Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite...

Area of Science:

• Analytical Chemistry
• Biochemistry
• Data Science

Background:

• Automatic metabolite profiling in NMR datasets faces challenges due to data variability and low-intensity signals.
• Current lineshape fitting methods often yield suboptimal spectral resolution, limiting software applicability to specific matrices and protocols.

Purpose of the Study:

• To develop an optimized workflow for automatic metabolite profiling in NMR.
• To enhance the accuracy and reliability of metabolite signal parameter analysis by reducing uncertainty.

Main Methods:

• Implementing an iterative analysis and modeling approach for signal parameters.
• Generating narrow and accurate predictions of expected metabolite signal parameters.
• Developing a workflow capable of learning and modeling sample properties.

Main Results:

• Improved metabolite profiling quality indicators were achieved.
• Maximized performance of automatic metabolite profiling.
• Demonstrated overcoming of limitations associated with biological matrices, sample preparation, and lineshape fitting.

Conclusions:

• The proposed workflow enhances automatic metabolite profiling accuracy and reliability.
• This method offers a more versatile solution, overcoming current restrictions in NMR data analysis.
• The approach enables robust metabolite profiling across diverse biological matrices and preparation methods.