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Record W4412489279 · doi:10.1080/10408398.2025.2525449

Phenolics and polyphenolics in mangrove plants: antioxidant activity and recent trends in food application – a review

2025· review· en· W4412489279 on OpenAlexafffund
Eliot Patrick Botosoa, Fereidoon Shahidi

Bibliographic record

VenueCritical Reviews in Food Science and Nutrition · 2025
Typereview
Languageen
FieldMedicine
TopicPhytochemicals and Antioxidant Activities
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPolyphenolAntioxidantShelf lifeFood industryMangroveBiotechnologyFood qualityFood scienceNatural foodFood processingFood productsChemistryBusinessBiologyEcologyBiochemistry

Abstract

fetched live from OpenAlex

Mangrove plants represent an interesting source of bioactive molecules that may be valuable for human use and especially for applications in the food sector. Phenolic compounds, ubiquitous in mangrove plants, are of considerable interest due to their antioxidant properties and other potential beneficial effects. This contribution aimed at gathering and emphasizing all recent works performed to unravel their antioxidant activities and use in food to improve food quality and extend shelf life. Food industries and food technologists may express a huge interest in their exploitation and valorization. Indeed, their addition and/or incorporation in food would allow to control off-flavor development, retard the formation of toxic oxidation products such as primary and secondary oxidation substances, maintain and improve nutritional quality, and to improve the shelf-life of foods. Based on the safety concerns and limitations on the use of synthetic antioxidants, their replacement with natural antioxidants provides a viable alternative.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.064
GPT teacher head0.395
Teacher spread0.331 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2025
Admission routes2
Has abstractyes

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