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小麦品质的早期预测:利用质谱和多变量数据分析在籽粒发育过程中的分析

Early prediction of wheat quality: analysis during grain development using mass spectrometry and multivariate data analysis.

作者信息

Ghirardo Andrea, Sørensen Helle Aagaard, Petersen Marianne, Jacobsen Susanne, Søndergaard Ib

机构信息

BioCentrum-DTU, Biochemistry and Nutrition Group, Søltofts Plads, Building 224, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark.

出版信息

Rapid Commun Mass Spectrom. 2005;19(4):525-32. doi: 10.1002/rcm.1823.

Abstract

Matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry and multivariate data analysis have been used for the determination of wheat quality at different stages of grain development. Wheat varieties with one of two different end-use qualities (i.e. suitable or not suitable for bread-making purposes) were investigated. The samples were collected from grains from 15 until 45 days post-anthesis (dpa). Gluten proteins from wheat grains were extracted and subsequently analysed by mass spectrometry. Discrimination partial least-squares regression and soft independent modelling of class analogy were used to determine the quality of new and unknown wheat samples. With these methods, we were able to predict correctly the end-use qualities at every stage investigated. This new fast technique, based on the rapidity of mass spectrometry combined with the objectivity of multivariate data analysis, offers a method that can replace the traditional rather time-consuming ones such as gel electrophoresis. This study focused on the determination of wheat quality at 15 dpa, when the grain is due for harvest 1 month later.

摘要

基质辅助激光解吸/电离飞行时间质谱法和多变量数据分析已用于测定小麦籽粒发育不同阶段的品质。研究了具有两种不同最终用途品质(即适合或不适合用于面包制作)之一的小麦品种。样品采集自开花后15至45天(dpa)的籽粒。从小麦籽粒中提取面筋蛋白,随后进行质谱分析。采用判别偏最小二乘回归和类软独立建模来确定新的和未知小麦样品的品质。通过这些方法,我们能够正确预测所研究每个阶段的最终用途品质。这种基于质谱分析的快速性与多变量数据分析的客观性相结合的新的快速技术,提供了一种可以取代传统的耗时方法(如凝胶电泳)的方法。本研究重点关注开花后15天(此时籽粒将在1个月后收获)的小麦品质测定。

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