A new analytical concept based on chemistry and toxicology for herbal extracts analysis: From phenolic composition to bioactivity
Bibliographic record
Abstract
Studies regarding the bioactivity of teas are mainly based on the phenolic composition and in vitro antioxidant activity of the herbal species used in their preparation. The aim of this study was to compare the in vitro and ex vivo antioxidant activity, cytotoxic/antiproliferative activity against cancer cells, the inhibitory activity of α-amylase, α-glucosidase and angiotensin I-converting enzymes, as well as the inhibition of DNA-induced fission of the peroxyl radical, in relation to aqueous extracts of Camellia sinensis var. sinensis (CS), Ilex paraguariensis (IP), Aspalathus linearis (AL) and an optimised extract (OT) containing the three herb species. A bivariate and multivariate statistical approach was employed to associate functional activities with individual phenolic composition. The CS and OT extracts showed the highest levels of hesperidin, quercetin-3-rutinoside, (-)-epigallocatechin-3-gallate and isoquercitrin. The CS and OT extracts showed the highest antioxidant activity, greater ability to inhibit α-amylase and proliferation of HCT8 cells, and greater ability to reduce Folin-Ciocalteu reagent. The AL extract, which is the major source of quercetin-3-rutinoside, hesperidin and isoquercitrin, showed the highest ability to inhibit α-glucosidase, the inhibition of LDL oxidation and protection of human erythrocytes. The IP extract showed the highest inhibition of lipoperoxidation in brain homogenate of Wistar rats, antihypertensive activity, and A549 cell proliferation; chlorogenic acid was its major phenolic compound. In general, the in vitro functionality of each extract was dependent on its chemical composition and the OT extract presented the most varied phenolic composition, and biological activity similar to the CS sample. In conclusion, the mixture of CS, AL, and IP represents a chemical and functional-based strategy to develop functional teas.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".