Antioxidant Properties of Wheat As Affected by Pearling
Bibliographic record
Abstract
The effects of pearling on the content of phenolics and antioxidant capacity of two Canadian wheat classes, namely, Canada Western Amber Durum; Triticum turgidum L. var. durum; CWAD) and Canada Western Red Spring; Triticum aestivum L.; CWRS) were examined. The antioxidant activity of wheat phenolics was evaluated using oxygen radical absorbance capacity (ORAC), inhibition of photochemiluminescence (PCL), Rancimat method, inhibition of oxidation of low-density lipoprotein, and DNA. The phenolic composition of wheat extracts was determined using high-performance liquid chromatography. The antioxidant capacity of both pearled grains and byproducts significantly decreased as the degree of pearling increased. Among grains, the unprocessed whole grains demonstrated the highest antioxidant capacity. The byproducts always demonstrated higher antioxidant capacity compared to the pearled grains, regardless of the wheat class. The resultant byproducts from 10-20% pearling possessed the highest antioxidant capacity. Processing of cereals may thus exert a significant effect on their antioxidant activity. The concentration of grain antioxidants is drastically reduced during the refining process. As phenolic compounds are concentrated in the outermost layers, the bran fractions resulting from pearling may be used as a natural source of antioxidants and as value-added products in the preparation of functional food ingredients or for enrichment of certain products.
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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".