Total Polyphenol Content and Antioxidant Capacity of Tea Bags: Comparison of Black, Green, Red Rooibos, Chamomile and Peppermint over Different Steep Times
Bibliographic record
Abstract
Globally, traditional and herbal teas are a prominent dietary source of polyphenols, and represent a class of bioactive molecules that are closely associated with a variety of health benefits. Most consumers prepare tea using tea bags, although there is little information about whether this production step alters the content of the final product. The study purpose was to investigate the effect of steep time and tea type on the polyphenol content and predicted antioxidant capacity of commercially available tea bag products, including Green, Orange Pekoe, Red Roiboos, Peppermint, and Chamomile. Total polyphenol content (TPC), antioxidant capacity (1,1-diphenyl-2-picrylhydrazyl inhibition), and total predicted antioxidant capacity were measured in aliquots sampled every minute for 10 min. Polyphenols were extracted into solution in a nonlinear fashion, with ~80–90% of the TPC appearing within 5 min of tea bag immersion. Moreover, a significant range in TPC values was observed between products, with true teas containing at least two-fold greater polyphenol content than the herbal varieties. Our results are consistent with previous work using loose-leaf tea products and demonstrates that tea bag products are an effective source of polyphenols that may offer health benefits relating to their constituent antioxidant activity.
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 | 0.000 |
| Bibliometrics | 0.001 | 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".