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

文献类型： 外文期刊

作者： Ma, Fei 1 ; Wang, Ju 1 ; Liu, Changhong 1 ; Lu, Xuzhong 2 ; Chen, Wei 1 ; Chen, Conggui 1 ; Yang, Jianbo 2 ; Zheng, Lei 1 ;

作者机构： 1.Hefei Univ Technol, Sch Biotechnol & Food Engn, Hefei 230009, Anhui, Peoples R China

2.Anhui Acad Agr Sci, Rice Res Inst, Hefei 230031, Peoples R China

3.Hefei Univ Technol, Sch Med Engn, Hefei 230009, Peoples R China

关键词： Multispectral imaging;Sunflower seeds;Quality characteristics

期刊名称：FOOD ANALYTICAL METHODS （ 影响因子：3.366； 五年影响因子：3.07 ）

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收录情况： SCI

摘要： Multispectral imaging in the visible and near-infrared (405-970 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.

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