Predicting PSR filters by transverse relaxation enhancements

Area of Science:

• Nuclear Magnetic Resonance (NMR) Spectroscopy
• Paramagnetic Contrast Agents
• Analytical Chemistry

Background:

• The paramagnetic spin relaxation (PSR) filter is an NMR technique for selectively suppressing signals from specific components in mixtures.
• Its application is limited by the difficulty in predicting the Gd(3+)-complexing ability of mixture components.

Purpose of the Study:

• To identify a reliable predictive tool for the effectiveness of the PSR filter in complex mixtures.
• To demonstrate the robustness of this predictive tool across various NMR experiments and magnetic field strengths.

Main Methods:

• Investigated the relationship between transverse relaxation enhancement (R(2p)) and PSR filter performance.
• Applied 1D and 2D PSR filters to commercial multicomponent samples, including beverages and drugs.
• Evaluated the predictive capability of R(2p) at different magnetic fields and for various nuclei (1H, 13C) and experiments (COSY, HMQC).

Main Results:

• The PSR filter's suppression efficiency is primarily governed by the transverse relaxation enhancement (R(2p)) induced by Gd(3+).
• R(2p) was confirmed as a robust and reliable parameter for predicting suppression in both 1D and 2D PSR filters.
• The predictive power of R(2p) was validated across diverse sample matrices and NMR acquisition parameters.

Conclusions:

• Transverse relaxation enhancement (R(2p)) serves as a dependable metric for predicting the outcome of paramagnetic spin relaxation (PSR) filtering.
• This finding facilitates the broader application of PSR filters in analyzing complex mixtures like pharmaceuticals and food products.
• The study establishes R(2p) as a key parameter for optimizing NMR-based separation and analysis techniques.