Composition and Stability of Anthocyanins in Blue-Grained Wheat
Bibliographic record
Abstract
Wheat grain is recognized as a good source of potentially health-enhancing components such as dietary fiber, phenolics, tocopherols, and carotenoids. Anthocyanins, another group of bioactive compounds, are found in blue and purple wheat grains. In the present study, a blue aleurone spring wheat line "Purendo 38" with relatively high content of total anthocyanins was used to investigate the composition and stability of anthocyanins over three crop years. Commercial cultivars of purple (Konini) and red (Katepwa) wheats were included in the study. Separation of anthocyanins by high-performance liquid chromatography (HPLC) showed that each wheat had a distinct anthocyanin profile. Four major anthocyanins were separated from blue wheat extracts as compared to five anthocyanins in purple wheat. Cyanidin 3-glucoside was the predominant anthocyanin in purple wheat, whereas it was the second major anthocyanin in blue wheat. The predominant anthocyanin in blue wheat, making up approximately 41% of the total anthocyanin content, remains to be structurally unidentified. Blue wheat anthocyanins were thermally most stable at pH 1. Their degradation was slightly lower at pH 3 as compared to pH 5. Increasing the temperature from 65 to 95 degrees C increased degradation of blue wheat anthocyanins. Addition of SO(2) during heating of blue wheat had a stabilizing effect on anthocyanin pigments. The optimal SO(2) concentrations were 500-1000 ppm for whole meals and 1000-3000 ppm for isolated anthocyanins. Further studies are underway to identify and verify individual anthocyanins in blue wheat and their potential end uses.
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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) | 0.000 | 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 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".