PDF(Pubmed)
Abstract：
Mycotoxins, toxic secondary metabolites produced by certain fungi, pose significant threats to global food safety and public health. These compounds can contaminate a variety of crops, leading to economic losses and health risks to both humans and animals. Traditional lab analysis methods for mycotoxin detection can be time-consuming and may not always be suitable for large-scale screenings. However, in recent years, machine learning (ML) methods have gained popularity for use in the detection of mycotoxins and in the food safety industry in general due to their accurate and timely predictions. We provide a systematic review on some of the recent ML applications for detecting/predicting the presence of mycotoxin on a variety of food ingredients, highlighting their advantages, challenges, and potential for future advancements. We address the need for reproducibility and transparency in ML research through open access to data and code. An observation from our findings is the frequent lack of detailed reporting on hyperparameters in many studies and a lack of open source code, which raises concerns about the reproducibility and optimisation of the ML models used. The findings reveal that while the majority of studies predominantly utilised neural networks for mycotoxin detection, there was a notable diversity in the types of neural network architectures employed, with convolutional neural networks being the most popular.
摘要：
霉菌毒素,某些真菌产生的有毒次级代谢产物，对全球食品安全和公众健康构成重大威胁。这些化合物会污染多种作物，对人类和动物造成经济损失和健康风险。用于霉菌毒素检测的传统实验室分析方法可能耗时且可能不总是适合大规模筛选。然而,近年来,机器学习（ML）方法由于其准确及时的预测而在霉菌毒素的检测和一般的食品安全行业中得到了普及。我们对一些最近的ML应用进行了系统评价，用于检测/预测各种食品成分中霉菌毒素的存在，突出他们的优势，挑战,以及未来发展的潜力。我们通过对数据和代码的开放访问来满足ML研究中对可重复性和透明度的需求。从我们的发现中可以看出，在许多研究中经常缺乏对超参数的详细报告，并且缺乏开源代码。这引起了人们对所用ML模型的可重复性和优化性的担忧。研究结果表明，虽然大多数研究主要利用神经网络进行霉菌毒素检测，所采用的神经网络架构的类型有显著的多样性，卷积神经网络是最受欢迎的。
