Evaluation of the Phenolics and in vitro Antioxidant Activity of Different Botanical Herbals Used for Tea Infusions in Brazil
Bibliographic record
Abstract
Background: The consumption of herbal teas has gained much attention due to its healthpromoting benefits, including antioxidant, neuroprotective, antimicrobial, antitumor, and antiinflammatory effects. These biological activities are associated in part to the antioxidant activity of chemical compounds present in teas, especially flavonoids and phenolic acids. Objective: The aim of this study was to evaluate a total of 17 different botanical herbal infusions consumed in Brazil in terms of their phenolic antioxidants. Methods: The analysis performed were total phenolic compounds, total flavonoids, total flavonols, tannin content and in vitro antioxidant activity (DPPH, ABTS, CUPRAC, FRAP, and ORAC assays). Data were processed using univariate, bivariate and multivariate analysis (hierarchical cluster analysis). Results: The use of Hierarchical Cluster Analysis (HCA) suggested an unsupervised classification relationship based on level of functionality of the herbal teas. Higher levels of total phenolics, total flavonoids and antioxidant activity were found in Anemopaegma mirandum while higher values of tannin content and total flavonols were found in Peumus boldus. All antioxidant activity assays showed significant correlations among each other (r > 0.84, p < 0.001), and with total phenolic and flavonoids (r > 0.83, p < 0.001). Using HCA, three clusters were suggested and cluster 1 showed the highest functionality. Conclusion: The herbal infusions evaluated can be a good resource of bioactive compounds to consume and supplementing food products. Nevertheless, future studies should focus on the evaluation of these herbal teas using in vivo systems to understand the mechanisms of action when these different herbal infusions are used as beverages.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".