manual SPME injection.
• MS system: Agilent 7000D triple quadrupole in full‐scan mode (35–500 m/z), EI ionization at 70 eV.

Data Processing:

• MassHunter Qualitative software for peak deconvolution and export to .cef files.
• Mass Profiler Professional (MPP) for alignment, filtering (frequency >60%, CV <25%), univariate analysis (ANOVA p<0.05, fold change ≥2), PCA, hierarchical clustering, and Venn diagrams.

Main Results and Discussion

Identification:
The workflow filtered 164 differential entities and ultimately identified 40 key aroma compounds (alcohols, aldehydes, ketones, esters, heterocycles, alkanes) via NIST 14 library matching and retention index comparisons.

Chemometric Analysis:

• PCA: First three principal components explained 97.96% of variance. RAWT samples separated distinctly from CKWT on PC2 and PC3, while FWT, CKWT, and NAWT diverged along PC1.
• Hierarchical Clustering: FWT and CKWT clustered together; RAWT formed its own subgroup, indicating rapid aging alters aroma composition significantly.
• Venn Diagram: Four compounds unique to CKWT, five unique to RAWT, and additional unique sets for NAWT and FWT, demonstrating treatment-specific markers.

Benefits and Practical Applications

GC-MS coupled with chemometric tools offers an efficient, reproducible approach to:

• Discriminate white teas by aging method.
• Monitor quality and consistency in rapid aging technologies.
• Identify marker compounds for product authentication.

Future Trends and Opportunities

• Refinement of rapid aging protocols to more closely mimic long-term aging chemistry.
• Integration with sensory evaluation and other omics techniques for holistic quality assessment.
• Application of machine learning algorithms for predictive modeling of aroma evolution.
• Extension of this platform to other tea categories and fermented beverages.

Conclusion

The combination of SPME-GC-MS and advanced chemometric analysis effectively profiles and differentiates white tea aging treatments. Rapid aging emerges as a viable method to achieve aroma characteristics comparable to natural aging within a significantly reduced timeframe.

Reference

• Dai W., Xie D., Lu M., Li P., Lv H., Yang C., Peng Q., Zhu Y., Guo L., Zhang Y., Tan J., Lin Z. Profiling volatile compounds in aged white tea using SPME-GC-MS. Food Research International, 2017, 96: 40–45.
• Lorenzo C., Garde-Cerdán T., Pedroza M.A., Alonso G.L., Salinas M.R. Evolution of aroma compounds during wine aging. Food Research International, 2009, 42(9): 1281–1286.