Nutraceutical Potential of Herbal Beverages
Bibliographic record
Abstract
A variety of herbal beverages are consumed worldwide. Globalization facilitates the expansion of herbal beverage market beyond cultural and geographical boundaries. The Covid-19 pandemic has led to a significant growth in demand for herbal beverages due to their functionality in many regions of the world. Recent advances of phytochemical research have revealed the groups of available bioactive compounds of herbal beverages. Different morphological parts of herbs and plants, such as leaves, stem, bark, roots, flowers, and fruits, belonging to wide array of maturity stages are usually used. Herbal beverages can be used in everyday life as a component of the balanced diet, thus to enhance the antioxidant status and attenuate inflammatory conditions improving health and wellness. The constituent plant bioactive agents found in herbal beverages include phenolic acids, flavonoids, terpenoids coumarins, polyacetylenes, carotenoids, saponins, and alkaloids. An abundance of scientific evidence demonstrate that these phytochemicals provide numerous bioactivities, namely, antioxidant, anti-inflammatory, antibacterial, antiviral, antiallergic, antithrombotic, and vasodilatory actions, in addition to antimutagenic, anticarcinogenic, and antiaging properties among others. Moreover, tendency of consumer choices for natural, minimally processed, ethically sourced, healthy food products remains a main driver for increasing the global use of herbal 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".