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基于光谱和成像技术的肉类品质无损检测研究进展

  • 王大臣 ,
  • 王子杰 ,
  • 姜雨珊 ,
  • 蒋雪松 ,
  • 姜洪喆 ,
  • 周宏平 ,
  • 王英力
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  • 1(南京林业大学 机械电子工程学院, 江苏 南京, 210037)
    2(华中农业大学 工学院, 湖北 武汉, 430070)
第一作者:博士,讲师(王英力讲师为通信作者,E-mail:yingliwang@mail.hzau.edu.cn)

收稿日期: 2025-08-08

  修回日期: 2025-09-17

  网络出版日期: 2026-06-24

基金资助

江苏省普通高校自然科学研究资助项目(24KJB210014);江苏省自然科学基金项目(BK20240682);中央高校基本科研业务费专项资金资助项目(2662023GXQD001)

Recent advances in nondestructive detection of meat quality based on spectroscopy and imaging techniques

  • WANG Dachen ,
  • WANG Zijie ,
  • JIANG Yushan ,
  • JIANG Xuesong ,
  • JIANG Hongzhe ,
  • ZHOU Hongping ,
  • WANG Yingli
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  • 1(College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China)
    2(College of Engineering, Huazhong Agricultural University, Wuhan 430070, China)

Received date: 2025-08-08

  Revised date: 2025-09-17

  Online published: 2026-06-24

摘要

随着消费者食品质量和安全性意识的提升,肉类品质无损检测成为现代肉类工业发展的必然需求。光谱技术和成像技术能够快速、准确地评估肉类的感官特性、理化特性以及卫生安全指标。该文综述了光谱和成像技术在肉类品质检测中的应用,主要介绍了上述技术的理论基础、发展现状、当前应用和优缺点,并展望了未来的发展趋势。尽管这些技术在实验室环境中取得了显著成果,但其在工业应用中仍面临成本高、复杂环境干扰因素多、数据分析过程复杂等挑战。未来的研究方向可聚焦于融合多种低成本检测技术并结合深度学习算法,以提高检测的全面性和准确性,推动其在肉类工业中的规模化应用。

本文引用格式

王大臣 , 王子杰 , 姜雨珊 , 蒋雪松 , 姜洪喆 , 周宏平 , 王英力 . 基于光谱和成像技术的肉类品质无损检测研究进展[J]. 食品与发酵工业, 2026 , 52(11) : 404 -415 . DOI: 10.13995/j.cnki.11-1802/ts.044173

Abstract

With the enhancement of consumers’ awareness of food quality and safety, nondestructive testing of meat quality has become an inevitable demand for the development of the modern meat industry.Spectroscopy and imaging technologies can rapidly and accurately assess the sensory characteristics, physicochemical properties, and hygiene and safety indicators of meat.This paper reviews the application of spectroscopy and imaging technologies in meat quality inspection, mainly introducing the theoretical basis, development status, current applications, advantages, and disadvantages of these technologies, and looks forward to the future development trends.Although these technologies have achieved remarkable results in laboratory environments, they still face challenges, including high costs, numerous interference factors in complex environments, and complex data analysis processes in industrial applications.In the future, multiple low-cost techniques can be integrated and combined with deep learning algorithms to improve the comprehensiveness and accuracy of detection, promoting their practical application in the meat industry.

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