AI Article Synopsis
- The quantitative deconvolution of 1D-NMR spectra is essential for accurate analysis and interpretation in modern NMR workflows.
- This text discusses the development of a new automated solution using deep neural networks, specifically DEEP Picker and Voigt Fitter, for improving the deconvolution process of complex 1D-NMR spectra.
- The method shows exceptional performance in analyzing overlapping peaks and large dynamic ranges, making it a valuable tool for studying complex molecular systems and mixtures.
Article Abstract
The quantitative deconvolution of 1D-NMR spectra into individual resonances or peaks is a key step in many modern NMR workflows as it critically affects downstream analysis and interpretation. Depending on the complexity of the NMR spectrum, spectral deconvolution can be a notable challenge. Based on the recent deep neural network DEEP Picker and Voigt Fitter for 2D NMR spectral deconvolution, we present here an accurate, fully automated solution for 1D-NMR spectral analysis, including peak picking, fitting, and reconstruction. The method is demonstrated for complex 1D solution NMR spectra showing excellent performance also for spectral regions with multiple strong overlaps and a large dynamic range whose analysis is challenging for current computational methods. The new tool will help streamline 1D-NMR spectral analysis for a wide range of applications and expand their reach toward ever more complex molecular systems and their mixtures.
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|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10539790 | PMC |
| http://dx.doi.org/10.5194/mr-4-19-2023 | DOI Listing |
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