Robust and transferable quantification of NMR spectral quality using IROC analysis

Area of Science:

• Nuclear Magnetic Resonance (NMR) Spectroscopy
• Data Analysis and Signal Processing

Background:

• Non-Fourier methods in NMR spectroscopy excel with nonuniformly-sampled data but introduce nonlinearities.
• These nonlinearities create nonrandom noise, making conventional quality metrics like signal-to-noise ratio unreliable.
• A lack of robust, transferable metrics hinders the comparison of non-Fourier methods and sampling schemes.

Purpose of the Study:

• To introduce a novel method for robustly characterizing spectral quality in NMR spectroscopy.
• To provide a versatile quantitative approach for evaluating nonlinear spectral estimates.
• To facilitate the identification of optimal non-Fourier methods and nonuniform sampling strategies.

Main Methods:

• Development of in situ receiver operating characteristic analysis (IROC) based on Receiver Operating Characteristic curves.
• Utilization of synthetic signals added to empirical data as a "ground truth" for analysis.
• Generation of robust scalar-valued metrics for comprehensive spectral quality assessment.

Main Results:

• IROC effectively addresses challenges posed by nonlinear spectral estimates.
• The method provides reliable scalar metrics for various aspects of spectral quality.
• Demonstrated applications in parameter optimization, method comparison, and sampling scheme optimization.

Conclusions:

• IROC offers a versatile and quantitative solution for spectral quality assessment in NMR.
• This approach overcomes limitations of conventional metrics for nonlinear spectral analysis.
• IROC will accelerate the discovery of optimal experimental designs and analysis techniques for multidimensional NMR.