Effects of debranning on the distribution of pentosans and relationships to phenolic content and antioxidant activity of wheat pearling fractions
Bibliographic record
Abstract
Pentosans represent the major dietary fiber constituent in wheat and the predominant source of antioxidant activity (AOA) whose nature is closely associated with the phenolic compound ferulic acid. Incremental debranning was used to determine effects on pentosan content and AOA of pearled fractions. Four cultivar samples were debranned to obtain pearling fractions, each equivalent to 5% of initial sample weight up to 60%. Total pentosans (TP), water-extractable pentosans (WEP), total phenolic content (TPC) and AOA were determined. There were significant differences among genotypes in response to debranning although trends were consistent. WEP was maximized in the 10% debranning fraction which likely maximized aleurone content. TP content was highest in the initial 5% fraction and progressively declined in successive fractions. Relationships between pentosan composition and AOA followed different trends. TP and water-unextractable pentosans (WUP) were highly correlated to TPC (R2 = 0.84) and AOA (R2 = 0.88) for the 10–25% debranning fractions. No significant correlations were found between TPC and AOA with WEP. Results indicated that debranning wheat to recover material between the initial 5% pearling fraction and the next 5% was effective to produce bran fractions for functional food or nutraceutical purposes, highly enriched in dietary fiber and AOA.
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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) | 0.001 | 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 | 0.001 |
| 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".