Characterization of anthocyanin‐containing purple wheat prototype products as functional foods with potential health benefits
Bibliographic record
Abstract
Abstract Background and objectives Purple wheat is relatively rich in anthocyanin and phenolic acid compounds that have demonstrated positive physiological effects in humans. This study was intended to develop purple wheat (PW) food prototypes with potential to impact metabolic markers and health conditions. Several products including PW bars, crackers, bread, pancake, and porridge were developed and evaluated based on their nutrient content, anthocyanin composition, and antioxidant properties. Findings The products substantially varied in their nutrient contents with protein being high in PW bread, pancake, and porridge (16.5%–18.8%) and intermediate in PW bars and crackers (8.9%–11%–6%). The bran‐enriched PW products and whole wheat pancake were rich in dietary fiber (16.4%–32.6%). The bran was added to boost levels of anthocyanin and dietary fiber in products. The crackers and bars had the highest anthocyanin content (55.9 and 41.7 µg/g, respectively) and were selected over other products for antioxidant testing in vitro. These two products strongly inhibited ABTS, DPPH, and peroxyl radical activities pointing to potential to influence metabolic markers and general health. Conclusions The PW bars and crackers hold promise as functional foods based on their contents of polyphenols and dietary fiber and antioxidant properties. Significance and novelty The study provides useful information about the innovative PW bars and crackers for further human studies.
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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".