Bioactive components and antioxidant capacity of Ontario hard and soft wheat varieties
Bibliographic record
Abstract
Ragaee, S., Guzar, I., Abdel-Aal, E-S. M. and Seetharaman, K. 2012. Bioactive components and antioxidant capacity of Ontario hard and soft wheat varieties. Can. J. Plant Sci. 92: 19-30. Consumer awareness of food and health through improved diet has promoted research on the bioactive components of agricultural products. wholegrain wheat and products rich in wheat bran were found to inhibit oxidation of biologically important molecules such as DNA, LDL cholesterol and membrane lipids, and are linked with reduced incidence of several diseases. The main objective of the present study was to evaluate selected wheat varieties grown in Ontario based on their contents of bioactive compounds and antioxidant properties to identify potential candidates for the functional foods industry. The 21 wheat varieties obtained from different locations in Ontario varied significantly in soluble and bound phenolic acids, ranging between 114 to 155 and 805 to 1068 µg g-1, respectively. Dietary fiber fractions had narrow ranges being 2.8-4.0% for soluble dietary fiber and 10.1-13.0% for insoluble dietary fiber. Antioxidant capacity measured as DPPH radical inhibition ranged between 5.7-14.9% and 74.1-87.1% for soluble and bound phenolic compounds, respectively. The results demonstrate that certain wholegrain wheat varieties would be excellent sources of bioactive components.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".