Discrimination of Maize Haploid Seeds from Hybrid Seeds Using Vis Spectroscopy and Support Vector Machine Method
LIU Jin1, GUO Ting-ting1, LI Hao-chuan2, JIA Shi-qiang3, YAN Yan-lu3, AN Dong3, ZHANG Yao1, CHEN Shao-jiang1*
1. National Maize Improvement Center of China, China Agricultural University, Beijing 100193, China 2. Agronomy College, Henan Agricultural University, Zhengzhou 450002, China 3. College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Abstract：Doubled haploid (DH) lines are routinely applied in the hybrid maize breeding programs of many institutes and companies for their advantages of complete homozygosity and short breeding cycle length. A key issue in this approach is an efficient screening system to identify haploid kernels from the hybrid kernels crossed with the inducer. At present, haploid kernel selection is carried out manually using the“red-crown” kernel trait (the haploid kernel has a non-pigmented embryo and pigmented endosperm) controlled by the R1-nj gene. Manual selection is time-consuming and unreliable. Furthermore, the color of the kernel embryo is concealed by the pericarp. Here, we establish a novel approach for identifying maize haploid kernels based on visible (Vis) spectroscopy and support vector machine (SVM) pattern recognition technology. The diffuse transmittance spectra of individual kernels (141 haploid kernels and 141 hybrid kernels from 9 genotypes) were collected using a portable UV-Vis spectrometer and integrating sphere. The raw spectral data were preprocessed using smoothing and vector normalization methods. The desired feature wavelengths were selected based on the results of the Kolmogorov-Smirnov test. The wavelengths with p values above 0.05 were eliminated because the distributions of absorbance data in these wavelengths show no significant difference between haploid and hybrid kernels. Principal component analysis was then performed to reduce the number of variables. The SVM model was evaluated by 9-fold cross-validation. In each round, samples of one genotype were used as the testing set, while those of other genotypes were used as the training set. The mean rate of correct discrimination was 92.06%. This result demonstrates the feasibility of using Vis spectroscopy to identify haploid maize kernels. The method would help develop a rapid and accurate automated screening-system for haploid kernels.
刘 金1，郭婷婷1，李浩川2，贾仕强3，严衍禄3，安 冬3，张 垚1，陈绍江1* . 基于可见光光谱高效鉴别玉米单倍体籽粒 [J]. 光谱学与光谱分析, 2015, 35(11): 3268-3274.
LIU Jin1, GUO Ting-ting1, LI Hao-chuan2, JIA Shi-qiang3, YAN Yan-lu3, AN Dong3, ZHANG Yao1, CHEN Shao-jiang1*. Discrimination of Maize Haploid Seeds from Hybrid Seeds Using Vis Spectroscopy and Support Vector Machine Method. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2015, 35(11): 3268-3274.
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