Floral aroma improvement via solar withering and shaking in summer green tea: Sensory and analytical insights
Bibliographic record
Abstract
Summer green tea leaves often exhibit excessive astringency, weak aroma and poor sensory quality, leading to low consumer acceptance, resource waste, and economic losses, which contradict sustainable development. Improving its quality and utilization remains a key challenge. This study investigated the effects of solar withering and shaking on summer green tea aroma. Sensory evaluations revealed that spreading-shaking (SR) and spreading-solar withering-shaking (SRS) significantly improved aroma, imparting floral notes. Utilizing electronic nose ( E -nose), headspace solid-phase microextraction-gas chromatography–mass spectrometry (HS-SPME-GC-MS) and headspace gas chromatography-ion mobility spectrometry (HS-GC-IMS), 61 volatile compounds were identified by HS-SPME-GC–MS and 80 by HS-GC-IMS. 9 key aroma compounds (such as geraniol and trans- β -ionone) with rOAV ≥1 and VIP ≥ 1 were selected. PLS-DA confirmed significant aroma differences among processing methods. The findings highlight the potential of solar withering and shaking in enhancing summer green tea aroma, providing a basis for optimized processing and sustainable tea production. • Solar withering and shaking enhance summer green tea aroma quality. • GC-IMS and GC–MS identified 80 and 61 volatiles, highlighting key floral compounds. • Shaking promotes trans-nerolidol, indole, and β-ionone accumulation. • PLSDA analysis confirms distinct aroma profiles among processing techniques. • Optimized processing improves summer tea aroma and industrial application potential.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".