Neural Networks for Conversion of Simulated NMR Spectra from Low-Field to High-Field for Quantitative Metabolomics
<b>Background:</b> The introduction of benchtop NMR instruments has made NMR spectroscopy a more accessible, affordable option for research and industry, but the lower spectral resolution and SNR of a signal acquired on low magnetic field spectrometers may complicate the quantitative ana...
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MDPI AG
2024-12-01
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| Online Access: | https://www.mdpi.com/2218-1989/14/12/666 |
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| author | Hayden Johnson Aaryani Tipirneni-Sajja |
| author_facet | Hayden Johnson Aaryani Tipirneni-Sajja |
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| description | <b>Background:</b> The introduction of benchtop NMR instruments has made NMR spectroscopy a more accessible, affordable option for research and industry, but the lower spectral resolution and SNR of a signal acquired on low magnetic field spectrometers may complicate the quantitative analysis of spectra. <b>Methods:</b> In this work, we compare the performance of multiple neural network architectures in the task of converting simulated 100 MHz NMR spectra to 400 MHz with the goal of improving the quality of the low-field spectra for analyte quantification. Multi-layered perceptron networks are also used to directly quantify metabolites in simulated 100 and 400 MHz spectra for comparison. <b>Results:</b> The transformer network was the only architecture in this study capable of reliably converting the low-field NMR spectra to high-field spectra in mixtures of 21 and 87 metabolites. Multi-layered perceptron-based metabolite quantification was slightly more accurate when directly processing the low-field spectra compared to high-field converted spectra, which, at least for the current study, precludes the need for low-to-high-field spectral conversion; however, this comparison of low and high-field quantification necessitates further research, comparison, and experimental validation. <b>Conclusions:</b> The transformer method of NMR data processing was effective in converting low-field simulated spectra to high-field for metabolomic applications and could be further explored to automate processing in other areas of NMR spectroscopy. |
| format | Article |
| id | doaj-art-ed686208ecef4f59aaf3dfe9eb1e31d2 |
| institution | OA Journals |
| issn | 2218-1989 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Metabolites |
| spelling | doaj-art-ed686208ecef4f59aaf3dfe9eb1e31d22025-08-20T02:00:35ZengMDPI AGMetabolites2218-19892024-12-01141266610.3390/metabo14120666Neural Networks for Conversion of Simulated NMR Spectra from Low-Field to High-Field for Quantitative MetabolomicsHayden Johnson0Aaryani Tipirneni-Sajja1Department of Biomedical Engineering, The University of Memphis, Memphis, TN 38152, USADepartment of Biomedical Engineering, The University of Memphis, Memphis, TN 38152, USA<b>Background:</b> The introduction of benchtop NMR instruments has made NMR spectroscopy a more accessible, affordable option for research and industry, but the lower spectral resolution and SNR of a signal acquired on low magnetic field spectrometers may complicate the quantitative analysis of spectra. <b>Methods:</b> In this work, we compare the performance of multiple neural network architectures in the task of converting simulated 100 MHz NMR spectra to 400 MHz with the goal of improving the quality of the low-field spectra for analyte quantification. Multi-layered perceptron networks are also used to directly quantify metabolites in simulated 100 and 400 MHz spectra for comparison. <b>Results:</b> The transformer network was the only architecture in this study capable of reliably converting the low-field NMR spectra to high-field spectra in mixtures of 21 and 87 metabolites. Multi-layered perceptron-based metabolite quantification was slightly more accurate when directly processing the low-field spectra compared to high-field converted spectra, which, at least for the current study, precludes the need for low-to-high-field spectral conversion; however, this comparison of low and high-field quantification necessitates further research, comparison, and experimental validation. <b>Conclusions:</b> The transformer method of NMR data processing was effective in converting low-field simulated spectra to high-field for metabolomic applications and could be further explored to automate processing in other areas of NMR spectroscopy.https://www.mdpi.com/2218-1989/14/12/666NMR spectroscopyneural networksmetabolomicstransformerlow-field NMR |
| spellingShingle | Hayden Johnson Aaryani Tipirneni-Sajja Neural Networks for Conversion of Simulated NMR Spectra from Low-Field to High-Field for Quantitative Metabolomics Metabolites NMR spectroscopy neural networks metabolomics transformer low-field NMR |
| title | Neural Networks for Conversion of Simulated NMR Spectra from Low-Field to High-Field for Quantitative Metabolomics |
| title_full | Neural Networks for Conversion of Simulated NMR Spectra from Low-Field to High-Field for Quantitative Metabolomics |
| title_fullStr | Neural Networks for Conversion of Simulated NMR Spectra from Low-Field to High-Field for Quantitative Metabolomics |
| title_full_unstemmed | Neural Networks for Conversion of Simulated NMR Spectra from Low-Field to High-Field for Quantitative Metabolomics |
| title_short | Neural Networks for Conversion of Simulated NMR Spectra from Low-Field to High-Field for Quantitative Metabolomics |
| title_sort | neural networks for conversion of simulated nmr spectra from low field to high field for quantitative metabolomics |
| topic | NMR spectroscopy neural networks metabolomics transformer low-field NMR |
| url | https://www.mdpi.com/2218-1989/14/12/666 |
| work_keys_str_mv | AT haydenjohnson neuralnetworksforconversionofsimulatednmrspectrafromlowfieldtohighfieldforquantitativemetabolomics AT aaryanitipirnenisajja neuralnetworksforconversionofsimulatednmrspectrafromlowfieldtohighfieldforquantitativemetabolomics |