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二维核磁共振谱的非负矩阵分解:在复杂混合物分析中的应用

Non-negative matrix factorization of two-dimensional NMR spectra: application to complex mixture analysis.

作者信息

Snyder David A, Zhang Fengli, Robinette Steven L, Bruschweiler-Li Lei, Brüschweiler Rafael

机构信息

Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306, USA.

出版信息

J Chem Phys. 2008 Feb 7;128(5):052313. doi: 10.1063/1.2816782.

Abstract

A central problem in the emerging field of metabolomics is how to identify the compounds comprising a chemical mixture of biological origin. NMR spectroscopy can greatly assist in this identification process, by means of multi-dimensional correlation spectroscopy, particularly total correlation spectroscopy (TOCSY). This Communication demonstrates how non-negative matrix factorization (NMF) provides an efficient means of data reduction and clustering of TOCSY spectra for the identification of unique traces representing the NMR spectra of individual compounds. The method is applied to a metabolic mixture whose compounds could be unambiguously identified by peak matching of NMF components against the BMRB metabolomics database.

摘要

代谢组学这一新兴领域的一个核心问题是如何识别构成生物来源化学混合物的化合物。核磁共振光谱法借助多维相关光谱法,尤其是全相关光谱法(TOCSY),能够极大地助力这一识别过程。本通讯展示了非负矩阵分解(NMF)如何为TOCSY光谱的数据约简和聚类提供一种有效方法,以识别代表各个化合物核磁共振光谱的独特踪迹。该方法应用于一种代谢混合物,其化合物可通过将NMF组分的峰与BMRB代谢组学数据库进行峰匹配来明确识别。

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