Optimization of the Extraction of Proanthocyanidins from Grape Seeds Using Ultrasonication-Assisted Aqueous Ethanol and Evaluation of Anti-Steatosis Activity In Vitro
Bibliographic record
Abstract
Conventional extraction methods of proanthocyanidins (PAC) are based on toxic organic solvents, which can raise concerns about the use of extracts in supplemented food and nutraceuticals. Thus, a PAC extraction method was developed for grape seeds (GS) and grape seed powder using food-grade ethanol by optimizing the extraction conditions to generate the maximum yield of PAC. Extraction parameters, % ethanol, solvent: solid (s:s) ratio, sonication time, and temperature were optimized by the central composite design of the response surface method. The yields of PAC under different extraction conditions were quantified by the methylcellulose precipitable tannin assay. The final optimum conditions were 47% ethanol, 10:1 s:s ratio (v:w), 53 min sonication time, and 60 °C extraction temperature. High-performance liquid chromatography analysis revealed the presence of catechin, procyanidin B2, oligomeric and polymeric PAC in the grape seed-proanthocyanidin extracts (GS-PAC). GS-PAC significantly reduced reactive oxygen species and lipid accumulation in the palmitic-acid-induced mouse hepatocytes (AML12) model of steatosis. About 50% of the PAC of the GS was found to be retained in the by-product of wine fermentation. Therefore, the developed ethanol-based extraction method is suitable to produce PAC-rich functional ingredients from grape by-products to be used in supplemented food and nutraceuticals.
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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 | 0.000 | 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 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".