The use of principal component analysis (PCA) for simultaneous spectral quantification of a single resonant peak across a series of spectra has gained popularity among the NMR community. The approach is fast, requires no assumptions regarding the peak lineshape and provides quantitation even for peaks with very low signal-to-noise ratio. PCA produces estimates of all peak parameters: area, frequency, phase and linewidth. If desired, these estimates can be used to correct the original data so that the peak in all spectra has the same lineshape. This ability makes PCA useful not only for direct peak quantification, but also for processing spectral data prior to application of pattern recognition/classification techniques. This article briefly reviews the theoretical basis of PCA for spectral quantification, addresses issues of data processing prior to PCA, describes suitable and unsuitable datasets for PCA application and summarizes the developments and the limitations of the method.
- Line shape correction
- Peak parameters
- Spectral estimation
ASJC Scopus subject areas
- Radiology Nuclear Medicine and imaging
- Radiological and Ultrasound Technology