Optimization of extraction conditions for the maximum recovery of L‐theanine from tea leaves: Comparison of black, green, and white tea
Bibliographic record
Abstract
Abstract Background The caffeine content of tea ( Camellia sinensis (L.) Kuntze) can counteract the anti‐anxiety effects of L‐theanine. This study aims to find out the extraction method of L‐theanine and caffeine from tea leaves to obtain the highest L‐theanine and the lowest caffeine content. For this purpose, L‐theanine and caffeine contents from three tea types (white, black and green) were extracted under different time and temperature conditions and their levels were determined in a single high‐performance liquid chromatography (HPLC) analysis. Also, this study is the first to attempt to optimize the tea extraction conditions by maximizing the ratio of L‐theanine to caffeine concentration. Results The results show that white tea extracted for 5 min at high temperatures (90–100°C) had the highest L‐theanine level (21.52 mg/mL). Whereas, white tea, extracted for 5 min at low temperatures (10–11°C), had negligible caffeine (0.006 mg/mL). The caffeine content was relatively high in the extracts prepared from all types of tea under high temperatures (90–100°C). Whereas, caffeine level was low in tea extracted at low temperatures. The L‐theanine‐to‐caffeine ratio was largest for white tea extracted at 10–11°C for 5 min (L‐theanine/caffeine ratio > 200), and this ratio was lowest (0.96) for black tea extracted at 90–100°C for 30 min. Conclusion According to these data, the temperature and time of extraction have significant effects on the amount of L‐theanine and caffeine extracted from Camellia sinensis (tea). In addition, white tea drinks prepared for 5 min at 10–11°C, could be recommended to people intolerant of caffeine side effects.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".