15 Nov 2022
15 Nov 2022
Status: this preprint is currently under review for the journal MR.

DEEP Picker1D and Voigt Fitter1D: A versatile tool set for the automated quantitative spectral deconvolution of complex 1D NMR spectra

Da-Wei Li1, Lei Bruschweiler-Li1, Alexandar L. Hansen1, and Rafael Brüschweiler1,2,3 Da-Wei Li et al.
  • 1Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, USA
  • 2Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
  • 3Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210, USA

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 challenging. 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.

Da-Wei Li et al.

Status: open (until 18 Dec 2022)

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Da-Wei Li et al.

Da-Wei Li et al.


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Short summary
Recent advances in machine learning have opened new opportunities toward the automated analysis and spectral reconstruction of highly complex NMR spectra, including ones encountered in metabolomics. We demonstrate the combined power of the deep neural network “DEEP Picker 1D” and “Voigt Fitter1D” software for the quantitative streamlined analysis of 1D 1H NMR spectra extending the reach of a wide range of NMR applications.