A Novel Select Method Based on the Sparseness as Acquiring Appropriate Data for the Data Interpolation of Accelerated Nuclear Magnetic Resonance Spectroscopy

Accelerated nuclear magnetic resonance spectroscopy (A-NMRS) is one of a molecular analysis method which requires an interpolation process to a measurement data. In general, the condition for the data acquisition has been determined beforehand. However, with the principle of A-NMRS, the condition can be changed both before and during the data acquisition. This study proposes a method to improve interpolation performance by adjusting the data acquisition condition through real-time data analysis. Based on the conventional knowledge, the interpolation method for A-NMRS was based on a sparse optimization. The current study has proposed a method to identify the conditions that allow us to measure the data with the lowest sparseness. To demonstrate an advantage of the proposed method, this paper experiments to compare the interpolation performance of the conventional methodology and the proposed methodology.