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Spectral identification of melon seeds variety based on k-nearest neighbor and Fisher discriminant analysis

文献类型： 外文期刊

作者： Li, Cuiling 1 ; Jiang, Kai 1 ; Zhao, Xueguan 1 ; Fan, Pengfei 1 ; Wang, Xiu 1 ; Liu, Chuan 1 ;

作者机构： 1.Beijing Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

2.Natl Res Ctr Intelligent Equipment Agr, Beijing 100097, Peoples R China

关键词： Melon seed;variety identification;K-nearest neighbour;Fisher discriminant analysis

期刊名称：AOPC 2017: OPTICAL SPECTROSCOPY AND IMAGING

ISSN： 0277-786X

年卷期： 2017 年 10461 卷

页码：

收录情况： SCI

摘要： Impurity of melon seeds variety will cause reductions of melon production and economic benefits of farmers, this research aimed to adopt spectral technology combined with chemometrics methods to identify melon seeds variety. Melon seeds whose varieties were "Yi Te Bai", "Yi Te Jin", "Jing Mi NO.7", "Jing Mi NO.11" and "Yi Li Sha Bai "were used as research samples. A simple spectral system was developed to collect reflective spectral data of melon seeds, including a light source unit, a spectral data acquisition unit and a data processing unit, the detection wavelength range of this system was 200-1100nm with spectral resolution of 0.14 similar to 7.7nm. The original reflective spectral data was pre-treated with de-trend (DT), multiple scattering correction (MSC), first derivative (FD), normalization (NOR) and Savitzky-Golay (SG) convolution smoothing methods. Principal Component Analysis (PCA) method was adopted to reduce the dimensions of reflective spectral data and extract principal components. K-nearest neighbour (KNN) and Fisher discriminant analysis (FDA) methods were used to develop discriminant models of melon seeds variety based on PCA. Spectral data pretreatments improved the discriminant effects of KNN and FDA, FDA generated better discriminant results than KNN, both KNN and FDA methods produced discriminant accuracies reaching to 90.0% for validation set. Research results showed that using spectral technology in combination with KNN and FDA modelling methods to identify melon seeds variety was feasible.

  • 相关文献

[1]MELON SEED VARIETY IDENTIFICATION BASED ON HYPERSPECTRAL TECHNOLOGY COMBINED WITH DISCRIMINANT ANALYSIS. Li, Cuiling,Fan, Pengfei,Jiang, Kai,Wang, Xiu,Feng, Qingchun,Zhang, Chunfeng,Li, Cuiling,Fan, Pengfei,Jiang, Kai,Wang, Xiu,Feng, Qingchun,Zhang, Chunfeng. 2017

[2]Validation of a set of informative simple sequence repeats markers for variety identification in Pak-choi (Brassica rapa L. ssp chinensis var. communis). Su, Tongbing,Zhao, Xiuyun,Sui, Guanglei,Yu, Shuancang,Zhang, Fenglan,Wang, Weihong,Zhang, Deshuang,Yu, Yangjun,Su, Tongbing,Zhao, Xiuyun,Sui, Guanglei,Yu, Shuancang,Zhang, Fenglan,Wang, Weihong,Zhang, Deshuang,Yu, Yangjun,Su, Tongbing,Zhao, Xiuyun,Sui, Guanglei,Yu, Shuancang,Zhang, Fenglan,Wang, Weihong,Zhang, Deshuang,Yu, Yangjun.

[3]Melon Seeds Variety Identification Based on Chlorophyll Fluorescence Spectrum and Reflectance Spectrum. 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. 2018

[4]Development of maizeSNP3072, a high-throughput compatible SNP array, for DNA fingerprinting identification of Chinese maize varieties. Tian, Hong-Li,Wang, Feng-Ge,Zhao, Jiu-Ran,Yi, Hong-Mei,Wang, Lu,Wang, Rui,Yang, Yang,Song, Wei.

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