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.
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页数:18
相关论文
共 101 条
[1]   Contextual deep learning-based audio-visual switching for speech enhancement in real-world environments [J].
Adeel, Ahsan ;
Gogate, Mandar ;
Hussain, Amir .
INFORMATION FUSION, 2020, 59 :163-170
[2]  
Aggarwal A., 2018, Biomed. Res, V29, P218, DOI [10.4066/biomedicalresearch.29-16-1853, DOI 10.4066/BIOMEDICALRESEARCH.29-16-1853]
[3]   Advances in Analysis and Detection of Major Mycotoxins in Foods [J].
Agriopoulou, Sofia ;
Stamatelopoulou, Eygenia ;
Varzakas, Theodoros .
FOODS, 2020, 9 (04)
[4]   Machine-Learning-Based Disease Diagnosis: A Comprehensive Review [J].
Ahsan, Md Manjurul ;
Luna, Shahana Akter ;
Siddique, Zahed .
HEALTHCARE, 2022, 10 (03)
[5]   Determination of aflatoxins in coffee by means of ultra-high performance liquid chromatography-fluorescence detector and fungi isolation [J].
Al-Ghouti, Mohammad A. ;
AlHusaini, Aisha ;
Abu-Dieyeh, Mohammed H. ;
Abd Elkhabeer, Mahmoud ;
Alam, Muhammad Masood .
INTERNATIONAL JOURNAL OF ENVIRONMENTAL ANALYTICAL CHEMISTRY, 2022, 102 (18) :6999-7014
[6]   Detection of fusarium head blight in wheat under field conditions using a hyperspectral camera and machine learning [J].
Almoujahed, Muhammad Baraa ;
Rangarajan, Aravind Krishnaswamy ;
Whetton, Rebecca L. ;
Vincke, Damien ;
Eylenbosch, Damien ;
Vermeulen, Philippe ;
Mouazen, Abdul M. .
COMPUTERS AND ELECTRONICS IN AGRICULTURE, 2022, 203
[7]   Mycotoxin detection [J].
Anfossi, Laura ;
Giovannoli, Cristina ;
Baggiani, Claudio .
CURRENT OPINION IN BIOTECHNOLOGY, 2016, 37 :120-126
[8]  
Beia IS, 2023, SCI PAP-SER MANAG EC, V23, P85
[9]   Mycotoxins [J].
Bennett, JW ;
Klich, M .
CLINICAL MICROBIOLOGY REVIEWS, 2003, 16 (03) :497-+
[10]   Deep-Learning Approach for Fusarium Head Blight Detection in Wheat Seeds Using Low-Cost Imaging Technology [J].
Bernardes, Rodrigo Cupertino ;
De Medeiros, Andre ;
da Silva, Laercio ;
Cantoni, Leo ;
Martins, Gustavo Ferreira ;
Mastrangelo, Thiago ;
Novikov, Arthur ;
Mastrangelo, Clissia Barboza .
AGRICULTURE-BASEL, 2022, 12 (11)