Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1007/s00216-018-0857-5
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Titel (primär) Performance of combined fragmentation and retention prediction for the identification of organic micropollutants by LC-HRMS
Autor Hu, M.; Müller, E.; Schymanski, E.L.; Ruttkies, C.; Schulze, T. ORCID logo ; Brack, W.; Krauss, M. ORCID logo
Quelle Analytical and Bioanalytical Chemistry
Erscheinungsjahr 2018
Department WANA
Band/Volume 410
Heft 7
Seite von 1931
Seite bis 1941
Sprache englisch
Keywords LC-HRMS; Environmental contaminants; Structure elucidation; Fragmentation prediction; Retention prediction; Micropollutants
UFZ Querschnittsthemen RU2;
Abstract In nontarget screening, structure elucidation of small molecules from high resolution mass spectrometry (HRMS) data is challenging, particularly the selection of the most likely candidate structure among the many retrieved from compound databases. Several fragmentation and retention prediction methods have been developed to improve this candidate selection. In order to evaluate their performance, we compared two in silico fragmenters (MetFrag and CFM-ID) and two retention time prediction models (based on the chromatographic hydrophobicity index (CHI) and on log D). A set of 78 known organic micropollutants was analyzed by liquid chromatography coupled to a LTQ Orbitrap HRMS with electrospray ionization (ESI) in positive and negative mode using two fragmentation techniques with different collision energies. Both fragmenters (MetFrag and CFM-ID) performed well for most compounds, with average ranking the correct candidate structure within the top 25% and 22 to 37% for ESI+ and ESI− mode, respectively. The rank of the correct candidate structure slightly improved when MetFrag and CFM-ID were combined. For unknown compounds detected in both ESI+ and ESI−, generally positive mode mass spectra were better for further structure elucidation. Both retention prediction models performed reasonably well for more hydrophobic compounds but not for early eluting hydrophilic substances. The log D prediction showed a better accuracy than the CHI model. Although the two fragmentation prediction methods are more diagnostic and sensitive for candidate selection, the inclusion of retention prediction by calculating a consensus score with optimized weighting can improve the ranking of correct candidates as compared to the individual methods.
dauerhafte UFZ-Verlinkung
Hu, M., Müller, E., Schymanski, E.L., Ruttkies, C., Schulze, T., Brack, W., Krauss, M. (2018):
Performance of combined fragmentation and retention prediction for the identification of organic micropollutants by LC-HRMS
Anal. Bioanal. Chem. 410 (7), 1931 - 1941 10.1007/s00216-018-0857-5