This study explores the use of neural networks to convert simulated low-field NMR spectra into high-field equivalents for accurate and efficient quantitative metabolomics. By leveraging deep learning techniques, the approach aims to overcome the limitations of low-field NMR, enhancing spectral resolution and quantitative analysis in metabolomics research.
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