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Comparative Evaluation of Electron Ionization Mass Spectral Prediction Methods

Devata, Sriram and Cleaves, H. James and Dimandja, John and Heist, Christopher A. and Meringer, Markus (2023) Comparative Evaluation of Electron Ionization Mass Spectral Prediction Methods. Journal of the American Society for Mass Spectrometry, 34 (8), pp. 1584-1592. American Chemical Society. doi: 10.1021/jasms.3c00059. ISSN 1044-0305.

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Official URL: https://dx.doi.org/10.1021/jasms.3c00059


During the past decade promising methods for computational prediction of electron ionization mass spectra have been developed. The most prominent ones are based on quantum chemistry (QCEIMS) and machine learning (CFM-EI, NEIMS). Here we provide a threefold comparison of these methods with respect to spectral prediction and compound identification. We found that there is no unambiguous way to determine the best of these three methods. Among other factors, we find that the choice of spectral distance functions plays an important role regarding the performance for compound identification.

Item URL in elib:https://elib.dlr.de/196562/
Document Type:Article
Title:Comparative Evaluation of Electron Ionization Mass Spectral Prediction Methods
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Devata, SriramInternational Institute of Information Technology, Hyderabad, IndiaUNSPECIFIEDUNSPECIFIED
Cleaves, H. JamesEarth-Life Science Institute, Tokyo Institute of TechnologyUNSPECIFIEDUNSPECIFIED
Dimandja, JohnGeorgia Institute of TechnologyUNSPECIFIEDUNSPECIFIED
Heist, Christopher A.Georgia Institute of TechnologyUNSPECIFIEDUNSPECIFIED
Meringer, MarkusUNSPECIFIEDhttps://orcid.org/0000-0001-8526-2429UNSPECIFIED
Date:30 June 2023
Journal or Publication Title:Journal of the American Society for Mass Spectrometry
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In ISI Web of Science:Yes
Page Range:pp. 1584-1592
Publisher:American Chemical Society
Keywords:Quantum chemistry, Machine learning, Compound identification, Spectral distance, Space exploration missions
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Artificial Intelligence, R - Exploration of the Solar System
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Atmospheric Processors
Deposited By: Meringer, Dr.rer.nat. Markus
Deposited On:06 Sep 2023 12:17
Last Modified:14 Sep 2023 17:45

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