Ionic Liquids as Efficient Extractants for Quercetin from Red Onion (Allium cepa L.)
Bibliographic record
Abstract
The solubility of Quercetin in alcohols, esters and in 1-ethyl-3-methylimidazolium trifluoroacetate, [EMIM][TFA] ionic liquid (IL) using the dynamic method was measured at constant pH in a range of temperature 233-373 K and compare to the literature data. The experimental solubility data have been correlated by means of commonly known GE models,UNIQUAC and NRTL with the assumption that the systems studied here present simple eutectic behaviour. The basic thermal properties of Quercetin, i.e., fusion temperature and the enthalpy of fusion have been measured with differential scanning microcalorimetry technique (DSC).The application of alcohols, esters and ionic liquids (ILs) as alternatives to conventional organic solvents in the liquid-liquid extraction of Quercetin from different medicinal plants, flowers and frozen red onion (Allium cepa L.) was investigated. The parameters affecting the extraction yield using ILs such as chemical structures of the IL cation and anion, the phase volume ratio of extracting solvent, time of extraction and the Quercetin form of sample and concentration were evaluated. Specific Quercetin composition was performed through HPLC measurements. Using the most effective ILs in extraction, the 14.3±0.1g.kg-1 and 5.9±0.1g.kg-1of Quercetin from frozen pure red onion was obtained with N,N-diethyl-N-methyl-N-(2-methoxyethyl)ammonium tetrafluoroborate, [N2,2,1,2OCH3][BF4] and 1-ethyl-3-methylimidazolium trifluoroacetate, [EMIM][TFA], respectively.
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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 | 0.000 | 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) | 0.000 | 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 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".