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 ourmodel can detect signal regions e􀀀ectively andminimize classification errors between di􀀀erent types of resonance patterns. We demonstrate that the network generalizes remarkably well on real experimental 1H NMR spectra.

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

Fischetti, Giulia
;
Caldarelli, Guido;Scarso, Alessandro;
2023-01-01

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 ourmodel can detect signal regions e􀀀ectively andminimize classification errors between di􀀀erent types of resonance patterns. We demonstrate that the network generalizes remarkably well on real experimental 1H NMR spectra.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5012069
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