Detection of Mycotoxin Contamination in Foods Using Artificial Intelligence: A Review

• Published in: Foods Vol. 13; no. 20; p. 3339
• Main Authors: Aggarwal, Ashish, Mishra, Akanksha, Tabassum, Nazia, Kim, Young-Mog, Khan, Fazlurrahman
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.10.2024; MDPI
• Subjects: Agricultural commodities; Agricultural pollution; Algorithms; Artificial intelligence; Biosensors; Cereals; Chromatography; Cognition & reasoning; Computational linguistics; Contamination; Damage detection; Data collection; Data mining; Datasets; Deep learning; Detectors; Food; Food contamination; Food contamination & poisoning; Food products; Food safety; Fungi; Human populations; Imaging techniques; Language processing; Liver cancer; Machine learning; Mass spectrometry; Mass spectroscopy; Metabolites; mycotoxin detection; Mycotoxins; Natural language interfaces; Neural networks; Oxytetracycline; Plant metabolites; Public health; Review; Safety and security measures; Scientific imaging; Scientists; Secondary metabolites; spectroscopy; Support vector machines; Technicians; Wheat; South Korea; food safety; deep learning; spectroscopy; chromatography; mycotoxin detection; machine learning; hyperspectral imaging; artificial intelligence; multi-mycotoxin detection
• ISSN: 2304-8158; 2304-8158
• DOI: 10.3390/foods13203339

Mycotoxin contamination of foods is a major concern for food safety and public health worldwide. The contamination of agricultural commodities employed by humankind with mycotoxins (toxic secondary metabolites of fungi) is a major risk to the health of the human population. Common methods for mycotoxin detection include chromatographic separation, often combined with mass spectrometry (accurate but time-consuming to prepare the sample and requiring skilled technicians). Artificial intelligence (AI) has been introduced as a new technique for mycotoxin detection in food, providing high credibility and accuracy. This review article provides an overview of recent studies on the use of AI methods for the discovery of mycotoxins in food. The new approach demonstrated that a variety of AI technologies could be correlated. Deep learning models, machine learning algorithms, and neural networks were implemented to analyze elaborate datasets from different analytical platforms. In addition, this review focuses on the advancement of AI to work concomitantly with smart sensing technologies or other non-conventional techniques such as spectroscopy, biosensors, and imaging techniques for rapid and less damaging mycotoxin detection. We question the requirement for large and diverse datasets to train AI models, discuss the standardization of analytical methodologies, and discuss avenues for regulatory approval of AI-based approaches, among other top-of-mind issues in this domain. In addition, this research provides some interesting use cases and real commercial applications where AI has been able to outperform other traditional methods in terms of sensitivity, specificity, and time required. This review aims to provide insights for future directions in AI-enabled mycotoxin detection by incorporating the latest research results and stressing the necessity of multidisciplinary collaboration among food scientists, engineers, and computer scientists. Ultimately, the use of AI could revolutionize systems monitoring mycotoxins, improving food safety and safeguarding global public health.