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Automatic classification of signal regions in 1H Nuclear Magnetic Resonance spectra.


ABSTRACT: The identification and characterization of signal regions in Nuclear Magnetic Resonance (NMR) spectra is a challenging but crucial phase in the analysis and determination of complex chemical compounds. Here, we present a novel supervised deep learning approach to perform automatic detection and classification of multiplets in 1H NMR spectra. Our deep neural network was trained on a large number of synthetic spectra, with complete control over the features represented in the samples. We show that our model can detect signal regions effectively and minimize classification errors between different types of resonance patterns. We demonstrate that the network generalizes remarkably well on real experimental 1H NMR spectra.

SUBMITTER: Fischetti G 

PROVIDER: S-EPMC9874632 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Automatic classification of signal regions in <sup>1</sup>H Nuclear Magnetic Resonance spectra.

Fischetti Giulia G   Schmid Nicolas N   Bruderer Simon S   Caldarelli Guido G   Scarso Alessandro A   Henrici Andreas A   Wilhelm Dirk D  

Frontiers in artificial intelligence 20230111


The identification and characterization of signal regions in Nuclear Magnetic Resonance (NMR) spectra is a challenging but crucial phase in the analysis and determination of complex chemical compounds. Here, we present a novel supervised deep learning approach to perform automatic detection and classification of multiplets in <sup>1</sup>H NMR spectra. Our deep neural network was trained on a large number of synthetic spectra, with complete control over the features represented in the samples. W  ...[more]

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