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

被引:11
作者
Aggarwal, Ashish [1 ]
Mishra, Akanksha [1 ]
Tabassum, Nazia [2 ,3 ]
Kim, Young-Mog [2 ,3 ,4 ]
Khan, Fazlurrahman [2 ,3 ,5 ,6 ]
机构
[1] Lovely Profess Univ, Sch Bioengn & Biosci, Phagwara 144001, Punjab, India
[2] Pukyong Natl Univ, Natl Key Res Inst Univ, Marine Integrated Biomed Technol Ctr, Busan 48513, South Korea
[3] Pukyong Natl Univ, Res Ctr Marine Integrated Technol B, Busan 48513, South Korea
[4] Pukyong Natl Univ, Dept Food Sci & Technol, Busan 48513, South Korea
[5] Pukyong Natl Univ, Ocean & Fisheries Dev Int Cooperat Inst, Busan 48513, South Korea
[6] Pukyong Natl Univ, Int Grad Program Fisheries Sci, Busan 48513, South Korea
关键词
mycotoxin detection; artificial intelligence; deep learning; machine learning; spectroscopy; chromatography; hyperspectral imaging; food safety; multi-mycotoxin detection; FEED; AFLATOXIN; CHAIN;
D O I
10.3390/foods13203339
中图分类号
TS2 [食品工业];
学科分类号
0832 ;
摘要
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.
引用
收藏
页数:18
相关论文
共 101 条
[41]   Emerging health threat and cost of Fusarium mycotoxins in European wheat [J].
Johns, Louise E. ;
Bebber, Daniel P. ;
Gurr, Sarah J. ;
Brown, Neil A. .
NATURE FOOD, 2022, 3 (12) :1014-+
[42]   Metataxonomic Mapping of the Microbial Diversity of Irish and Eastern Mediterranean Cheeses [J].
Kamilari, Eleni ;
Tsaltas, Dimitrios ;
Stanton, Catherine ;
Ross, R. Paul .
FOODS, 2022, 11 (16)
[43]   Prediction of Penicillium expansum spoilage and patulin concentration in apples used for apple juice production by electronic nose analysis [J].
Karlshoj, Kristian ;
Nielsen, Per V. ;
Larsen, Thomas O. .
JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY, 2007, 55 (11) :4289-4298
[44]   Retrospective analysis of the key molecules involved in the green synthesis of nanoparticles [J].
Khan, Fazlurrahman ;
Jeong, Geum-Jae ;
Singh, Priyanka ;
Tabassum, Nazia ;
Mijakovic, Ivan ;
Kim, Young-Mog .
NANOSCALE, 2022, 14 (40) :14824-14857
[45]   A novel method for non-invasive detection of aflatoxin contaminated dried figs with deep transfer learning approach [J].
Kilic, Cihan ;
Inner, Burak .
ECOLOGICAL INFORMATICS, 2022, 70
[46]   Detection of aflatoxins in ground maize using a compact and automated Raman spectroscopy system with machine learning [J].
Kim, Yong-Kyoung ;
Qin, Jianwei ;
Baek, Insuck ;
Lee, Kyung-Min ;
Kim, Sung-Youn ;
Kim, Seyeon ;
Chan, Diane ;
Herrman, Timothy J. ;
Kim, Namkuk ;
Kim, Moon S. .
CURRENT RESEARCH IN FOOD SCIENCE, 2023, 7
[47]   Predicting early mycotoxin contamination in stored wheat using machine learning [J].
Kim, Yonggik ;
Kang, Seokho ;
Ajani, Oladayo Solomon ;
Mallipeddi, Rammohan ;
Ha, Yushin .
JOURNAL OF STORED PRODUCTS RESEARCH, 2024, 106
[48]   Deep learning for small and big data in psychiatry [J].
Koppe, Georgia ;
Meyer-Lindenberg, Andreas ;
Durstewitz, Daniel .
NEUROPSYCHOPHARMACOLOGY, 2021, 46 (01) :176-190
[49]   Diverse mycotoxin threats to safe food and feed cereals [J].
Latham, Rosie L. ;
Boyle, Jeremy T. ;
Barbano, Anna ;
Loveman, William G. ;
Brown, Neil A. .
ESSAYS IN BIOCHEMISTRY, 2023, 67 (05) :797-809
[50]   The impact of seasonal weather variation on mycotoxins: maize crop in 2014 in northern Italy as a case study [J].
Leggieri, M. Camardo ;
Lanubile, A. ;
Dall'Asta, C. ;
Pietri, A. ;
Battilani, P. .
WORLD MYCOTOXIN JOURNAL, 2020, 13 (01) :25-36