Girmatsion Mogos, Tang Xiaoqian, Zhang Qi, Li Peiwu
Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China; Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs; Laboratory of Risk Assessment for Oilseed Products (Wuhan), Ministry of Agriculture and Rural Affairs; Quality Inspection and Test Center for Oilseed Products, Ministry of Agriculture and Rural Affairs, China; Hamelmalo Agricultural College, Department of Food Science, Keren, Eritrea.
Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan 430062, China; Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs; Laboratory of Risk Assessment for Oilseed Products (Wuhan), Ministry of Agriculture and Rural Affairs; Quality Inspection and Test Center for Oilseed Products, Ministry of Agriculture and Rural Affairs, China; Food Safety Research Institute, Hubei University, Wuhan 430062, China; Hubei Hongshan Laboratory, Wuhan 430070, China.
Food Res Int. 2025 May;209:116285. doi: 10.1016/j.foodres.2025.116285. Epub 2025 Mar 17.
The growing concern over food safety, driven by threats such as food contaminations and adulterations has prompted the adoption of advanced technologies like electronic nose (e-nose) and hyperspectral imaging (HSI), which are increasingly enhanced by machine learning innovations. This paper aims to provide a comprehensive review on food safety, by combining insights from both e-nose and HSI technologies alongside machine learning algorithms. First, the basic principles of e-nose, HSI, and machine learning, with particular emphasis on artificial neural network (ANN) and deep learning (DL) are briefly discussed. The review then examines how machine learning enhances the performance of e-nose and HSI, followed by an exploration of recent applications in detecting food hazards, including drug residues, microbial contaminants, pesticide residues, toxins, and adulterants. Subsequently, key limitations encountered in the applications of machine learning, e-nose and HSI, along with future perspectives on the potential advancements of these technologies are highlighted. E-nose and HSI technologies have shown their great potential for applications in food safety assessment through machine learning assistance. Despite this, their use is primarily limited to laboratory environments, restricting their real-world applications. Additionally, the lack of standardized protocols hampers their acceptance and the reproducibility of tests in food safety assessments. Thus, further research is essential to address these limitations and enhance the effectiveness of e-nose and HSI technologies in practical applications. Ultimately, this paper offers a detailed understanding of both technologies, highlighting the pivotal role of machine learning and presenting insights into their innovative applications within food safety evaluation.
由食品污染和掺假等威胁引发的对食品安全的日益关注，促使人们采用了电子鼻（e-nose）和高光谱成像（HSI）等先进技术，而机器学习创新也在不断提升这些技术。本文旨在通过结合电子鼻和高光谱成像技术以及机器学习算法的见解，对食品安全进行全面综述。首先，简要讨论了电子鼻、高光谱成像和机器学习的基本原理，特别强调了人工神经网络（ANN）和深度学习（DL）。然后，综述考察了机器学习如何提高电子鼻和高光谱成像的性能，接着探讨了它们在检测食品危害方面的最新应用，包括药物残留、微生物污染、农药残留、毒素和掺假物。随后，强调了机器学习、电子鼻和高光谱成像应用中遇到的关键限制，以及这些技术潜在进步的未来展望。电子鼻和高光谱成像技术在机器学习的辅助下，已显示出在食品安全评估中的巨大应用潜力。尽管如此，它们的使用主要局限于实验室环境，限制了其在现实世界中的应用。此外，缺乏标准化协议阻碍了它们在食品安全评估中的接受度和测试的可重复性。因此，进一步的研究对于解决这些限制并提高电子鼻和高光谱成像技术在实际应用中的有效性至关重要。最终，本文详细介绍了这两种技术，突出了机器学习的关键作用，并阐述了它们在食品安全评估中的创新应用。
