Phenolics of <i>Vaccinium</i> berries and other fruit crops
Bibliographic record
Abstract
Abstract The concentrations and profiles of phenolics of selected fruit crops common in the Western diet, including several Vaccinium species, were examined to better understand how these crops may be useful sources of phenolic phytochemicals. Vaccinium fruit had a high phenolic concentration compared to non‐ Vaccinium fruit. Some Vaccinium fruit were particularly rich in certain phenolic subgroups, especially anthocyanins and pro‐anthocyanidins. Among the pro‐anthocyanidin oligomers measured using fluorometric and mass spectroscopic detection, the trimers and tetramers were most abundant, while pro‐anthocyanidins with a degree of polymerization greater than 8 were least abundant. As biomedical studies determine which phenolic structures are associated with particular bioactivities, information on the phenolic concentration and profile of selected species will be useful in developing specific uses for fruit crops in human health. Methods were compared to assess the usefulness of simpler versus more sophisticated means of phenolic analysis. The phenolic components of fruit extracts were purified approximately 20‐fold, and not qualitatively altered, by C18 solid‐phase extraction. However, fruit extracts obtained from C18 solid‐phase extraction differed in their relative abundance of phenolic components. Colorimetric and HPLC‐DAD measures of phenolic concentration were correlated ( R 2 = 0.79), as was pro‐anthocyanidin concentration detected fluorometrically and by mass spectrometry ( R 2 = 0.44). Copyright © 2007 Crown in the right of Canada and Society of Chemical Industry
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".