Evidence map›Paper›PMID 40423314›Full record

ReviewToxins2025

New Strategies and Artificial Intelligence Methods for the Mitigation of Toxigenic Fungi and Mycotoxins in Foods.

Fernando Mateo, Eva María Mateo, Andrea Tarazona, María Ángeles García-Esparza, José Miguel Soria, Misericordia Jiménez

Abstract readReview
In one paragraph

Review in Toxins, 2025. 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. Review
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  5. Article
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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.

Fernando MateoDepartment of Electronic Engineering, ETSE, (UV), Burjassot, 46100 Valencia, Spain.ORCID 0000-0001-6596-9371
Eva María MateoDepartment of Microbiology and Ecology, Faculty of Medicine and Odontology, University of Valencia (UV), 46010 Valencia, Spain.ORCID 0000-0001-7841-9838
Andrea TarazonaDepartment of Microbiology and Ecology, Faculty of Biology, (UV), Burjassot, 46100 Valencia, Spain.
María Ángeles García-EsparzaDepartment of Pharmacy, Cardenal Herrera University-CEU Universities, 46115 Valencia, Spain.ORCID 0000-0001-6510-0879
José Miguel SoriaDepartment of Biomedical Sciences, Cardenal Herrera University-CEU Universities, 46115 Valencia, Spain.ORCID 0000-0001-9144-5096
Misericordia JiménezDepartment of Microbiology and Ecology, Faculty of Biology, (UV), Burjassot, 46100 Valencia, Spain.ORCID 0000-0002-1404-4982

Funding

Ministerio de Ciencia, Innovación y Universidades PID2022-136803OB-I00
6 · The paper itself

Abstract

The proliferation of toxigenic fungi in food and the subsequent production of mycotoxins constitute a significant concern in the fields of public health and consumer protection. This review highlights recent strategies and emerging methods aimed at preventing fungal growth and mycotoxin contamination in food matrices as opposed to traditional approaches such as chemical fungicides, which may leave toxic residues and pose risks to human and animal health as well as the environment. The novel methodologies discussed include the use of plant-derived compounds such as essential oils, classified as Generally Recognized as Safe (GRAS), polyphenols, lactic acid bacteria, cold plasma technologies, nanoparticles (particularly metal nanoparticles such as silver or zinc nanoparticles), magnetic materials, and ionizing radiation. Among these, essential oils, polyphenols, and lactic acid bacteria offer eco-friendly and non-toxic alternatives to conventional fungicides while demonstrating strong antimicrobial and antifungal properties; essential oils and polyphenols also possess antioxidant activity. Cold plasma and ionizing radiation enable rapid, non-thermal, and chemical-free decontamination processes. Nanoparticles and magnetic materials contribute advantages such as enhanced stability, controlled release, and ease of separation. Furthermore, this review explores recent advancements in the application of artificial intelligence, particularly machine learning methods, for the identification and classification of fungal species as well as for predicting the growth of toxigenic fungi and subsequent mycotoxin production in food products and culture media.

Indexed as

Artificial IntelligenceFood ContaminationFood MicrobiologyFungiMycotoxinsAnimalsHumansMycotoxinscold plasmaessential oilsirradiationlactic acid bacteriamachine learningmagnetic materialsmycotoxinsnanoparticlespolyphenolstoxigenic fungi

Identifiers

PMID40423314
PMCPMC12115481

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.