NMR; Singular value decomposition; Harr Wavelet; Peak suppression
Issue Date
2003-07
Publisher
Korean Chemical Society (대한화학회)
Citation
Bulletin of the Korean Chemical Society, v. 24, issue. 7, page. 971-974
Abstract
By utilizing singular value decomposition (SVD)and shift averaged Haar wavelet
transform (WT) with a set of Daubechies wavelet coefficients (1/2, -1/2),
a method that can simultaneously eliminate an unwanted large solvent peak and
noise peaks from NMR data has been was developed. Noise elimination was accomplished
by shift-averaging the time domain NMR data after a large solvent peak was
suppressed by SVD. The algorithms took advantage of the WT, giving the excellent
results for the noise elimination in the Gaussian type NMR spectral lines of
NMR data pretreated with SVD, providing the superb results in the adjustment of
phase and magnitude of the spectrum. SVD and shift averaged Haar wavelet methods
were quantitatively evaluated in terms of threshold values and signal to noise (S/N)
ratio values.