Predicting the Diagnostic Information of Tandem Mass Spectra of Environmentally Relevant Compounds Using Machine Learning-Reference-Cited by-同舟云学术

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Predicting the Diagnostic Information of Tandem Mass Spectra of Environmentally Relevant Compounds Using Machine Learning

Published:2023-10-09 Issue:42 Volume:95 Page:15810-15817
ISSN:0003-2700
Container-title:Analytical Chemistry
language:en
Short-container-title:Anal. Chem.

Author:

Codrean S.¹,Kruit B.¹,Meekel N.²^ORCID,Vughs D.²,Béen F.²³^ORCID

Affiliation:

1. Faculty of Science, Artificial Intelligence, Vrije Universiteit Amsterdam, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands

2. KWR Water Research Institute, Groningenhaven 7, P.O. Box 1072, 3430 BB Nieuwegein, The Netherlands

3. Chemistry for Environment and Health, Amsterdam Institute for Life and Environment (A-LIFE), Vrije Universiteit De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands

Funder

Joint Research Programme Water Utilities (BTO)

Publisher

American Chemical Society (ACS)

Subject

Analytical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.3c03470

Reference31 articles.

1. Nontarget Screening with High Resolution Mass Spectrometry in the Environment: Ready to Go?

2. Neumann, S.; Stravs, M.; Schymanski, E.; Schulze, T. MassBankEU. https://massbank.eu/MassBank/Search (accessed 2022-11-22).

3. Fiehnlab. MassBank of North America. https://mona.fiehnlab.ucdavis.edu/ (accessed 2022-11-22).

4. The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

5. Annotating Nontargeted LC-HRMS/MS Data with Two Complementary Tandem Mass Spectral Libraries

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