Spreading Degree Modulates Floral Aroma Development in Green Tea: Integrated GC-E-Nose, Metabolomics, and Molecular Docking Reveals Key Odorants and Olfactory Receptor Interactions

TL;DR

This study shows how adjusting the spreading process in green tea production can enhance floral aromas by identifying key odorants and their interactions with olfactory receptors.

Contribution

The study identifies five key odorants and their interaction with OR1D2, revealing how spreading degree affects floral aroma in green tea.

Findings

Spreading degree significantly modulates green tea's aroma profile, especially promoting floral notes.

38 volatile compounds showed spreading-dependent accumulation, with five key odorants identified as contributors to floral aroma.

Molecular docking showed strong binding of these odorants to OR1D2 via hydrogen bonding and hydrophobic interactions.

Abstract

The spreading process constitutes a pivotal stage in green tea manufacturing. This study integrated GC-E-Nose with targeted metabolomics to comprehensively elucidate the dynamic changes in sensory characteristics and aroma substances of green tea across varying spreading degrees. Our findings demonstrated that spreading degree significantly modulated green tea’s aroma profile, with lighter degree particularly promoting the development of desirable floral aroma. GC-MS/MS quantification identified 70 volatile compounds, among which 38 exhibited spreading-dependent differential accumulation (VIP > 1.0, p < 0.05). Five key odorants, including indole, β-ionone, nerolidol, cis-jasmone, and β-damascenone, were highlighted as essential contributors to the floral aroma. Molecular docking simulations indicated stronger binding affinities between these five odorants and the olfactory receptor OR1D2 (<−6 kcal/mol), primarily via hydrogen bonding and hydrophobic interactions. These findings indicate that modulating the spreading degree is an effective processing strategy to enhance the development of floral aroma in green tea, offering valuable insights for precision-driven optimization of tea processing protocols.

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