In-tube collision-induced dissociation for selected ion flow-drift tube mass spectrometry, SIFDT-MS: a case study of NO+ reactions with isomeric monoterpenes

2016 ◽  
Vol 30 (18) ◽  
pp. 2009-2016 ◽  
Author(s):  
Anatolii Spesyvyi ◽  
Kristýna Sovová ◽  
Patrik Španěl

2015 ◽  
Vol 87 (24) ◽  
pp. 12151-12160 ◽  
Author(s):  
Anatolii Spesyvyi ◽  
David Smith ◽  
Patrik Španěl


2019 ◽  
Author(s):  
Michal Lacko ◽  
Nijing Wang ◽  
Kristýna Sovová ◽  
Pavel Pásztor ◽  
Patrik Španěl

Abstract. Soft chemical ionization mass spectrometry (SCI-MS) techniques can be used to accurately quantify volatile organic compounds (VOCs) in air in real time; however, differentiation of isomers still represents a challenge. A suitable pre-separation technique is thus needed, ideally capable of analyses in a few tens of seconds. To this end, a bespoke fast GC with an electrically heated 5 m long metallic capillary column was coupled to a selected ion flow tube mass spectrometry (SIFT-MS) instrument. To assess the performance of this combination a case study of monoterpene isomer (C10H16) analyses was carried out. The monoterpenes were quantified by SIFT-MS using H3O+ reagent ions (analyte ions C10H17+, m/z 137, and C6H9+, m/z 81) and NO+ reagent ions (analyte ions C10H16+, m/z 136, and C7H9+, m/z 93). The combinations of the fragment ion relative intensities obtained using H3O+ and NO+ were shown to be characteristic of the individual monoterpenes. Two non-polar GC columns (Restek Inc.) were tested: the advantage of MXT-1 was shorter retention times whilst the advantage of MXT-Volatiles was better temporal separation. Thus, it is possible to quantify components of a monoterpene mixture in less than 45 s by the MXT-1 column and to separate them in less 180 s by the MXT-Volatiles column. As an illustrative example, the headspace of three conifer needle samples was analysed by both reagent ions with both columns showing that mainly α-pinene, β-pinene and 3-carene were present.



2015 ◽  
Vol 63 (3) ◽  
pp. 829-835 ◽  
Author(s):  
Simon Van Kerrebroeck ◽  
Joeri Vercammen ◽  
Roel Wuyts ◽  
Luc De Vuyst


2020 ◽  
Vol 86 (7) ◽  
pp. 12-19
Author(s):  
I. V. Plyushchenko ◽  
D. G. Shakhmatov ◽  
I. A. Rodin

A viral development of statistical data processing, computing capabilities, chromatography-mass spectrometry, and omics technologies (technologies based on the achievements of genomics, transcriptomics, proteomics, metabolomics) in recent decades has not led to formation of a unified protocol for untargeted profiling. Systematic errors reduce the reproducibility and reliability of the obtained results, and at the same time hinder consolidation and analysis of data gained in large-scale multi-day experiments. We propose an algorithm for conducting omics profiling to identify potential markers in the samples of complex composition and present the case study of urine samples obtained from different clinical groups of patients. Profiling was carried out by the method of liquid chromatography mass spectrometry. The markers were selected using methods of multivariate analysis including machine learning and feature selection. Testing of the approach was performed using an independent dataset by clustering and projection on principal components.





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