Influence of Steep Time on Polyphenol Content and Antioxidant Capacity of Black, Green, Rooibos, and Herbal Teas
Bibliographic record
Abstract
Potential health benefits of tea consumption are often attributed to the antioxidant activity of polyphenols. Whether steep time, often variable in a real-life situation, makes a biological difference in terms of polyphenol content and antioxidant activity is uncertain. The study objective was to characterize eight popular and commercially available teas for total polyphenol content (TPC) and antioxidant capacity in relation to steep time. Dragonwell (DW), Sencha (S), English Breakfast (EB), Golden Monkey (GM), Green Rooibos (GR), Red Rooibos (RR), Chamomile (C), and Peppermint (P) loose leaf teas were individually steeped in water for 1–10 min at 1 min intervals. TPC increased with longer durations of steep time; however, the majority of polyphenols observed after 10 minutes were extracted in the first 5 min regardless of tea type. After 5 min of steeping, differences (p < 0.05) in TPC were observed across teas (JS~EB~P > DW > GM~GR~RR > C). Different teas also varied in their ability to inhibit the free radical 2, 2-diphenyl-1-picrylhydrazyl (DPPH) when normalized for polyphenol concentration (1 µg/mL) and there was no effect due to steep time. Predicted antioxidant capacity of teas also demonstrated significant differences among teas after 5 and 10 min. In conclusion, steep time modulates TPC but not the antioxidative capacity of tea polyphenols.
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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 | 0.000 |
| 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 | 0.000 | 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".