Wheat and Rice beyond Phenolic Acids: Genetics, Identification Database, Antioxidant Properties, and Potential Health Effects
Bibliographic record
Abstract
Wheat and rice play a vital role in human nutrition and food security. A better understanding of the potential health benefits associated with consuming these cereals, combined with studies by plant scientists and food chemists to view the entire food value chain from the field, pre and post-harvest processing, and subsequent "fork" consumption, may provide the necessary tools to optimize wheat and rice production towards the goal of better human health improvement and food security, providing tools to better adapt to the challenges associated with climate change. Since the available literature usually focuses on only one food chain segment, this narrative review was designed to address the identities and concentration of phenolics of these cereal crops from a farm-to-fork perspective. Wheat and rice genetics, phenolic databases, antioxidant properties, and potential health effects are summarized. These cereals contain much more than phenolic acids, having significant concentrations of flavonoids (including anthocyanins) and proanthocyanidins in a cultivar-dependent manner. Their potential health benefits in vitro have been extensively studied. According to a number of in vivo studies, consumption of whole wheat, wheat bran, whole rice, and rice bran may be strategies to improve health. Likewise, anthocyanin-rich cultivars have shown to be very promising as functional foods.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".