Processing technology as aroma architect: OAV fingerprints decode differentiation and compatibility of key odorants in Fuding Dabai tea via GC×GC-TOF-MS and sensomics
Abstract
This study systematically investigates the regulatory role of processing technology in the aroma differentiation of Fuding Dabai tea ( Camellia sinensis ). Using an integrated sensomics approach combining quantitative descriptive analysis and GC × GC–TOF–MS, we deciphered aroma formation in green (GT), white (WT), black (BT), and dark (DT) teas. Among 187 volatiles detected, WT exhibited the highest VOC content (2349.42 μg/L) and the most key odorants (30 of 36). Multivariate statistical modeling identified fermentation degree as the primary factor driving aroma divergence, clearly discriminating fermented (BT/DT) from non/light-fermented (GT/WT) teas. OPLS-DA selected 12 marker compounds (VIP > 1), predominantly alcohols and aldehydes. Sweet aroma exhibited strong correlations with benzeneacetaldehyde ( r = 0.91) and ( E )-2-hexenal ( r = 0.92), while aged aroma correlated strongly with ( E,E )-2,4-heptadienal ( r = 0.92) (all p < 0.001). We demonstrate that processing reconfigures aroma profiles through enzymatic inhibition, oxidative conversion, and microbial fermentation pathways. These results provide a biochemical basis for aroma-oriented optimization in tea processing and establish the superior suitability of Fuding Dabai for white tea production. • Processing techniques reshape tea aroma through enzyme inhibition-oxidation-fermentation biochemical axis. • Comprehensive chromatography identified 187 volatile compounds dominated by alcohols and ketones. • Thirty-five key odorants with odor activity values ≥1 show distinct process-specific distributions. • Fermentation extent explained 53.3% variance as dominant factor in aroma differentiation.
Fields of Study & Subjects
Tea Polyphenols and Effects
Fermentation and Sensory Analysis
Sirtuins and Resveratrol in Medicine
Publication details
- Journal / source
- Food Chemistry X
- Publisher
- Elsevier BV
- Volume
- 34
- Pages
- 103530–103530
- Publication date
- 13 January 2026
- Language
- EN