Phenolic compounds in cereal grains and effects of processing on their composition and bioactivities: a review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cereals are a staple food in the diets of many populations globally. Besides their nutritive function in food, they are also rich in various groups of bioactive compounds, especially polyphenols. Wheat, rice, barley, rye, oat, maize, millet, sorghum, and other cereal grains present a great variety of phenolic acids, flavonoids, proanthocyanidins, alkylresorcinols, and lignans, which can be affected in many ways by the post-harvest treatments and further processing of these feedstocks. This review discusses up-to-date studies about the effects of common cereal processing techniques on their phenolic composition, biological activities, and bioefficiency. Generally, mild thermal and high-pressure treatments enhance cereals’ phenolic composition by releasing the insoluble-bound fraction, which increases their bioaccessibility. On the other hand, processes involving extreme temperature conditions and removal of the grains’ outer layers may drastically reduce the phenolic content. Therefore, it is imperative to optimize the processing conditions of cereals, so their health-promoting benefits are preserved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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 | 0.001 |
| 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