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

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 H-1 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 H-1 NMR spectra.

Automated summary

We propose a novel supervised deep learning approach for the automatic detection and classification of multiplets in H-1 NMR spectra. Our deep neural network is trained on synthetic spectra and shows effective detection of signal regions and minimized classification errors. The network also demonstrates good generalization on real experimental H-1 NMR spectra.