Sinha Roy, A.; Srivastava, M. Unsupervised Analysis of Small Molecule Mixtures by Wavelet-Based Super-Resolved NMR. Molecules2023, 28, 792.
Sinha Roy, A.; Srivastava, M. Unsupervised Analysis of Small Molecule Mixtures by Wavelet-Based Super-Resolved NMR. Molecules 2023, 28, 792.
Sinha Roy, A.; Srivastava, M. Unsupervised Analysis of Small Molecule Mixtures by Wavelet-Based Super-Resolved NMR. Molecules2023, 28, 792.
Sinha Roy, A.; Srivastava, M. Unsupervised Analysis of Small Molecule Mixtures by Wavelet-Based Super-Resolved NMR. Molecules 2023, 28, 792.
Abstract
Resolving small molecule mixtures by nuclear magnetic resonance (NMR) spectroscopy has been of great interest for a long time for its precision, reproducibility and efficiency. However, spectral analyses for such mixtures are often highly challenging due to overlapping resonance lines and limited chemical shift windows. The existing experimental and theoretical methods to produce shift NMR spectra in dealing with the problem have limited applicability owing to sensitivity issues, inconsistency and / or requirement of prior knowledge. Recently, we have resolved the problem by decoupling multiplet structures in NMR spectra by the wavelet packet transform (WPT) technique. In this work, 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 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 the varying compositions, while a median false positive rate of 0.04 was obtained. The approach can be scaled easily for much larger datasets.
Chemistry and Materials Science, Physical Chemistry
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
