Signal Deconvolution and Noise Factor Analysis Based on a Combination of Time-Frequency Analysis and Probabilistic...

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Data Science

Background:

• Nuclear magnetic resonance (NMR) spectroscopy provides atomic-resolution data for molecular characterization.
• Analyzing complex NMR data from mixtures is challenging due to noise and signal overlap.
• Essential data-cleansing steps include quality checking, noise reduction, and signal deconvolution.

Purpose of the Study:

• To develop an NMR measurement informatics tool for effective data cleansing.
• To improve signal-to-noise ratio (SNR) and enable better analysis of complex NMR datasets.
• To identify key experimental factors influencing NMR data quality.

Main Methods:

• Developed a novel informatics tool combining short-time Fourier transform (STFT) and probabilistic sparse matrix factorization (PSMF).
• Applied the tool to raw free induction decay (FID) signals of one-dimensional NMR spectra.
• Utilized noise factor analysis to correlate SNR with acquisition parameters.

Main Results:

• The developed signal deconvolution method increased the signal-to-noise ratio (SNR) by approximately tenfold.
• The tool successfully separated signals of macromolecules and small molecules in diffusion-edited spectra based on T2* relaxation time.
• Noise factor analysis revealed significant correlations between SNR and specific experimental acquisition parameters.

Conclusions:

• The NMR informatics tool provides effective data cleansing for complex mixtures.
• The method significantly enhances spectral quality and aids in resolving overlapping signals.
• Understanding the impact of acquisition parameters on SNR is crucial for optimizing NMR experiments.