Optimization of Solid−Liquid Extraction of Resveratrol and Other Phenolic Compounds from Milled Grape Canes (<i>Vitis vinifera</i>)
Bibliographic record
Abstract
Optimization of the solid-liquid extraction conditions for trans-resveratrol, trans--viniferin, ferulic acid, and total phenolics from milled grape canes has been investigated. The temperature and ethanol concentration were found to be major process variables for all responses, whereas the solvent to solid ratio was found not to be significant for any of the responses studied. The yields of trans-resveratrol, trans--viniferin, and total phenolics increased with increasing temperature. Maximum yields of trans-resveratrol (4.25 mg/g dw), trans--viniferin (2.03 mg/g), and total phenolics (9.28 mg/g dw) were predicted from the combination of a moderate ethanol concentration (50-70%) and the highest temperature (83.6 degrees C), whereas an ethanol concentration of 35% at the lowest temperature studied (16.4 degrees C) was optimal for the extraction of ferulic acid (1.05 mg/g dw). Effective diffusivity values of resveratrol in the solid phase, D eff for different extraction conditions, were calculated by fitting the experimental results to a model derived from the Fick's second law. Effective diffusivity of resveratrol in the solid phase varied from 3.1 x 10 (-13) to 26.6 x 10 (-13) m (2) s (-1) with changing extraction conditions. The increase in effective diffusivity of resveratrol was observed with increasing temperature, and the highest predicted level was obtained when using 54% ethanol/water mixture at 83.6 degrees C. The increase in ethanol concentration exhibited the favorable effect up to 50-55%, thereafter effective diffusivity decreased with a further increase in concentration.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".