4.6 Article

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

期刊

METABOLITES
卷 9, 期 1, 页码 -

出版社

MDPI
DOI: 10.3390/metabo9010003

关键词

nontarget analysis; liquid chromatography mass spectrometry; compound identification; tandem mass spectral library; forensics; wastewater

资金

  1. HBM4EU project
  2. European Union's Horizon 2020 research and innovation programme [733032]
  3. Luxembourg National Research Fund (FNR) [12341006]
  4. European Union Seventh Framework Program project SOLUTIONS [603437]
  5. Swiss Federal Office for the Environment
  6. Swiss National Science Foundation [205320165935]

向作者/读者索取更多资源

Tandem mass spectral databases are indispensable for fast and reliable compound identification in nontargeted analysis with liquid chromatography-high resolution tandem mass spectrometry (LC-HRMS/MS), which is applied to a wide range of scientific fields. While many articles now review and compare spectral libraries, in this manuscript we investigate two high-quality and specialized collections from our respective institutes, recorded on different instruments (quadrupole time-of-flight or QqTOF vs. Orbitrap). The optimal range of collision energies for spectral comparison was evaluated using 233 overlapping compounds between the two libraries, revealing that spectra in the range of CE 20-50 eV on the QqTOF and 30-60 nominal collision energy units on the Orbitrap provided optimal matching results for these libraries. Applications to complex samples from the respective institutes revealed that the libraries, combined with a simple data mining approach to retrieve all spectra with precursor and fragment information, could confirm many validated target identifications and yield several new Level 2a (spectral match) identifications. While the results presented are not surprising in many ways, this article adds new results to the debate on the comparability of Orbitrap and QqTOF data and the application of spectral libraries to yield rapid and high-confidence tentative identifications in complex human and environmental samples.

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