Bibliographic record
Abstract
One type of functional food that has been receiving much attention is food rich in prebiotics. The old but still valid definition of prebiotics defines them as non-digestible food components that selectively stimulate the growth and/or activity of the beneficial bacteria in the colon and, as a result, improve the host health. Cereals, as one of the main components in the human diet, contain substantial levels of dietary fiber with probable prebiotic potential. In addition, dietary fiber, particularly soluble dietary fiber, has recently emerged as a promising natural highly functional food ingredient in food production. This review focuses on the prebiotic potential of cereal dietary fiber types and covers the achievements and developments regarding its isolation. First, the probiotic and prebiotic concepts will be discussed. Next, different components of dietary fiber and their effect on the host bacteria through in vitro and/or in vivo studies will be reviewed. In a last part, this paper also discusses means of boosting the prebiotic properties of cereal components and innovative strategies for the extraction of cereal dietary fiber. The review focuses on wheat as a leading cereal crop that is widely and intensely used throughout the world in food production.
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.
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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".