Low Molecular Weight Phenolics of Grape Juice and Winemaking Byproducts: Antioxidant Activities and Inhibition of Oxidation of Human Low-Density Lipoprotein Cholesterol and DNA Strand Breakage
Bibliographic record
Abstract
Bioactive compounds belonging to phenolic acids, flavonoids, and proanthocyanidins of grape juice and winemaking byproducts were identified and quantified by HPLC-DAD-ESI-MS(n). The concentration of phenolic compounds in different grape cultivars was in the order Tempranillo > Cora > Syrah > Isabel. The insoluble-bound fraction was most prominent, contributing 63 and 79% to the total for Isabel and Tempranillo, respectively. Juice-processing byproducts had a higher content of free than esterified phenolics, but the opposite was noted for winemaking byproducts. Insoluble-bound phenolics were up to 15 and 10 times more effective as antioxidants than those of free and esterified fractions, respectively, as evaluated by the DPPH, ABTS, and H2O2 scavenging activities and reducing power determinations. In general, insoluble-bound phenolics (100 ppm) were more effective in inhibiting copper-induced human LDL-cholesterol oxidation than free and esterified phenolics, exhibiting equal or higher efficacy than catechin. Phenolic extracts from all fractions inhibited peroxyl radical-induced DNA strand breakage. These findings shed further light for future studies and industrial application of grape byproducts, which may focus not only on the soluble phenolics but also on the insoluble-bound fraction.
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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".