Poster Presentation 23rd International Society of Magnetic Resonance Conference 2023

Analysis of small molecules is essential to metabolomics, natural products, drug discovery, food technology, and many other areas of interest. Nuclear Magnetic Resonance (NMR) spectroscopy is a powerful method to resolve such mixtures and has been of great interest for a long time for its precision, reproducibility, and efficiency. Current barriers preclude from identifying the constituent molecules in a mixture by NMR as overlapping clusters of resonance lines pose a major challenge in resolving signature frequencies for individual molecules. While homonuclear decoupling techniques produce much simplified pure shift spectra, they often affect sensitivity. Conversion of typical NMR spectra to pure shift spectra by signal processing without a priori knowledge about the coupling patterns is essential for accurate analysis. We developed a super-resolved wavelet packet transform based 1H NMR spectroscopy that can be used in high-throughput studies to reliably decouple individual constituents of small molecule mixtures. Further, we developed a scheme for deploying the method in generating highly resolved WPT NMR spectra and predicting the composition of the corresponding molecular mixtures from their 1H NMR spectra in an automated fashion. The four-step spectral analysis scheme consists of calculating the WPT spectrum, peak matching with a WPT shift NMR library, followed by two optimization steps in producing the predicted molecular composition of a mixture. The robustness of the method was tested on an augmented dataset of 1000 molecular mixtures, each containing 3 to 7 molecules. The method successfully predicted the constituent molecules with a median true positive rate of 1.0 against varying compositions, while a median false positive rate of 0.04 was obtained. The approach can be scaled easily for much larger datasets.