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

IF 4.7 2区 农林科学 Q1 FOOD SCIENCE & TECHNOLOGY
Foods Pub Date : 2024-10-21 DOI:10.3390/foods13203339
Ashish Aggarwal, Akanksha Mishra, Nazia Tabassum, Young-Mog Kim, Fazlurrahman Khan
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引用次数: 0

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.

利用人工智能检测食品中的霉菌毒素污染:综述。
食品中的霉菌毒素污染是全世界食品安全和公众健康的一个主要问题。人类使用的农产品受到霉菌毒素(真菌的有毒次级代谢产物)的污染,对人类健康构成重大威胁。霉菌毒素检测的常用方法包括色谱分离法,通常与质谱法相结合(准确,但制备样品耗时,需要熟练的技术人员)。人工智能(AI)作为一种新技术已被引入食品中霉菌毒素的检测中,具有很高的可信度和准确性。这篇综述文章概述了近期有关使用人工智能方法发现食品中霉菌毒素的研究。新方法表明,多种人工智能技术可以相互关联。深度学习模型、机器学习算法和神经网络被用于分析来自不同分析平台的精细数据集。此外,本综述重点关注人工智能与智能传感技术或其他非常规技术(如光谱学、生物传感器和成像技术)协同工作的进展,以实现快速、低破坏性的霉菌毒素检测。我们对训练人工智能模型所需的大型、多样化数据集提出了质疑,讨论了分析方法的标准化问题,并讨论了基于人工智能的方法获得监管部门批准的途径,以及该领域的其他热点问题。此外,本研究还提供了一些有趣的用例和实际商业应用,在这些用例和应用中,人工智能在灵敏度、特异性和所需时间方面都优于其他传统方法。本综述旨在通过纳入最新研究成果,强调食品科学家、工程师和计算机科学家之间多学科合作的必要性,为人工智能霉菌毒素检测的未来发展方向提供见解。最终,人工智能的使用将彻底改变霉菌毒素监测系统,改善食品安全,保障全球公众健康。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Foods
Foods Immunology and Microbiology-Microbiology
CiteScore
7.40
自引率
15.40%
发文量
3516
审稿时长
15.83 days
期刊介绍: Foods (ISSN 2304-8158) is an international, peer-reviewed scientific open access journal which provides an advanced forum for studies related to all aspects of food research. It publishes reviews, regular research papers and short communications. Our aim is to encourage scientists, researchers, and other food professionals to publish their experimental and theoretical results in as much detail as possible or share their knowledge with as much readers unlimitedly as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. There are, in addition, unique features of this journal: Ÿ manuscripts regarding research proposals and research ideas will be particularly welcomed Ÿ electronic files or software regarding the full details of the calculation and experimental procedure, if unable to be published in a normal way, can be deposited as supplementary material Ÿ we also accept manuscripts communicating to a broader audience with regard to research projects financed with public funds
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