Worley Bradley, Powers Robert
Department of Chemistry, University of Nebraska-Lincoln, Lincoln, NE 68588-0304.
Chemometr Intell Lab Syst. 2014 Feb 15;131:1-6. doi: 10.1016/j.chemolab.2013.11.005.
Nuclear magnetic resonance (NMR) spectroscopy has proven invaluable in the diverse field of chemometrics due to its ability to deliver information-rich spectral datasets of complex mixtures for analysis by techniques such as principal component analysis (PCA). However, NMR datasets present a unique challenge during preprocessing due to differences in phase offsets between individual spectra, thus complicating the correction of random dilution factors that may also occur. We show that simultaneously correcting phase and dilution errors in NMR datasets representative of metabolomics data yields improved cluster quality in PCA scores space, even with significant initial phase errors in the data.
由于核磁共振(NMR)光谱能够提供复杂混合物的信息丰富的光谱数据集,以便通过主成分分析(PCA)等技术进行分析,因此在化学计量学的不同领域已证明具有极高价值。然而,由于各个光谱之间相位偏移的差异,NMR数据集在预处理过程中面临独特挑战,这也使得可能出现的随机稀释因子的校正变得复杂。我们表明,在代表代谢组学数据的NMR数据集中同时校正相位和稀释误差,即使数据中存在显著的初始相位误差,也能在PCA得分空间中提高聚类质量。