Articles | Volume 4, issue 1
Research article
08 Feb 2023
Research article |  | 08 Feb 2023

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

Da-Wei Li, Lei Bruschweiler-Li, Alexandar L. Hansen, and Rafael Brüschweiler


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on mr-2022-21', Anonymous Referee #1, 13 Dec 2022
    • AC1: 'Reply on RC1', Rafael Brüschweiler, 14 Dec 2022
  • RC2: 'Comment on mr-2022-21', Milo Westler, 13 Dec 2022
    • AC2: 'Reply on RC2', Rafael Brüschweiler, 14 Dec 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Rafael Brüschweiler on behalf of the Authors (15 Dec 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (16 Dec 2022) by Nicola Salvi
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 the Voigt Fitter1D software for the quantitative streamlined analysis of 1D 1H NMR spectra, extending the reach of a wide range of NMR applications.