Bayesian approach for automated quantitative analysis of benchtop NMR data

Area of Science:

• Analytical Chemistry
• Spectroscopy
• Computational Chemistry

Background:

• Benchtop Nuclear Magnetic Resonance (NMR) instruments offer cost-effectiveness but often suffer from poor spectral resolution, hindering accurate quantification.
• Traditional NMR data processing requires significant user expertise, limiting the widespread adoption of benchtop NMR in routine industrial applications.
• Existing methods struggle with the complex spectral data generated by lower-field instruments, necessitating advanced post-acquisition processing.

Purpose of the Study:

• To develop a fully automated algorithmic approach for analyzing Nuclear Magnetic Resonance (NMR) spectra from benchtop instruments.
• To enhance the accuracy and reliability of quantification for industrial applications like process monitoring and quality control using benchtop NMR.
• To establish benchtop NMR as a viable and effective alternative to traditional high-field NMR by overcoming spectral resolution limitations.

Main Methods:

• Parametric modeling formulated using Bayesian statistics to incorporate prior knowledge of chemical systems.
• Development of an algorithm for automated routine analysis of sample sets with similar compositions.
• Utilization of quantum mechanical models for chemical species to ensure spectrometer field strength invariance.

Main Results:

• Achieved average concentration errors of 0.01 mol/mol for alcohol and acetate mixtures.
• Demonstrated average concentration errors of 0.02 mol/mol for aqueous mixtures of biologically relevant species.
• The automated method proved competitive with traditional processing of high-field NMR data.

Conclusions:

• The proposed automated, Bayesian statistical approach significantly enhances the quantification accuracy of benchtop NMR data.
• This method overcomes spectral dispersion limitations, making benchtop NMR suitable for industrial quality control and process monitoring.
• The field-invariant algorithm broadens the applicability of NMR technology to smaller laboratories and routine analyses.