Gas Chromatography Coupled to Atmospheric Pressure Chemical Ionization FT-ICR Mass Spectrometry for Improvement of Data Reliability.
Gas Chromatography Coupled to Atmospheric Pressure Chemical Ionization FT-ICR Mass Spectrometry for Improvement of Data Reliability.
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气相色谱与大气压化学电离 FT-ICR 质谱联用可提高数据可靠性
DOI:
10.1021/acs.analchem.5b02114
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发表时间:
2015
影响因子:
7.4
通讯作者:
Zimmermann R
中科院分区:
文献类型:
--
作者:
Schwemer T;Rüger CP;Sklorz M;Zimmermann R
Atmospheric pressure chemical ionization (APCI) offers the advantage of molecular ion information with low fragmentation. Hyphenating APCI to gas chromatography (GC) and ultrahigh resolution mass spectrometry (FT-ICR MS) enables an improved characterization of complex mixtures. Data amounts acquired by this system are very huge, and existing peak picking algorithms are usually extremely time-consuming, if both gas chromatographic and ultrahigh resolution mass spectrometric data are concerned. Therefore, automatic routines are developed that are capable of handling these data sets and further allow the identification and removal of known ionization artifacts (e.g., water- and oxygen-adducts, demethylation, dehydrogenation, and decarboxylation). Furthermore, the data quality is enhanced by the prediction of an estimated retention index, which is calculated simply from exact mass data combined with a double bond equivalent correction. This retention index is used to identify mismatched elemental compositions. The approach was successfully tested for analysis of semivolatile components in heavy fuel oil and diesel fuel as well as primary combustion particles emitted by a ship diesel research engine. As a result, 10–28% of the detected compounds, mainly low abundant species, classically assigned by using only the mass spectrometric information, were identified as not valid and removed. Although GC separation is limited by the slow acquisition rate of the FT-ICR MS (<1 Hz), a database driven retention time comparison, as commonly used for low resolution GC/MS, can be applied for revealing isomeric information.
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DOI:
--
发表时间:
2004
期刊:
影响因子:
--
作者:
P. Kotianová;E. Matisová;X. Puxbaum;J. Lehotay
通讯作者:
J. Lehotay
影响因子:
4.3
作者:
Schiewek, Ralf;Lorenz, Matthias;Schmitz, Oliver J.
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Schmitz, Oliver J.
DOI:
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发表时间:
2007
期刊:
Bioinformatics Research and Development
影响因子:
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作者:
J. Rémy
通讯作者:
J. Rémy
影响因子:
3.4
作者:
Ralf Tautenhahn;C. Böttcher;S. Neumann
通讯作者:
S. Neumann
DOI:
10.1007/s13361-011-0304-8
发表时间:
2012-03-01
影响因子:
3.2
作者:
Ghislain, Thierry;Faure, Pierre;Michels, Raymond
通讯作者:
Michels, Raymond