Antioxidant properties of commercial soft and hard winter wheats (<i>Triticum aestivum</i> L.) and their milling fractions
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Bibliographic record
Abstract
Abstract The total phenolic content (TPC), total antioxidant activity (TAA), 2,2‐diphenyl‐1‐picrylhydrazyl (DPPH) scavenging capacity, inhibition of coupled oxidation in a β‐carotene–linoleate model system, iron(II) chelation activity and inhibition of copper‐induced oxidation of human low‐density lipoprotein (hLDL) cholesterol of 80% ethanolic extracts of soft and hard winter wheat samples and their milling fractions, namely flour, germ, bran and shorts, were investigated. Soft wheat extracts examined demonstrated higher TPC and TAA compared to those of hard wheat extracts. The germ fraction possessed the highest TPC, followed by bran, shorts, whole grain and flour for both wheat types examined. The TAA of both wheat types showed similar results except that shorts performed better than bran in this assay. Free radical scavenging properties of whole grain and milling fractions of both soft and hard wheat samples were examined against DPPH radical. The germ and flour fractions demonstrated the highest and lowest DPPH radical scavenging activity, respectively, among wheat fractions. Wheat extracts were also efficient in preventing bleaching of β‐carotene, which is also known to be free radical mediated. In the iron(II) chelation assay the flour extracts demonstrated excellent activity, while the germ extracts showed a weak activity. The trends were similar in both soft and hard wheat for the iron(II) chelation assay. Wheat extracts also inhibited copper‐induced oxidation of hLDL. In LDL oxidation assay, wheat extracts performed better than the reference antioxidant, ferulic acid. Thus wheat phenolics may serve as effective antioxidative components as measured by in vitro techniques. Copyright © 2005 Society of Chemical Industry
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it