Articles | Volume 4, issue 1
https://doi.org/10.5194/mr-4-19-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/mr-4-19-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
08 Feb 2023
Research article |
| 08 Feb 2023
| 08 Feb 2023
DEEP Picker1D and Voigt Fitter1D: a versatile tool set for the automated quantitative spectral deconvolution of complex 1D-NMR spectra
Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, USA
Lei Bruschweiler-Li
Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, USA
Alexandar L. Hansen
Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, USA
Rafael Brüschweiler
CORRESPONDING AUTHOR
Campus Chemical Instrument Center, The Ohio State University, Columbus, Ohio 43210, USA
Department of Chemistry and Biochemistry, The Ohio State University, Columbus, Ohio 43210, USA
Department of Biological Chemistry and Pharmacology, The Ohio State University, Columbus, Ohio 43210, USA
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Cited
15 citations as recorded by crossref.
- Analysis of chi angle distributions in free amino acids via multiplet fitting of proton scalar couplings N. Syed et al. 10.5194/mr-5-103-2024
- Reverse Curve Fitting Approach for Quantitative Deconvolution of Closely Overlapping Triplets in Fourier Transform Nuclear Magnetic Resonance Spectroscopy Using Odd-Order Derivatives S. Chen et al. 10.3390/magnetochemistry11060050
- NMR metabolite quantification of a synthetic urine sample: an inter-laboratory comparison of processing workflows C. Canlet et al. 10.1007/s11306-023-02028-4
- Multispectral investigation of natural resins L. Heidrich et al. 10.1002/ardp.202400517
- Quantitative NMR spectroscopy of complex mixtures P. Giraudeau 10.1039/D3CC01455J
- Automatic spectral fitting for LIBS and Raman spectra by boosted deconvolution method M. Meneses-Nava 10.1016/j.chemolab.2025.105334
- Deep learning and its applications in nuclear magnetic resonance spectroscopy Y. Luo et al. 10.1016/j.pnmrs.2024.101556
- COLMAR1d: A Web Server for Automated, Quantitative One-Dimensional Nuclear Magnetic Resonance-Based Metabolomics at Arbitrary Magnetic Fields D. Li et al. 10.1021/acs.analchem.4c02688
- COLMAR1d2d: Synergistic Combination of 1D with 2D NMR for Enhanced High-Throughput Identification and Quantification of Metabolites in Complex Mixtures R. Cabrera Allpas et al. 10.1021/acs.analchem.5c00957
- Detection of intramolecular protein dynamics on nanosecond-to-microsecond timescales by nanoparticle-assisted NMR spin relaxation (NASR) X. Xiang et al. 10.1038/s41596-025-01177-1
- Exploring microbial natural products through NMR-based metabolomics D. Wang et al. 10.1039/D4NP00065J
- CloudBrain-NMR: An Intelligent Cloud-Computing Platform for NMR Spectroscopy Processing, Reconstruction, and Analysis D. Guo et al. 10.1109/TIM.2024.3415787
- ShimNet: A Neural Network for Postacquisition Improvement of NMR Spectra Distorted by Magnetic-Field Inhomogeneity S. Jopa et al. 10.1021/acs.jpcb.5c02632
- Assessment of the purity of pyributicarb with signals partially overlapping with a structural isomer impurity in NMR spectrum C. Okamoto et al. 10.1007/s44211-025-00817-6
- NMR-Onion - a transparent multi-model based 1D NMR deconvolution algorithm M. Brinks Sørensen et al. 10.1016/j.heliyon.2024.e36998
15 citations as recorded by crossref.
- Analysis of chi angle distributions in free amino acids via multiplet fitting of proton scalar couplings N. Syed et al. 10.5194/mr-5-103-2024
- Reverse Curve Fitting Approach for Quantitative Deconvolution of Closely Overlapping Triplets in Fourier Transform Nuclear Magnetic Resonance Spectroscopy Using Odd-Order Derivatives S. Chen et al. 10.3390/magnetochemistry11060050
- NMR metabolite quantification of a synthetic urine sample: an inter-laboratory comparison of processing workflows C. Canlet et al. 10.1007/s11306-023-02028-4
- Multispectral investigation of natural resins L. Heidrich et al. 10.1002/ardp.202400517
- Quantitative NMR spectroscopy of complex mixtures P. Giraudeau 10.1039/D3CC01455J
- Automatic spectral fitting for LIBS and Raman spectra by boosted deconvolution method M. Meneses-Nava 10.1016/j.chemolab.2025.105334
- Deep learning and its applications in nuclear magnetic resonance spectroscopy Y. Luo et al. 10.1016/j.pnmrs.2024.101556
- COLMAR1d: A Web Server for Automated, Quantitative One-Dimensional Nuclear Magnetic Resonance-Based Metabolomics at Arbitrary Magnetic Fields D. Li et al. 10.1021/acs.analchem.4c02688
- COLMAR1d2d: Synergistic Combination of 1D with 2D NMR for Enhanced High-Throughput Identification and Quantification of Metabolites in Complex Mixtures R. Cabrera Allpas et al. 10.1021/acs.analchem.5c00957
- Detection of intramolecular protein dynamics on nanosecond-to-microsecond timescales by nanoparticle-assisted NMR spin relaxation (NASR) X. Xiang et al. 10.1038/s41596-025-01177-1
- Exploring microbial natural products through NMR-based metabolomics D. Wang et al. 10.1039/D4NP00065J
- CloudBrain-NMR: An Intelligent Cloud-Computing Platform for NMR Spectroscopy Processing, Reconstruction, and Analysis D. Guo et al. 10.1109/TIM.2024.3415787
- ShimNet: A Neural Network for Postacquisition Improvement of NMR Spectra Distorted by Magnetic-Field Inhomogeneity S. Jopa et al. 10.1021/acs.jpcb.5c02632
- Assessment of the purity of pyributicarb with signals partially overlapping with a structural isomer impurity in NMR spectrum C. Okamoto et al. 10.1007/s44211-025-00817-6
- NMR-Onion - a transparent multi-model based 1D NMR deconvolution algorithm M. Brinks Sørensen et al. 10.1016/j.heliyon.2024.e36998
Latest update: 24 Aug 2025
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.
Recent advances in machine learning have opened new opportunities toward the automated analysis...
