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Record W4417281471 · doi:10.1016/j.jff.2025.107127

Resveratrol in food systems: challenges, innovations, and health potential

2025· article· en· W4417281471 on OpenAlexafffund
Jenny Bouchard, Nicola Gasparre, Thomas Netticadan, Cristina M. Rosell

Bibliographic record

VenueJournal of Functional Foods · 2025
Typearticle
Languageen
FieldMedicine
TopicSirtuins and Resveratrol in Medicine
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaResearch Manitoba
KeywordsResveratrolHealth benefitsFunctional foodPolyphenolFood productsHuman healthFood fortification

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.301

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.320
Teacher spread0.270 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2025
Admission routes2
Has abstractyes

Explore more

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