Green Extraction of Polyphenols via Deep Eutectic Solvents and Assisted Technologies from Agri-Food By-Products
Bibliographic record
Abstract
Polyphenols are the largest group of phytochemicals with important biological properties. Their presence in conveniently available low-cost sources, such as agri-food by-products, has gained considerable attention in their recovery and further exploitation. Retrieving polyphenols in a green and sustainable way is crucial. Recently, deep eutectic solvents (DESs) have been identified as a safe and environmentally benign medium capable of extracting polyphenols efficiently. This review encompasses the current knowledge and applications of DESs and assisted technologies to extract polyphenols from agri-food by-products. Particular attention has been paid to fundamental mechanisms and potential applications in the food, cosmetic, and pharmaceutical industries. In this way, DESs and DESs-assisted with advanced techniques offer promising opportunities to recover polyphenols from agri-food by-products efficiently, contributing to a circular and sustainable economy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".