Discrimination of Kernel Quality Characteristics for Sunflower Seeds Based on Multispectral Imaging Approach

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• 作者：Fei Ma ; Ju Wang ; Changhong Liu ; Xuzhong Lu ; Wei Chen…
• 关键词：Multispectral imaging ; Sunflower seeds ; Quality characteristics
• 刊名：Food Analytical Methods
• 出版年：2015
• 出版时间：August 2015
• 年：2015
• 卷：8
• 期：7
• 页码：1629-1636
• 全文大小：682 KB
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• 作者单位：Fei Ma (1)
  Ju Wang (1)
  Changhong Liu (1)
  Xuzhong Lu (2)
  Wei Chen (1)
  Conggui Chen (1)
  Jianbo Yang (2)
  Lei Zheng (1) (3)
  
  1. School of Biotechnology and Food Engineering, Hefei University of Technology, Hefei, 230009, Anhui province, People’s Republic of China
  2. Rice Research Institute, Anhui Academy of Agricultural Sciences, Hefei, 230031, China
  3. School of Medical Engineering, Hefei University of Technology, Hefei, 230009, China
  
• 刊物类别：Chemistry and Materials Science
• 刊物主题：Chemistry
  Food Science
  Chemistry
  Microbiology
  Analytical Chemistry
  
• 出版者：Springer New York
• ISSN：1936-976X

文摘

Multispectral imaging in the visible and near-infrared (405-70?nm) regions was tested for nondestructive discrimination of insect-infested, moldy, heterochromatic, and rancidity in sunflower seeds. An excellent classification (accuracy >97?%) for intact sunflower seeds could be achieved using Fisher’s linear discriminant function based on 10 feature wavelengths that were selected from the original 19 wavelengths by Wilks-lambda stepwise method. Intact sunflower seeds with different degree of rancidity could be precisely clustered by multispectral imaging technology combined with principal component analysis-cluster analysis (PCA-CA). Our results demonstrate the capability of multispectral imaging technology as a tool for rapid and nondestructive analysis of seed quality attributes, which enables many applications in the agriculture and food industry.