Evidence map›Paper›PMID 42761358›Full record

ReviewFood science & nutrition2026

Artificial Intelligence for Food Safety and Quality Assurance: Technologies, Applications and Future Directions.

Harsh B Jadhav, Moumita Das, Arpita Das, Aayeena Altaf, Kamal Alaskar, Robert Mugabi

Abstract readReview
In one paragraph

Review in Food science & nutrition, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Harsh B JadhavFood Technology, Amity Institute of Biotechnology Amity University Jaipur Rajasthan India.ORCID https://orcid.org/0000-0002-6344-6865
Moumita DasDepartment of Food and Nutrition Swami Vivekananda University Barrackpore West Bengal India.ORCID https://orcid.org/0009-0007-0831-2657
Arpita DasDepartment of Food and Nutrition Brainware University Kolkata West Bengal India.
Aayeena AltafFood Technology, Amity Institute of Biotechnology Amity University Jaipur Rajasthan India.
Kamal AlaskarDepartment of Computer Applications Bharati Vidyapeeth, Institute of Management Kolhapur India.
Robert MugabiDepartment of Food Technology and Nutrition Makerere University Kampala Uganda.ORCID https://orcid.org/0000-0003-3218-3684

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Food quality and safety are critical components of consumer welfare. Food processing firms have been compelled to implement innovative measures to ensure food safety due to growing consumer demand for safe food. One new technology that has been applied in the food processing industry is artificial intelligence. Artificial intelligence-based technologies, such as machine learning, blockchain, robotics, computer vision, and sensor networks, assist in identifying pathogens in food, detecting early outbreaks, and cleaning food processing equipment during processing and storage. Furthermore, the use of these technologies to enhance food safety has been driven by the goal of achieving a sustainable food system. The primary focus of this review is the application of AI-based technology to enhance food safety and quality assurance.

Indexed as

artificial intelligenceblockchainfood safetymachine learningquality assurance

Identifiers

PMID42761358
PMCPMC13586437

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.