Chemometric Methods for the Analysis of Graftage-Related Black Tea Aroma Variation by Solid Phase Mirco-Extraction and Gas Chromatography-Mass Spectrometry

Importance of the Topic

Tea aroma is a key indicator of quality and is determined by complex volatile profiles. Grafting is widely used to modify cultivar performance but may alter secondary metabolite production in the scion, leading to changes in aroma. Understanding these changes helps guide rootstock selection and improve tea quality evaluation.

Objectives and Study Overview

This study aimed to compare volatile profiles of black tea from nongrafted and various grafted YingHong No.9 plants. Untargeted analysis by solid-phase microextraction coupled with triple quadrupole GC/MS and chemometric methods was applied to reveal graft-induced aroma variations.

Methodology and Instrumentation

Sample preparation involved five groups: nongrafted YingHong No.9 (CK) and YingHong No.9 grafted onto BaiMao No.2 (BM), HeiYe ShuiXian (HY), HuangZhiXiang DanCong (HZX), and WuLingHong (WLH), each with six replicates. Volatiles were extracted from 3.5 g tea infused with boiling water and spiked with ethyl decanoate, using a DVB/CAR/PDMS SPME fiber at 60 °C for 40 min. Analysis was performed on an Agilent 7890B GC with a DB-5MS column and an Agilent 7000D triple quadrupole MS (EI 70 eV, scan m/z 35–500). Data processing used Agilent MassHunter Profinder for molecular feature extraction and Profiler Professional for alignment, filtering by frequency, reproducibility, and ANOVA (p<0.05, fold change ≥1.5).

Main Findings and Discussion

A total of 34 characteristic compounds, including aldehydes, alcohols, ketones, and esters, were tentatively identified; eight were confirmed with standards. PCA explained over 70 % of sample variance in the first two components, clearly separating nongrafted and grafted groups. HCA clustered samples according to graft type, with BM closely resembling CK and HZX showing distinct volatile profiles.

Benefits and Practical Applications

This workflow enables rapid, untargeted profiling of complex aroma mixtures, facilitating the evaluation of grafting effects on tea quality. The approach can support rootstock selection, breed screening, and routine quality control in tea production.

Future Trends and Applications

Future studies may integrate high-resolution MS and machine learning for deeper volatile characterization, link chemical profiles with sensory data, and extend the method to other tea varieties or processing methods. Targeted quantification of marker compounds can further refine quality assessment.

Conclusion

SPME-GC/MS/MS combined with chemometric analysis effectively differentiated black tea samples based on graft-induced aroma variations. Automated data processing using Agilent software streamlined feature extraction and statistical analysis, providing actionable insights for tea propagation and quality control.

References

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