English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

De novo analysis of electron impact mass spectra using fragmentation trees

MPS-Authors
/persons/resource/persons23872

Hufsky,  Franziska
IMPRS on Ecological Interactions, MPI for Chemical Ecology, Max Planck Society;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

Hufsky, F., Rempt, M., Rasche, F., Pohnert, G., & Böcker, S. (2012). De novo analysis of electron impact mass spectra using fragmentation trees. Analytica Chimica Acta, 39, 67-76. doi:10.1016/j.aca.2012.06.021.


Cite as: https://hdl.handle.net/11858/00-001M-0000-000F-EDE0-A
Abstract
The automated fragmentation analysis of high resolution EI mass spectra based on a fragmentation tree algorithm is introduced. Fragmentation trees are constructed from EI spectra by automated signal extraction and evaluation. These trees explain relevant fragmentation reactions and assign molecular formulas to fragments. The method enables the identification of the molecular ion and the molecular formula of a metabolite if the molecular ion is present in the spectrum. These identifications are independent of existing library knowledge and, thus, support assignment and structural elucidation of unknown compounds. The method works even if the molecular ion is of very low abundance or hidden under contaminants with higher masses. We apply the algorithm to a selection of 50 derivatized and underivatized metabolites and demonstrate that in 78% of cases the molecular ion can be correctly assigned. The automatically constructed fragmentation trees correspond very well to published mechanisms and allow the assignment of specific relevant fragments and fragmentation pathways even in the most complex EI-spectra in our dataset. This method will be very helpful in the automated analysis of metabolites that are not included in common libraries and it thus has the potential to support the explorative character of metabolomics studies.