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Aggarwal A, Mishra A, Tabassum N, Kim YM, Khan F. Detection of Mycotoxin Contamination in Foods Using Artificial Intelligence: A Review. Foods 2024; 13:3339. [PMID: 39456400 PMCID: PMC11507438 DOI: 10.3390/foods13203339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2024] [Revised: 10/15/2024] [Accepted: 10/19/2024] [Indexed: 10/28/2024] Open
Abstract
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.
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Affiliation(s)
- Ashish Aggarwal
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144001, Punjab, India; (A.A.); (A.M.)
| | - Akanksha Mishra
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara 144001, Punjab, India; (A.A.); (A.M.)
| | - Nazia Tabassum
- Marine Integrated Biomedical Technology Center, The National Key Research Institutes in Universities, Pukyong National University, Busan 48513, Republic of Korea; (N.T.); (Y.-M.K.)
- Research Center for Marine Integrated Bionics Technology, Pukyong National University, Busan 48513, Republic of Korea
| | - Young-Mog Kim
- Marine Integrated Biomedical Technology Center, The National Key Research Institutes in Universities, Pukyong National University, Busan 48513, Republic of Korea; (N.T.); (Y.-M.K.)
- Research Center for Marine Integrated Bionics Technology, Pukyong National University, Busan 48513, Republic of Korea
- Department of Food Science and Technology, Pukyong National University, Busan 48513, Republic of Korea
| | - Fazlurrahman Khan
- Marine Integrated Biomedical Technology Center, The National Key Research Institutes in Universities, Pukyong National University, Busan 48513, Republic of Korea; (N.T.); (Y.-M.K.)
- Research Center for Marine Integrated Bionics Technology, Pukyong National University, Busan 48513, Republic of Korea
- Ocean and Fisheries Development International Cooperation Institute, Pukyong National University, Busan 48513, Republic of Korea
- International Graduate Program of Fisheries Science, Pukyong National University, Busan 48513, Republic of Korea
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