Spectral quantitation by principal component analysis using complex singular value decomposition

Principal component analysis (PCA) is a powerful method for quantitative an alysis of nuclear magnetic resonance spectral data sets. It has the advanta ge of being model independent, making it well suited for the analysis of sp ectra with complicated or unknown line shapes. Previous applications of PCA have required that all spectra in a data set be in phase or have implement ed iterative methods to analyze spectra that are not perfectly phased. Howe ver, improper phasing or imperfect convergence of the iterative methods has resulted in systematic errors in the estimation of peak areas with PCA. Pr esented here is a modified method of PCA, which utilizes complex singular v alue decomposition (SVD) to analyze spectral data sets with any amount of: variation in spectral phase. The new method is shown to be completely insen sitive to spectral phase. In the presence of noise, PCA with complex SVD yi elds a lower variation in the estimation of peak area than conventional PCA by a factor of approximately,root 2. The performance of the method is demo nstrated with simulated data and in vivo P-31 spectra from human skeletal m uscle. Magn Reson Med 41:450-455, 1999. (C) 1999 Wiley-Liss, Inc.