Evidence map›Paper›PMID 41717367›Full record

ReviewFood chemistry: X2026

Artificial intelligence in functional food innovation: Bioactive enhancement and formulation optimization: A quasi-systematic review.

Nadia Alkalbani, Leen Shahin, Hiba Benzeghiba, Reyad S Obaid, Tareq M Osaili, Leila Cheik Ismail, Ghayah Al Qasssimi, Maha Rauf, Khawla Abdulrahim, Afra Almashgouni and 2 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Food chemistry: X, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Nadia AlkalbaniDepartment of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, PO Box 27272, United Arab Emirates.
Leen ShahinDepartment of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, PO Box 27272, United Arab Emirates.
Hiba BenzeghibaDepartment of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, PO Box 27272, United Arab Emirates.
Reyad S ObaidDepartment of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, PO Box 27272, United Arab Emirates.
Tareq M OsailiDepartment of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, PO Box 27272, United Arab Emirates.
Leila Cheik IsmailDepartment of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, PO Box 27272, United Arab Emirates.
Ghayah Al QasssimiDepartment of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, PO Box 27272, United Arab Emirates.
Maha RaufDepartment of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, PO Box 27272, United Arab Emirates.
Khawla AbdulrahimDepartment of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, PO Box 27272, United Arab Emirates.
Afra AlmashgouniDepartment of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, PO Box 27272, United Arab Emirates.
Fatima AshuweihiDepartment of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, PO Box 27272, United Arab Emirates.
Dana Al-FuqahaDepartment of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, PO Box 27272, United Arab Emirates.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) is increasingly integrated into functional food research. This quasi-systematic review analyzes 53 peer-reviewed studies (2015-2025) to outline current applications and emerging directions, including the underexplored domain of antioxidant food development. The review attempts to provide an updated synthesis of AI approaches across compound discovery, metabolomics, and consumer modeling, emphasizing knowledge gaps and opportunities for methodological integration. Data-driven AI (classical machine learning) and deep learning methods have been applied to predict antioxidant activity, identify bioactive compounds, and reveal patterns in metabolomic data. Unsupervised approaches have assisted in clustering complex datasets, whereas optimization algorithms supported the adjustment of sensory, nutritional, and functional attributes. However, many current systems remain limited to

Indexed as

Bioactive compoundsBioavailabilityExplainable AIFunctional foodsMetabolomicsMulti-omicsOptimization

Identifiers

PMID41717367
PMCPMC12914456

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.