Non-target impurity profiling of marketplace Cetirizine using high-resolution mass spectrometry and multivariate data analysis

2016 ◽  
Vol 30 (17) ◽  
pp. 1941-1950 ◽  
Author(s):  
Zhe Zhou
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
High Resolution ◽  
Multivariate Data Analysis ◽  
High Resolution Mass Spectrometry ◽  
Multivariate Data ◽  
Impurity Profiling ◽  
High Resolution Mass ◽  
Resolution Mass

scholarly journals Analysis of stable isotope assisted metabolomics data acquired by high resolution mass spectrometry

2017 ◽  
Vol 9 (15) ◽  
pp. 2275-2283 ◽  
Author(s):  
X. Wei ◽  
P. K. Lorkiewicz ◽  
B. Shi ◽  
J. K. Salabei ◽  
B. G. Hill ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
High Resolution ◽  
Stable Isotope ◽  
Mass Spectrometer ◽  
High Resolution Mass Spectrometry ◽  
Metabolomics Data ◽  
High Resolution Mass ◽  
High Resolution Mass Spectrometer ◽  
Resolution Mass

Developed a suite of data analysis algorithms for automatic analysis of SIAM data acquired on a high resolution mass spectrometer.

scholarly journals Automated fragment formula annotation for electron ionisation, high resolution mass spectrometry: application to atmospheric measurements of halocarbons

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Myriam Guillevic ◽  
Aurore Guillevic ◽  
Martin K. Vollmer ◽  
Paul Schlauri ◽  
Matthias Hill ◽  
...  
Keyword(s):  
Mass Spectrometry ◽  
Data Analysis ◽  
High Resolution ◽  
High Resolution Mass Spectrometry ◽  
Trace Gases ◽  
Trace Gas ◽  
Atmospheric Measurements ◽  
High Resolution Mass ◽  
Atmospheric Trace Gas ◽  
Resolution Mass

Abstract Background Non-target screening consists in searching a sample for all present substances, suspected or unknown, with very little prior knowledge about the sample. This approach has been introduced more than a decade ago in the field of water analysis, together with dedicated compound identification tools, but is still very scarce for indoor and atmospheric trace gas measurements, despite the clear need for a better understanding of the atmospheric trace gas composition. For a systematic detection of emerging trace gases in the atmosphere, a new and powerful analytical method is gas chromatography (GC) of preconcentrated samples, followed by electron ionisation, high resolution mass spectrometry (EI-HRMS). In this work, we present data analysis tools to enable automated fragment formula annotation for unknown compounds measured by GC-EI-HRMS. Results Based on co-eluting mass/charge fragments, we developed an innovative data analysis method to reliably reconstruct the chemical formulae of the fragments, using efficient combinatorics and graph theory. The method does not require the presence of the molecular ion, which is absent in $$\sim$$ ∼ 40% of EI spectra. Our method has been trained and validated on >50 halocarbons and hydrocarbons, with 3–20 atoms and molar masses of 30–330 g mol$$^{-1}$$ - 1 , measured with a mass resolution of approx. 3500. For >90% of the compounds, more than 90% of the annotated fragment formulae are correct. Cases of wrong identification can be attributed to the scarcity of detected fragments per compound or the lack of isotopic constraint (no minor isotopocule detected). Conclusions Our method enables to reconstruct most probable chemical formulae independently from spectral databases. Therefore, it demonstrates the suitability of EI-HRMS data for non-target analysis and paves the way for the identification of substances for which no EI mass spectrum is registered in databases. We illustrate the performances of our method for atmospheric trace gases and suggest that it may be well suited for many other types of samples. The L-GPL licenced Python code is released under the name ALPINAC for ALgorithmic Process for Identification of Non-targeted Atmospheric Compounds.

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