Resveratrol in food systems: challenges, innovations, and health potential
Bibliographic record
Abstract
The integration of resveratrol, a naturally occurring, health promoting polyphenolic stilbene, into food systems poses a challenge due to its low water solubility, chemical instability, poor bioavailability, and bitter taste. To address these limitations, recent studies have focused on incorporating resveratrol into a range of food matrices such as wine, bakery, dairy, and meat products, using encapsulation techniques designed to enhance its stability during processing, maintain its therapeutic effects, and preserve desirable sensory attributes. This review provides a comprehensive overview of resveratrol’s plant sources, chemical characteristics, bioavailability, and health-promoting mechanisms while critically examining recent innovations in food-grade delivery systems, the functional roles of resveratrol in fortified food products and the associated barriers. Emphasis is placed on formulation challenges, matrix-specific applications, and future directions to improve consumer-relevant health benefits. • Resveratrol health benefits have been extensively tested • Physico-chemical properties of resveratrol limited its use in foods • Encapsulation enhances resveratrol stability in food processing • Resveratrol-rich ingredients can be used to produce functional foods • Food fortification with resveratrol allows obtaining health promoting foods
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".