Juice quality and polyphenol concentration of fresh fruits and pomace of selected Nova Scotia-grown grape cultivars
Bibliographic record
Abstract
Juice, fresh fruit and pomace of 10 selected Nova Scotia-grown grape cultivars (five table and five wine cultivars) were evaluated for their physicochemical parameters and major phenolic compounds to determine their suitability in use for developing functional beverages. Colour intensity, total soluble solids, total anthocyanin concentration and antioxidant capacity were significantly higher in juice of wine grape cultivars than in juice of table grape cultivars. Titratable acidity was not significantly different among the juice of the two categories. In juice, a high positive correlation was observed between total phenolic concentration measured using Folin-Ciocalteu assay and antioxidant capacity measured using oxygen radical absorbance capacity (ORAC) (r = 0.87) and ferric reducing antioxidant power (FRAP) (r = 0.95). Flavan-3-ols, stilbenes and anthocyanin concentrations were significantly higher in fruits of wine grape cultivars, while the distribution of flavonols was highly variable in two categories. The descending order of total phenol concentration in fruits was: Lucie Kuhlman > Baco Noir > Marechal Foch > Castel 19637 > Sovereign Coronation > Leon Millot > Van Buren > Swenson Red > Suffolk Red > Reliance. The average overall concentrations of flavan-3-ols, flavonols, stilbenes and anthocyanins in pomace were 40, 90, 60 and 50% higher than that of the fresh fruits. Among the cultivars evaluated, Castel 19637 and Lucie Kuhlman showed the greatest potential for the development of a functional beverage; however, further characterization on sensory quality attributes is required.Key words: Grape, juice, pomace, antioxidant capacity, anthocyanin, stilbenes, flavan-3-ols, flavonols
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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) | 0.001 | 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".