Volume 29, Issue 3 (8-2026)
J Arak Uni Med Sci 2026, 29(3): 0-0 |
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Hasheminia A, Bakhtiari Sardari A, Vaezi A. Application of Artificial Intelligence in Rapid Detection of Mycotoxins and Food Safety Enhancement. J Arak Uni Med Sci 2026; 29 (3)
URL: http://jams.arakmu.ac.ir/article-1-8183-en.html
URL: http://jams.arakmu.ac.ir/article-1-8183-en.html
1- Department of Laboratory Sciences, Student Research Committee, School of Allied Medical Sciences, Iran University of medical sciences, Tehran, Iran
2- Department of Laboratory Sciences, School of Allied Medical Sciences, Iran University of medical sciences, Tehran, Iran ,vaeziiafsane@gmail.com
2- Department of Laboratory Sciences, School of Allied Medical Sciences, Iran University of medical sciences, Tehran, Iran ,
Abstract: (67 Views)
Introduction: Mycotoxins are secondary metabolites produced by certain fungal species, primarily belonging to the genera Aspergillus, Fusarium, Alternaria, and Penicillium. They are important natural contaminants of food and agricultural products, including maize, peanuts, tree nuts, cereals, fruits, and animal feed. Mycotoxins threaten human and animal health, adversely affect agricultural productivity, and cause substantial economic losses. Aflatoxin, ochratoxin, trichothecenes, fumonisin, zearalenone and patulin are some of the main mycotoxins affecting food and agricultural products. Although, mycotoxin-producing fungi are usually identified by biological methods, most mycotoxins are identified and quantified by traditional methods based on analytical chemistry.
Methods: These methods are time-consuming, laborious, costly, and unstable due to the diversity of the sampling process of different products. This study was conducted to investigate recent advances and applications of artificial intelligence (AI) in the detection, prediction and measurement of mycotoxin contamination. The integration of advances in computer science and AI with spectroscopy and imaging technologies have overcome the limitations of traditional methods.
Conclusions: AI techniques, using advanced algorithms, are able to detect mycotoxins with high accuracy and efficiency in various types of agricultural products. However, the use of AI in mycotoxin detection requires diverse data and appropriate computational infrastructure. Future research should focus on developing more optimized algorithms, integrating multi-source data, and practical implementation of these technologies in industrial environments to ensure food safety.
Methods: These methods are time-consuming, laborious, costly, and unstable due to the diversity of the sampling process of different products. This study was conducted to investigate recent advances and applications of artificial intelligence (AI) in the detection, prediction and measurement of mycotoxin contamination. The integration of advances in computer science and AI with spectroscopy and imaging technologies have overcome the limitations of traditional methods.
Conclusions: AI techniques, using advanced algorithms, are able to detect mycotoxins with high accuracy and efficiency in various types of agricultural products. However, the use of AI in mycotoxin detection requires diverse data and appropriate computational infrastructure. Future research should focus on developing more optimized algorithms, integrating multi-source data, and practical implementation of these technologies in industrial environments to ensure food safety.
Keywords: Artificial intelligence, mycotoxin detection, food contamination, hyperspectral imaging, fungi
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