Zhou Zixuan, Tian Daoming, Yang Yingao, Cui Han, Li Yanchun, Ren Shuyue, Han Tie, Gao Zhixian
Tianjin Key Laboratory of Risk Assessment and Control Technology for Environment and Food Safety, Tianjin Institute of Environmental and Operational Medicine, Tianjin, 300050, China.
Beidaihe Rest and Recuperation Center of PLA, Qinhuangdao, 066000, China.
Curr Res Food Sci. 2024 Jan 12;8:100679. doi: 10.1016/j.crfs.2024.100679. eCollection 2024.
Recently, the application of biosensors in food safety assessment has gained considerable research attention. Nevertheless, the evaluation of biosensors' sensitivity, accuracy, and efficiency is still ongoing. The advent of machine learning has enhanced the application of biosensors in food security assessment, yielding improved results. Machine learning has been preliminarily applied in combination with different biosensors in food safety assessment, with positive results. This review offers a comprehensive summary of the diverse machine learning methods employed in biosensors for food safety. Initially, the primary machine learning methods were outlined, and the integrated application of biosensors and machine learning in food safety was thoroughly examined. Lastly, the challenges and limitations of machine learning and biosensors in the realm of food safety were underscored, and potential solutions were explored. The review's findings demonstrated that algorithms grounded in machine learning can aid in the early detection of food safety issues. Furthermore, preliminary research suggests that biosensors could be optimized through machine learning for real-time, multifaceted analyses of food safety variables and their interactions. The potential of machine learning and biosensors in real-time monitoring of food quality has been discussed.
近年来，生物传感器在食品安全评估中的应用受到了广泛的研究关注。然而，对生物传感器的灵敏度、准确性和效率的评估仍在进行中。机器学习的出现增强了生物传感器在食品安全评估中的应用，取得了更好的结果。机器学习已初步与不同的生物传感器结合应用于食品安全评估，效果良好。本综述全面总结了用于食品安全的生物传感器中所采用的各种机器学习方法。首先，概述了主要的机器学习方法，并深入研究了生物传感器与机器学习在食品安全中的综合应用。最后，强调了机器学习和生物传感器在食品安全领域的挑战和局限性，并探索了潜在的解决方案。综述结果表明，基于机器学习的算法有助于早期发现食品安全问题。此外，初步研究表明，通过机器学习可以优化生物传感器，以对食品安全变量及其相互作用进行实时、多方面的分析。还讨论了机器学习和生物传感器在食品质量实时监测中的潜力。
