Melon Seeds Variety Identification Based on Chlorophyll Fluorescence Spectrum and Reflectance Spectrum

文献类型： 外文期刊

第一作者： Li Cui-ling

作者： Li Cui-ling;Jiang Kai;Feng Qing-chu;Wang Xin;Meng Zhi-jun;Wang Song-lin;Gao Yuan-yuan;Li Cui-ling;Jiang Kai;Feng Qing-chu;Wang Xin;Wang Song-lin;Gao Yuan-yuan

作者机构：

关键词： Seed;Chlorophyll;Fluorescence spectrum;Reflectance spectrum;Variety identification

期刊名称：SPECTROSCOPY AND SPECTRAL ANALYSIS （ 影响因子：0.589； 五年影响因子：0.504 ）

ISSN： 1000-0593

年卷期： 2018 年 38 卷 1 期

页码：

收录情况： SCI

摘要： Melon is popular with us for its high nutritional value, and there are many varieties of melons. Impurity of melon seed variety will cause harm to melon production. This research adopted chlorophyll fluorescence spectrum combined with reflectance spectrum to identify melon seeds variety. Seeds whose varieties were "Yi Te Bai", "Yi Te Jin", "Jing Mi No. 7", "Jing Mi No. 11", "Yi Li Sha Bai" were used as research samples. A melon seeds variety identification system based on spectrum technology was developed, and it included an excitation light source unit, a spectral data acquisition unit and a data processing unit. This system was used to obtain fluorescence spectrums and reflectance spectrums of different varieties of melon seeds. First derivative (FD), Savitzky-Golay (SG), and FD associated with SG were utilized to preprocess spectral data respectively. Principal component analysis (PCA) method was adopted to reduce the dimensions of spectral data and extract principal components. This study adopted two different grouping methods to divided samples into training set and validation set according to the proportion of 3 1, and Fisher discriminant analysis and Bayes discriminant analysis methods were used to establish discriminant models of melon seeds variety respectively. This study compared the discriminant effect of the model developed only using chlorophyll fluorescence spectral information with the discriminant effect of model developed based on chlorophyll fluorescence spectral information combined with reflectance spectral information. Results showed that discriminant model developed using chlorophyll fluorescence spectral information combined with reflectance spectral information generated better determination results than only using chlorophyll fluorescence spectral information, and the discriminant accuracies of validation set reached 98% in both Fisher discrimination analysis and Bayes discriminant analysis. Research results showed that chlorophyll fluorescence spectrum combined with reflectance spectrum technique was feasible for melon seeds variety identification.

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