Vis/NIR Hyperspectral Imaging for Detection of Hidden Bruises on Kiwifruits

被引:47
作者
Lu, Qiang [1 ,2 ]
Tang, Ming-jie [1 ]
Cai, Jian-rong [1 ]
Zhao, Jie-wen [1 ]
Vittayapadung, Saritporn [3 ]
机构
[1] Jiangsu Univ, Sch Food & Biol Engn, Xuefu Rd 301, Zhenjiang City 212013, Jiangsu, Peoples R China
[2] Henan Univ Technol, Sch Informat Sci & Engn, Zhengzhou 450000, Henan, Peoples R China
[3] Chiang Mai Univ, Dept Mech Engn, FAME Lab, Chiang Mai 50000, Thailand
基金
中国国家自然科学基金;
关键词
Actinidia deliciosa; principal component analysis; support vector machine; GOLDEN-DELICIOUS APPLES; SURFACE-DEFECTS; CLASSIFICATION; INSPECTION; ALGORITHM; IMAGERY; SYSTEM;
D O I
10.17221/69/2010-CJFS
中图分类号
TS2 [食品工业];
学科分类号
100403 [营养与食品卫生学];
摘要
LU Q., TANG M.-J., CAI J.-R., ZHAO J.-W., VITTAYAPADUNG S. (2011): Vis/NIR hyperspectral imaging for detection of hidden bruises on kiwifruits. Czech J. Food Sci., 29: 595-602. It is necessary to develop a non-destructive technique for kiwifruit quality analysis because the machine injury could lower the quality of fruit and incur economic losses. Bruises are not visible externally owing to the special physical properties of kiwifruit peel. We proposed the hyperspectral imaging technique to inspect the hidden bruises on kiwifruit. The Vis/NIR (408-1117 nm) hyperspectral image data was collected. Multiple optimal wavelength (682, 723, 744, 810, and 852 nm) images were obtained using principal component analysis on the high dimension spectral image data (wavelength range from 600 nm to 900 nm). The bruise regions were extracted from the component images of the five waveband images using RBF-SVM classification. The experimental results showed that the error of hidden bruises detection on fruits by means of hyperspectral imaging was 12.5%. It was concluded that the multiple optimal waveband images could be used to constructs a multispectral detection system for hidden bruises on kiwifruits.
引用
收藏
页码:595 / 602
页数:8
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