Artificial Intelligence for Mass Spectrometry and Nuclear Magnetic Resonance Spectroscopy
Florian Fricke1, Safdar Mahmood2, Javier Hoffmann2, Marcelo Brandalero2, Sascha Liehr3, Simon Kern3, Klas Meyer3, Stefan Kowarik4, Stephan Westerdicky1, Michael Maiwald3 and Michael Hübner2
1Ruhr-Universität Bochum (RUB), Germany
2Brandenburgische Technische Universität (B-TU), Germany
3Bundesanstalt für Materialforschung und -prüfung (BAM), Germany
4University of Graz, Austria
ABSTRACT
Mass Spectrometry (MS) and Nuclear Magnetic Resonance Spectroscopy (NMR) are critical components of every industrial chemical process as they provide information on the concentrations of individual compounds and by-products. These processes are carried out manually and by a specialist, which takes a substantial amount of time and prevents their utilization for real-time closed-loop process control. This paper presents recent advances from two projects that use Artificial Neural Networks (ANNs) to address the challenges of automation and performance-efficient realizations of MS and NMR. In the first part, a complete toolchain has been developed to develop simulated spectra and train ANNs to identify compounds in MS. In the second part, a limited number of experimental NMR spectra have been augmented by simulated spectra to train an ANN with better prediction performance and speed than state-of-theart analysis. These results suggest that, in the context of the digital transformation of the process industry, we are now on the threshold of a possible strongly simplified use of MS and MRS and the accompanying data evaluation by machine-supported procedures, and can utilize both methods much wider for reaction and process monitoring or quality control.
Keywords: Industry 4.0, Cyber-Physical Systems, Artificial Neural Networks, Mass Spectrometry, Nuclear Magnetic Resonance Spectroscopy.
