Evidence mapPaperPMID 38201039Full record

ReviewFoods (Basel, Switzerland)2023

Combining AI Tools with Non-Destructive Technologies for Crop-Based Food Safety: A Comprehensive Review.

Hind Raki, Yahya Aalaila, Ayoub Taktour, Diego H Peluffo-Ordóñez

Abstract readReview
In one paragraph

Review in Foods (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Artificial Intelligence in Food Safety: A Tertiary Study.Comprehensive reviews in food science and food safety · 2026
    Review
  3. Review
  4. Review
  5. Review
  6. Review
  7. Article
  8. Review
  9. Article
  10. 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

4 authors.

Hind RakiCollege of Computing, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco.ORCID 0000-0002-2670-1011
Yahya AalailaCollege of Computing, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco.ORCID 0000-0002-0661-317X
Ayoub TaktourMaterials Sciences and Nanotechnoloy (MSN), University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco.ORCID 0009-0009-8677-8940
Diego H Peluffo-OrdóñezCollege of Computing, University Mohammed VI Polytechnic, Ben Guerir 43150, Morocco.ORCID 0000-0002-9045-6997

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

On a global scale, food safety and security aspects entail consideration throughout the farm-to-fork continuum, considering food's supply chain. Generally, the agrifood system is a multiplex network of interconnected features and processes, with a hard predictive rate, where maintaining the food's safety is an indispensable element and is part of the Sustainable Development Goals (SDGs). It has led the scientific community to develop advanced applied analytical methods, such as machine learning (ML) and deep learning (DL) techniques applied for assessing foodborne diseases. The main objective of this paper is to contribute to the development of the consensus version of ongoing research about the application of Artificial Intelligence (AI) tools in the domain of food-crop safety from an analytical point of view. Writing a comprehensive review for a more specific topic can also be challenging, especially when searching within the literature. To our knowledge, this review is the first to address this issue. This work consisted of conducting a unique and exhaustive study of the literature, using our

Indexed as

chemometricsfood contaminantsfood processesmachine learningspectroscopysustainability

Identifiers

PMID38201039
PMCPMC10777928

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.