Fractionation of Blue Wheat Anthocyanin Compounds and Their Contribution to Antioxidant Properties
Bibliographic record
Abstract
Demands for anthocyanin-rich foods and supplements are steadily increasing due to their significant roles in human health and food coloration. In the development of blue wheat as a functional food ingredient, dry milling was employed to separate the bran fraction where anthocyanins are concentrated 2.3-fold as compared to whole grain (13.9 mg/100 g). Anthocyanins were then extracted with aqueous ethanol and partially purified into a highly concentrated powder. The total anthocyanin content in the isolated powder as analyzed by high-performance liquid chromatography (HPLC) averaged 3378 mg/100 g and afforded a 243-fold increase in concentration relative to the whole grain product. Four main anthocyanins, delphinidin-3-glucoside (45%), cyanidin-3-glucoside (28%), delphinidin-3-rutinoside (22%), and cyanidin-3-rutinoside (2%), were isolated from the powder using preparative HPLC. Anthocyanin products and compounds were assessed against scavenging of 2,2-diphenyl-1-picryl-hydrazyl and 2,2'-azino-di-(3-ethylbenzthiazoline sulfonate) radicals and inhibition of human low-density lipoprotein cholesterol oxidation. Significant differences in antioxidant capacity were observed with anthocyanin powder and compounds exceeding that of butylated hydroxytoluene, indicating a potential for the development of blue wheat-based natural antioxidants and colorants.
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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".