Informatics for cross-sample analysis with comprehensive two-dimensional gas chromatography and high-resolution mass spectrometry (GCxGC–HRMS)

Reichenbach, Stephen E.; Xiang, Tian; Qing, Tao; Ledford, Edward B.; Wu, Zhanpin; Fiehn, Oliver

doi:10.1016/j.talanta.2010.09.057

Cited by 83 publications

(55 citation statements)

References 13 publications

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“…This image represents all the constituents present in all samples. This template creation approach [34] and its successful application for MTS-PP was reported elsewhere [1]. The present template was made of a total of 1480 regions and a 24 × 1480 matrix containing the relative percent responses of peak areas submitted to PCA analysis after mean-centering pre-treatment.…”

Section: Comparison Of Two Different Cigarette Typesmentioning

confidence: 99%

Thermal desorption comprehensive two-dimensional gas chromatography coupled to time of flight mass spectrometry for vapour phase mainstream tobacco smoke analysis

Savareear

Brokl

Wright

et al. 2017

Journal of Chromatography A

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a b s t r a c tA thermal desorption comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (TD-GC × GC-TOFMS) method has been developed for the analysis of mainstream tobacco smoke (MTS) vapour phase (VP). The selection process of the sample introduction approach involved comparing the results obtained from three different approaches: a) use of gas sampling bag followed by SPME (Tedlar ® -SPME), b) gas sampling bag followed by TD (Tedlar ® -TD), and c) sampling directly on TD sorbents (Direct-TD). Six different SPME fibers and six different TD sorbent beds were evaluated for the extraction capacities in terms of total number of peaks and related intensities or peak areas. The best results were obtained for the Direct-TD approach using Tenax TA/Carbograph1TD/Carboxen1003 sorbent tubes. The optimisation of TD tube desorption parameters was carried out using a face-centered central composite experimental design and resulted in the use of the Tenax TA/Carbograph 1TD/Carboxen 1003 sorbent with a 7.5 min desorption time, a 60 mL/min tube desorption flow, and a 250• C tube desorption temperature. The optimised method was applied to the separation of MTS-VP constituents, with 665 analytes detected. The method precision ranged from 1% to 15% for over 99% of identified peak areas and from 0% to 3% and 0% to 1% for both first ( 1 t R ) and second ( 2 t R ) dimension retention times, respectively. The method was applied to the analyses of two cigarette types differing in their filter construction. Principal component analysis (PCA) allowed a clear differentiation of the studied cigarette types (PC1 describing 94% of the explained variance). Supervised Fisher ratio analysis permitted the identification of compounds responsible for the chemical differences between the two sample types. A set of 91 most relevant compounds was selected by applying a Fisher ratio cut-off approach and most of them were selectively removed by one of the cigarette filter types.

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Section: Comparison Of Two Different Cigarette Typesmentioning

confidence: 99%

Thermal desorption comprehensive two-dimensional gas chromatography coupled to time of flight mass spectrometry for vapour phase mainstream tobacco smoke analysis

Savareear

Brokl

Wright

et al. 2017

Journal of Chromatography A

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“…This approach seeks to achieve the one-feature-to-one-analyte selectivity of peak features but with the implicit matching of region features. Schmarr et al [53,54] and Reichenbach and co-workers [51,52,1] described similar approaches to defining regions for individual peaks detected across multiple samples. Schmarr and Bernhardt indicated that this general approach is common for 2D gel electrophoresis.…”

Section: Peak-region Featuresmentioning

confidence: 99%

“…Reichenbach et al [1] analyzed data from GC × GC with highresolution mass spectrometry (HRMS) of samples from breast cancer tumors. There were eighteen samples each from different individuals, with six samples each for grades 1-3 as determined by a cancer pathologist.…”

Section: Peak-region Featuresmentioning

confidence: 99%

“…Given a set of exemplary samples from known classes, determine the chemical characteristics that are most relevant for distinguishing the classes. For example, given samples of tumors labeled by grade, determine which characteristics (i.e., biomarkers) are most useful in distinguishing different tumor grades [1].…”

Section: Introductionmentioning

confidence: 99%

“…In pattern recognition (Step 4), the matched comparative features are analyzed to recognize relevant characteristics or patterns among samples. Such pattern recognition is not specific to chromatographic analysis and so can be performed with various general-purpose methods, including statistical methods such as principal component analysis (PCA), analysis of variance (ANOVA), and discriminant function analysis (DFA), and machine-learning methods such as support vector machines (SVM), neural networks, and decision trees [1,4,31,. Of course, research continues to improve methods for preprocessing and pattern recognition and to evaluate their effectiveness for non-targeted cross-sample chromatographic analysis, but that research is not the focus of this review.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Features for non-targeted cross-sample analysis with comprehensive two-dimensional chromatography

Reichenbach

Xiang

Cordero

et al. 2012

Journal of Chromatography A

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Development of two‐dimensional gas chromatography (GC×GC) coupled with Orbitrap‐technology‐based mass spectrometry: Interest in the identification of biofuel composition

et al. 2020

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Comprehensive gas chromatography (GC) has emerged in recent years as the technique of choice for the analysis of volatile and semivolatile compounds in complex matrices. Coupling it with high‐resolution mass spectrometry (MS) makes a powerful tool for identification and quantification of organic compounds. The results obtained in this study showed a significant improvement by using GC×GC‐EI‐MS in comparison with GC‐EI‐MS; the separation of chromatogram peaks was highly improved, which facilitated detection and identification. However, the limitation of Orbitrap mass analyzer compared with time‐of‐flight analyzer is the data acquisition rate; the frequency average was about 25 Hz at a mass resolving power of 15.000, which is barely sufficient for the proper reconstruction of the narrowest chromatographic peaks. On the other hand, the different spectra obtained in this study showed an average mass accuracy of about 1 ppm. Within this average mass accuracy, some reasonable elemental compositions can be proposed and combined with characteristic fragment ions, and the molecules can be identified with precision. At a mass resolving power of 7.500, the scan rate reaches 43 Hz and the GC×GC‐MS peaks can be represented by more than 10 data points, which should be sufficient for quantification. The GC×GC‐MS was also applied to analyze a cellulose bio‐oil sample. Following this, a highly resolved chromatogram was obtained, allowing EI mass spectra containing molecular and fragment ions of many distinct molecules present in the sample to be identified.

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Informatics for cross-sample analysis with comprehensive two-dimensional gas chromatography and high-resolution mass spectrometry (GCxGC–HRMS)

Cited by 83 publications

References 13 publications

Thermal desorption comprehensive two-dimensional gas chromatography coupled to time of flight mass spectrometry for vapour phase mainstream tobacco smoke analysis

Thermal desorption comprehensive two-dimensional gas chromatography coupled to time of flight mass spectrometry for vapour phase mainstream tobacco smoke analysis

Features for non-targeted cross-sample analysis with comprehensive two-dimensional chromatography

Development of two‐dimensional gas chromatography (GC×GC) coupled with Orbitrap‐technology‐based mass spectrometry: Interest in the identification of biofuel composition

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