Effect of Fermentation Conditions and Plucking Standards of Tea Leaves on the Chemical Components and Sensory Quality of Fermented Juice
Bibliographic record
Abstract
The effects of fermentation conditions (temperature, time, and pH) and plucking standards (one leaf and a bud to four leaves and a bud) on the chemical components and sensory quality of the fermented juices processed from crushed fresh tea leaves were investigated. The results showed that optimum fermentation conditions that resulted in fermented juices of the best sensory quality and the highest content of TFs were a temperature of 35°C, time duration of 75 min, and pH 5.1. The fermented juices processed from new shoots with three leaves and a bud or four leaves and a bud afforded high overall acceptability and TF concentration. These differences arise because tea leaves with different plucking standards have different catechin content and enzyme activities. Fermented tea juice possessed higher concentrations of chemical components such as soluble solids, amino acids, and TFs and exhibited better sensory quality as compared to black tea infusion. The TF concentrations decreased as the pH of the fermenting juice increased, and the fermented juice showed the best overall acceptability. These results provide essential information for the improvement of the processing of black tea beverage by suggesting fermentation of fresh tea leaves as a better alternative to their infusion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".