Green extraction and characterization of leaves phenolic compounds: a comprehensive review
Bibliographic record
Abstract
Although containing significant levels of phenolic compounds (PCs), leaves biomass coming from either forest, agriculture, or the processing industry are considered as waste, which upon disposal, brings in environmental issues. As the demand for PCs in functional food, pharmaceutical, nutraceutical and cosmetic sector is escalating day by day, recovering PCs from leaves biomass would solve both the waste disposal problem while ensuring a valuable "societal health" ingredient thus highly contributing to a sustainable food chain from both economic and environmental perspectives. In our search for environmentally benign, efficient, and cost-cutting techniques for the extraction of PCs, green extraction (GE) is presenting itself as the best option in modern industrial processing. This current review aims to highlight the recent progress, constraints, legislative framework, and future directions in GE and characterization of PCs from leaves, concentrating particularly on five plant species (tea, moringa, stevia, sea buckthorn, and pistacia) based on the screened journals that precisely showed improvements in extraction efficiency along with maintaining extract quality. This overview will serve researchers and relevant industries engaged in the development of suitable techniques for the extraction of PCs with increasing yield.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".