食品与发酵工业 >

2024 , Vol. 50 >Issue 24: 353 - 361

DOI: https://doi.org/10.13995/j.cnki.11-1802/ts.039008

综述与专题评论

机器学习和大数据在食品领域的应用

  • 丁浩晗 ,
  • 田嘉伟 ,
  • 谢祯奇 ,
  • 沈嵩 ,
  • 崔晓晖 ,
  • 王震宇
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  • 1(江南大学,未来食品科学中心,江苏 无锡,214000)
    2(江南大学 人工智能与计算机学院,江苏 无锡,214000)
    3(武汉大学 国家网络安全学院,湖北 武汉 430072)
    4(嘉兴未来食品研究院,浙江 嘉兴,314005)
第一作者:博士,讲师(崔晓晖教授为通信作者,E-mail:xcui@whu.edu.cn)

收稿日期: 2024-02-28

  修回日期: 2024-04-25

  网络出版日期: 2024-12-30

基金资助

国家重点研发计划(2022YFF1101100);中央高校基本科研业务费专项资金资助(JUSRP123053);跨境网络空间安全教育部工程研究中心2023年度开放课题(KJAQ202304007)

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Applications of machine learning and big data in food industry

  • DING Haohan ,
  • TIAN Jiawei ,
  • XIE Zhenqi ,
  • SHEN Song ,
  • CUI Xiaohui ,
  • WANG Zhenyu
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  • 1(Science Center for Future Foods, Jiangnan University, Wuxi 214000, China)
    2(School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214000, China)
    3(School of Cyber Science and Engineering, Wuhan University, Wuhan 430072, China)
    4(Jiaxing Institute of Future Food, Jiaxing 314005, China)

Received date: 2024-02-28

  Revised date: 2024-04-25

  Online published: 2024-12-30

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摘要

近年来,食品行业经历了显著变化。全球化、技术进步以及不断变化的消费者需求推动了该行业的持续发展和创新。作为先进的新兴技术,机器学习和大数据在食品行业中发挥着越来越关键的作用。机器学习和大数据在食品领域的应用密切相关。机器学习通过模式识别和数据分析挖掘食品生产和供应链的关键信息,而大数据技术支持大规模数据处理,提供更全面的决策视角。这两者共同推动了食品研究创新和产业发展。利用机器学习对大数据集进行深入分析,可以预测食品趋势、改进生产流程、优化供应链。该文介绍了机器学习和大数据在食品工业的应用现状,涵盖了机器学习和深度学习技术、自适应神经模糊推理技术以及机器学习在食品检测中的多维应用,如机器学习算法与近红外光谱技术结合的食品检测技术、计算机视觉在食品领域的应用,以及机器学习算法和智能传感器结合的实时食品检测技术。分析了大数据面临的技术挑战(数据缺乏真实性、完整性及数据标注困难等),提出了潜在的解决方案(区块链技术及数据增强与预处理技术),并展望了机器学习技术在食品研究领域的未来发展趋势。此外,随着“食品工业4.0”的到来,食品工业正迎来快速发展,智能农业、机器人农业、无人机、3D打印、数字孪生等由机器学习衍生出的前沿技术将进一步崭露头角。面对如此多的创新和改进机遇,食品行业也将面临智能生产和可持续性等问题。最后,讨论了食品行业未来可能面临的挑战和发展方向。

关键词： 机器学习; 大数据; 食品工业; 新兴技术; 智能传感器

本文引用格式

丁浩晗 , 田嘉伟 , 谢祯奇 , 沈嵩 , 崔晓晖 , 王震宇 . 机器学习和大数据在食品领域的应用[J]. 食品与发酵工业, 2024 , 50(24) : 353 -361 . DOI: 10.13995/j.cnki.11-1802/ts.039008

Abstract

In recent years, the food industry has undergone significant changes.Globalization, technological advancements, and evolving consumer demands have driven continuous development and innovation within the sector.As an advanced emerging technology, machine learning and big data play an increasingly critical role in the food industry.The application of machine learning and big data in the food sector is closely intertwined.Machine learning utilizes pattern recognition and data analysis to extract key information from food production and supply chains, while big data technologies support the processing of large-scale datasets, providing a more comprehensive decision-making perspective.Together, these technologies propel innovation in food research and industrial development.In-depth analysis of large datasets using machine learning can predict food trends, improve production processes, and optimize supply chains.This paper presents the current state of application of machine learning and big data in the food industry, covering machine learning and deep learning technologies, adaptive neuro-fuzzy inference systems, and the multidimensional applications of machine learning in food detection, such as food detection techniques that combine machine learning algorithms with near-infrared spectroscopy, applications of computer vision in the food sector, and real-time food detection technologies that integrate machine learning algorithms with smart sensors.It analyzes the technical challenges faced by big data (such as issues with data authenticity, completeness, and difficulties in data labelling), proposes potential solutions (including blockchain technology and data augmentation and preprocessing techniques), and anticipates future development trends of machine learning technologies in food research.Furthermore, with the advent of “Food Industry 4.0,” the food sector is poised for rapid growth, and cutting-edge technologies derived from machine learning—such as smart agriculture, robotic farming, drones, 3D printing, and digital twins—will further emerge.In light of these numerous opportunities for innovation and improvement, the food industry will also need to address challenges related to intelligent production and sustainability.Finally, the paper discusses potential challenges and future directions for development within the food industry.

Key words： machine learning; big data; food industry; emerging technologies; smart sensors

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