SPECTRAL QUANTITATION BY PRINCIPAL COMPONENT ANAL: SINGULAR VALUE DECOMPOSITION

主成分分析（PCA）是一种有效的方法， NMR光谱数据集的定量分析。 它的优点 独立于模型，使其非常适合分析 具有复杂或未知线形的光谱。 先前 PCA的应用要求数据集中的所有光谱都是 同相，或已实施迭代方法来分析光谱 它们不是完美的相位。 然而，不适当的阶段或 迭代方法的不完美收敛导致了 用PCA估计峰面积的系统误差。 一 本文提出了一种改进的主成分分析方法， 奇异值分解（SVD）分析光谱数据集， 光谱相位的任何变化量。 新方法被证明是 对光谱相位完全不敏感。 在噪声存在的情况下， 具有复数SVD的PCA在以下估计中产生较低的变化： 峰面积比常规PCA约大一倍 2. 的 该方法的性能与模拟数据证明， 来自人骨骼肌的体内31P光谱。

Principal-component analysis (PCA) is a powerful method for quantitative analysis of NMR spectral data sets. It has the advantage of being model-independent, making it well suited for the analysis of spectra with complicated or unknown line shapes. Previous applications of PCA have required that all spectra in a data set be in-phase, or have implemented iterative methods to analyze spectra which are not perfectly phased. However, improper phasing or imperfect convergence of the iterative methods have resulted in systematic errors in the estimation of peak areas with PCA. A modified method of PCA is presented here which utilizes complex singular value decomposition (SVD) to analyze spectral data sets with any amount of variation in spectral phase. The new method is shown to completely insensitive to spectral phase. In the presence of noise, PCA with complex SVD yields a lower variation in the estimation of peak area than conventional PCA by a factor of approximately 2. The performance of the method is demonstrated with simulated data and in-vivo 31P spectra from human skeletal muscle.