基于光学特性的无损检测技术在杨梅品质方面的应用研究进展  

Research Progress in Application of Nondestructive Testing Technology of Chinese Bayberry Quality Based on Optical Characteristics

作　　者：张百刚[1] 孟子轩 张井[2] 蒋巧俊[2] 邹盈[2] 苏凤贤[2,3] ZHANG Baigang;MENG Zixuan;ZHANG Jing;JIANG Qiaojun;ZOU Ying;SU Fengxian(College of Life Science and Engineering,Lanzhou University of Technology,Lanzhou 730050,Gansu,China;College of Agriculture and Biotechnology,Wenzhou Vocational College of Science and Technology,Wenzhou 325006,Zhejiang,China;Key Laboratory of Crop Breeding in South Zhejiang,Wenzhou Academy of Agricultural Sciences,Wenzhou 325006,Zhejiang,China)

机构地区：[1]兰州理工大学生命科学与工程学院,甘肃兰州730050 [2]温州科技职业学院农业与生物技术学院,浙江温州325006 [3]温州市农业科学研究院浙南作物育种重点实验室,浙江温州325006

出　　处：《食品研究与开发》2025年第7期216-224,共9页Food Research and Development

基　　金：温州市基础性公益科研项目(N_(2)0220027);文成县科技计划项目(2021NKY07)。

摘　　要：杨梅因营养丰富和新鲜多汁等优点深受国内外消费者的喜爱,但分级分拣存在人工成本高、标准不一、误差率高、分组速度慢等缺点,机器分级分拣技术能够提高生产效率和保证品质一致性。利用可见/近红外光谱技术、计算机视觉技术及高光谱成像技术等基于光学特性的无损检测方法在杨梅品质检测方面的研究取得一些研究成果。该文基于上述光学特性的杨梅品质无损检测技术进行综述,分析各技术的优缺点,为基于光学特性无损检测技术在杨梅品质评价方面的应用提供参考。Chinese bayberry was popular among domestic and foreign consumers for the rich nutrients,freshness,and juiciness.However,the grading and sorting of Chinese bayberry face shortcomings such as high labor cost,inconsistent standards,high error rate,and slow speed.Machine grading and sorting can improve the efficiency and quality consistency.In recent years,research progress has been achieved in nondestructive testing methods based on optical characteristics,such as visible/near-infrared spectroscopy,computer vision technology,and hyperspectral imaging,in the quality inspection of Chinese bayberry.The nondestructive testing technologies for Chinese bayberry quality based on optical characteristics were reviewed,and their advantages and disadvantages were summarized,with a view to providing a reference for the application of nondestructive testing technologies based on optical characteristics in the evaluation of Chinese bayberry quality.

关 键 词：光学特性 无损检测 杨梅 品质 光谱 成像技术 

分 类 号：S667.6[农业科学—果树学]