Bioaccessibility and bioavailability of phenolic compounds in bread: a review
Bibliographic record
Abstract
Cereal-based products, like breads, are a vehicle for bioactive compounds, including polyphenols. The health effects of polyphenols like phenolic acids (PAs) are dependent on their bioaccessibility and bioavailability. The present review summarizes the current understanding of potential strategies to improve phenolic bioaccessibility and bioavailability and the main findings of in vitro and in vivo studies investigating these strategies applied to breads, including the use of raw ingredients with greater phenolic content and different pre-processing technologies, such as fermentation and enzymatic treatment of ingredients. There is considerable variability between in vitro studies, mainly resulting from the use of different methodologies, highlighting the need for standardization. Of the few in vivo bioavailability studies identified, acute, single-dose studies demonstrate that modifications to selected raw materials and bioprocessing of bran could increase the bioavailability, but not necessarily the net content, of bread phenolics. The two medium-term identified dietary interventions also demonstrated greater phenolic content, resulting from the modification of the raw materials used. Overall, the findings suggest that several strategies can be used to develop new bread products with greater phenolic bioaccessibility and bioavailability. However, due to the large variability and the few studies available, further investigations are required to determine better the usefulness of these innovative processes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".