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

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Computational Chemistry

Background:

• Fast Fourier Transform (FFT) is a standard for NMR spectroscopy.
• Parametric methods offer alternatives for time-domain NMR data quantification.
• Linear Prediction (LP) and Singular Value Decomposition (SVD) are established parametric techniques.

Purpose of the Study:

• To develop an advanced parametric method for quantifying time-domain NMR data.
• To improve accuracy in low signal-to-noise ratio (SNR) conditions and for closely spaced peaks.
• To leverage a priori knowledge of frequency intervals for enhanced parameter estimation.

Main Methods:

• Proposed an alternating optimization algorithm for time-domain NMR data.
• Utilized a priori knowledge of damped sinusoid frequency intervals.
• Compared performance against existing LP and SVD methods.

Main Results:

• The alternating optimization method achieved accurate parameter estimates (frequencies, amplitudes, damping ratios).
• Demonstrated superior performance in low SNR and crowded spectral regions.
• Showcased the utility of incorporating approximate frequency interval knowledge.

Conclusions:

• The proposed alternating optimization method provides accurate quantification of NMR time-domain data.
• This method outperforms traditional LP and SVD techniques by utilizing prior frequency information.
• It offers a significant advancement for NMR spectroscopy data analysis, particularly under challenging conditions.