Optimization of green extraction of high‐value components from <i>Eugenia uniflora</i> leaves: Thermal stability and in‐vitro biological activity
Bibliographic record
Abstract
Abstract Deep eutectic solvents (DES) were evaluated as a sustainable route to obtain bioactive compounds from Eugenia uniflora L. leaves (EUL). Completely randomized experimental designs determined the process conditions. The DES composed of cholinium chloride as hydrogen bond acceptor (HBA) and lactic acid, glycerol, or 1,2‐propanediol as hydrogen bond donor (HBD) showed the best extraction capacity. The better conditions were 150 min, 65°C, and a solid:liquid ratio of 1:30. The major constituents of the extracts, quantified by high‐performance liquid chromatography (HPLC), were naringin and caffeic acid. Moreover, the DES promoted a preservative effect on the EUL extracts against thermal treatments. All extracts exhibited high antioxidant and antiglycation activities and iron‐chelating potential. Moreover, when lactic acid was the HBD, the extracts showed considerable antibacterial activity. Therefore, it was effectively proposed as an effective and green route to obtain extracts with potential applications in the food, cosmetic, and pharmaceutical industries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".