Effects of time, ultrasonic treatment and pH during extraction on l-theanine and caffeine yields from white tea leaves
Bibliographic record
Abstract
L-theanine and caffeine are extracted simultaneously from tea. Extraction protocols to minimize caffeine can help prepare low-caffeine tea-based products. The ultrasound (US) application and the solution's pH are factors that could affect the L-theanine and caffeine's relative extraction. In this study, white tea was extracted under low temperatures (∼10ºC) for 5min and 20min, with and without applying US (20kHz), to determine whether the extraction of L-theanine and caffeine could be differentiated. A time-dependent increase in L-theanine and caffeine levels in both treatments was observed. The highest levels of L-theanine (0.81±0.06mg/ml) and caffeine (1.34±0.07mg/ml) were obtained by 20min US treatment. Although US-treated samples had higher amounts of these compounds, the 5min US-untreated sample was the only treatment with a higher level of L-theanine (0.21±0.03mg/ml) than caffeine (0.16±0.06mg/ml), hence, the lowest caffeine-to-L-theanine ratio (0.75±0.16). This ratio was lower in the shorter treatments regardless of US application. The second experiment investigated the impact of pH on extraction efficiency. Lowering the extraction pH from 5.17 to 2.79 by lemon juice did not significantly change the L-theanine and caffeine levels in tea samples. These data may suggest that conventional infusion of tea is preferable to US-assisted extraction (UAE) for the preparation of low-caffeine tea drinks.
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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) | 0.000 | 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".