利用近红外光谱进行玉米杂种优势类群划分的可行性研究(英文)

来源 :光谱学与光谱分析 | 被引量 : 0次 | 上传用户:jianghladros
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准确地划分玉米杂种优势群,可有效地指导种质的改良、杂种优势模式的构建和新品种的选育。目前主要利用系谱法、配合力测定、同工酶和分子标记等进行玉米杂种优势类群的划分,这些划分方法成本高,操作复杂,甚至有的方法还需破坏种子。本文探索了利用近红外光谱技术(NIRS)快速、无损地划分玉米杂种优势群的可行性。使用MPA型傅里叶变换近红外光谱仪漫反射模式采集整粒玉米种子的光谱,用滑动平均窗口平滑、一阶差分导数、矢量归一化对光谱进行预处理。将光谱用主成分分析方法降维到低维空间,使用层次聚类法对A组6个中国的骨干自交系和B组的6个优良自选系共12个材料进行杂种优势群划分,结果将A组6个自交系划分为3类,A1(郑58)和A2(掖478)为一类,A3(昌7-2)和A4(黄早四)为一类,A5(Mo17)和A6(四F1)为一类,这与系谱法分析结果高度吻合。对B组6个自选系,将B1和B2,B3和B4,B5和B6分别划分为一类,与SSR标记的类群划分结果一致。由此可见,利用NIRS划分玉米杂种优势类群是简便、快速、高效、可行的方法。 The accurate classification of maize heterotic groups can effectively guide the improvement of germplasm, the construction of heterosis patterns and the breeding of new varieties. At present, the method of genealogy, determination of combining ability, isozymes and molecular markers are used to classify the maize heterotic groups. These methods are costly, complicated to be operated and even destroy the seeds. This article explored the feasibility of rapidly and non-destructively partitioning maize heterotic groups using near infrared spectroscopy (NIRS). The spectrum of whole corn seeds was collected by diffuse reflectance mode of MPA Fourier transform near infrared spectrometer. The spectra were preprocessed by smoothing with sliding averaging window, first order difference derivative and vector normalization. The spectra were reduced to the low-dimensional space by principal component analysis. Hierarchical clustering method was used to classify the heterotic groups of 12 materials from 6 Chinese elite inbred lines in group A and 6 elite self-selected strains in group B, and the results The six inbred lines of A group were divided into three categories: A1 (Zheng 58) and A2 (Yin 478), A3 (Chang 7-2) and A4 (Huangzao Si) And A6 (F1), which are in good agreement with the results of pedigree analysis. For the six groups of B, the B1 and B2, B3 and B4, B5 and B6 were divided into one group, which was consistent with the SSR marker grouping. Thus, the use of NIRS to classify maize heterotic groups is a simple, rapid, efficient and feasible method.
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