Comparison Between the Phytochemical and Antioxidant Properties of Plants Used in Plant Infusions for Medicinal Purposes
Bibliographic record
Abstract
It has already been acknowledged among the medical community that plant based treatments represent an interesting contribution to modern therapeutics due to the presence in their composition of molecules with pharmacological and antioxidant action. The aim of this study was to evaluate the contents of total phenolics, flavonoids, and caffeine in six plants used traditionally by healers in Portugal and usually consumed as tea or infusion namely: Camellia sinensis, Melissa officinalis, Lippia citriodora, Cymbopogon citratus, Matricaria chamomilla, and Tilia cordata. Total phenolics ranged from 32.05 mg GAE/100g for aqueous extracts obtained from leaves of L. citriodora to 145.28 mg GAE/100g for aqueous extracts of C. sinensis. Significant variations in the flavonoid content were also found among analyzed plants and depending on the nature of the extract, with C. sinensis standing out again with the highest values (78.31 mg CE/100g) and the ethanolic extract obtained from the flowers of T. cordata exhibiting the lowest content (25.15 mg CE/100g). The concentration of caffeine was also very diverse and followed the sequence M. officinalis < T. cordata < C. citratus < M. chamomilla < L. citriodora < C. sinensis. The antioxidant activity of each plant was evaluated in vitro using a standard model system, the DPPH assay, and was found to vary according to C. citratus (90.9%) > C. sinensis (87.8%) > M. officinalis (50.7%) > M. chamomilla (45.3%) > T. cordata (32.2%) > L. citriodora (28.0%). The aqueous extracts presented lower antioxidant activity than the corresponding ethanolic ones.
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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 | 0.001 | 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".