Spectral fitting of NMR spectra using an alternating optimization method with a priori knowledge

As alternatives to the fast Fourier transform, advanced parametric methods based on the damped sinusoidal data model have been devised to better quant ify the nuclear magnetic resonance (NMR) spectroscopy time-domain data. Pre viously, linear prediction (LP) fitting methods using Householder triangula rization and singular value decomposition (SVD) techniques have been applie d to the NMR spectroscopy data analysis. In this paper, we propose an alter nating optimization method to quantify the time-domain NMR spectroscopy dat a. The proposed algorithm uses the a priori knowledge of the possible frequ ency intervals of the damped sinusoids to obtain more accurate parameter es timates when the NMR spectroscopy data are obtained under low signal-to-noi se ratio conditions and the peaks are close together. None of the LP and SV D type of methods can use such approximate a priori knowledge, We have show n with measured NMR spectroscopy data that the proposed algorithm can be us ed to obtain accurate parameter estimates of frequencies, amplitudes, and d amping ratios of the damped sinusoids and therefore the ultimate fit of the spectrum by using the a priori knowledge about the possible frequency inte rvals of the damped sinusoids, (C) 1999 Academic Press.