<i>C</i>‐Glycosylflavone and Lignan Diglucoside Contents of Commercial, Regular, and Whole‐Wheat Spaghetti
Bibliographic record
Abstract
ABSTRACT Consumption of whole‐wheat products, including whole‐wheat spaghetti, is associated with beneficial health effects. Flavonoids and lignans are antioxidant phytochemicals that have received much attention from researchers. Investigations were conducted on the content of flavonoid glycosides, lignan diglucoside, and secoisolariciresinol diglucoside (SDG) as contributors to the health‐promoting properties of whole‐wheat spaghetti. Flavonoid glycosides present in regular and whole‐wheat spaghetti samples were identified as 6‐C‐glucosyl‐8‐C‐arabinosyl apigenin and the sinapic acid ester of apigenin‐C‐diglycoside while, in a previous study, the sinapic acid ester of apigenin‐C‐diglycoside was found only in wheat germ tissues. The content of these compounds was significantly higher in whole‐wheat spaghetti (17.0 and 15.1 μg of apigenin equivalent/g) compared to the regular brands (9.5 and 5.8 μg apigenin equivalent/g). SDG content was also significantly higher in whole‐wheat spaghetti (41.8 μg/g) compared to the regular brands (12.9 μg/g). These findings lend further support to the notion that phenolic compounds, along with dietary fiber, are concentrated in the bran layers of the wheat kernel; hence, consumption of whole grain products is strongly recommended to obtain significant levels of health‐promoting phytochemicals.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".