Enhancing Spectrometer Performance with Unsupervised Machine Learning.

Solid-state NMR spectroscopy (SSNMR) is a powerful technique to probe structural and dynamic properties of biomolecules at an atomic level. Modern SSNMR methods employ multidimensional pulse sequences requiring data collection over a period of days to weeks. Variations in signal intensity or frequency due to environmental fluctuation introduce artifacts into the spectra.

Cross polarization stability in multidimensional NMR spectroscopy of biological solids.

Magic-angle spinning (MAS) solid-state nuclear magnetic resonance (SSNMR) spectroscopy is a powerful and versatile technique for probing structure and dynamics in large, insoluble biological systems at atomic resolution. With many recent advances in instrumentation and polarization methods, technology development in SSNMR remains an active area of research and presents opportunities to further improve data collection, processing, and analysis of samples with low sensitivity and complex tertiary and quaternary structures. SSNMR spectra are often collected as multidimensional data, requiring stable experimental conditions to minimize signal fluctuations (t noise).