AUTHOR=Fischetti Giulia , Schmid Nicolas , Bruderer Simon , Caldarelli Guido , Scarso Alessandro , Henrici Andreas , Wilhelm Dirk TITLE=Automatic classification of signal regions in 1H Nuclear Magnetic Resonance spectra JOURNAL=Frontiers in Artificial Intelligence VOLUME=5 YEAR=2023 URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2022.1116416 DOI=10.3389/frai.2022.1116416 ISSN=2624-8212 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.