Prospecting Antioxidant Capacities and Health-Enhancing Phytonutrient Contents of Southern Highbush Blueberry Wine Compared to Grape Wines and Fruit Liquors
Bibliographic record
Abstract
Blueberry wines may have a multitude of health benefits, but few studies have quantified the health-enhancing antioxidants, total phenols, anthocyanins and flavonoids in blueberry wines, especially the Southern highbush blueberry wine, in comparison to grape wines and fruit liquors. This study was initiated to fill such a gap by measuring the antioxidant capacity and key phytonutrients of Southern highbush blueberry wine as compared to red, Rose and white wines and fruit liquors. The Oxygen Radical Absorbance Capacity (ORAC) of the Southern highbush blueberry wine tested in this study ranged from 18.54 to 25.48 mmol TE/L, with an average of 22.57 ± 2.92 mmol TE/L. This was higher than the ORAC values of over 80% of the red wines and 100% of the Rose and white wines reported in literature. A majority of the red wines were higher, but all the Rose and white wines and most fruit liquors were lower in total phenolic content than the Southern highbush blueberry wine. Anthocyanin contents of the blueberry wines were generally comparable to those of the red wines. Results show that blueberry wines could be more potent than most red, Rose and white wines in health enhancement and disease prevention from the antioxidant perspective.
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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 | 0.001 | 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".