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Compound Identification in Comprehensive Gas Chromatography—Mass Spectrometry-Based Metabolomics by Blind Source Separation

  • Xavier Domingo-AlmenaraEmail author
  • Alexandre Perera
  • Noelia Ramírez
  • Jesus Brezmes
Conference paper
  • 650 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 375)

Abstract

Comprehensive gas chromatography - mass spectromety (GCxGC-MS) has become a promising tool in metabolomics. However, algorithms for GCxGC-MS data processing are needed in order to automatically process the data and extract the most pure information about the compounds appearing in the complex biological samples. This study shows the capability of orthogonal signal deconvolution (OSD), a novel algorithm based on blind source separation, to extract the spectra of the compounds appearing in GCxGC-MS samples. Results include a comparison between OSD and multivariate curve resolution - alternating least squares (MCR-ALS) with the extraction of metabolites spectra in a human serum sample analyzed through GCxGC-MS. This study concludes that OSD is a promising alternative for GCxGC-MS data processing.

Keywords

Comprehensive gas chromatography Orthogonal signal deconvolution Multivariate curve resolution Compound deconvolution Independent component analysis 

Notes

Acknowledgments

The authors want to thank the Center for Omics Sciences, Tarragona, for providing the samples and to Dr. R. Ras and Mrs. S. Mariné for the sample preparation and scientific advising.

References

  1. 1.
    Zhang, A., Sun, H., Wang, X.: Serum metabolomics as a novel diagnostic approach for disease: a systematic review. Anal. Bioanal. Chem. 404(4), 1239–1245 (2012)CrossRefGoogle Scholar
  2. 2.
    Seeley, J.V., Seeley, S.K.: Multidimensional gas chromatography: fundamental advances and new applications. Anal. Chem. 85(2), 557–578 (2012)CrossRefGoogle Scholar
  3. 3.
    Mondello, L., Tranchida, P.Q., Dugo, P., Dugo, G.: Comprehensive two-dimensional gas chromatography-mass spectrometry: a review. Mass Spectrom. Rev. 27(2), 101–124 (2008)CrossRefGoogle Scholar
  4. 4.
    Matos, J.T.V., Duarte, R.M.B.O., Duarte, A.C.: Trends in data processing of comprehensive two-dimensional chromatography: state of the art. J. Chromatogr. B 910, 31–45 (2012)CrossRefGoogle Scholar
  5. 5.
    Faber, N.K.M., Bro, R., Hopke, P.K.: Recent developments in CANDECOMP/PARAFAC algorithms: a critical review. Chemometr. Intell. Lab. Syst. 65(1), 119–137 (2003)CrossRefGoogle Scholar
  6. 6.
    van Stokkum, I.H.M., Mullen, K.M., Mihaleva, V.V.: Global analysis of multiple gas chromatographymass spectrometry (GC/MS) data sets: a method for resolution of co-eluting components with comparison to MCR-ALS. Chemometr. Intell. Lab. Syst. 95(2), 150–163 (2009)CrossRefGoogle Scholar
  7. 7.
    Roberts, S., Everson, R.: Independent Component Analysis: Principles and Practice. Cambridge University Press (2001)Google Scholar
  8. 8.
    Wang, G., Cai, W., Shao, X.: A primary study on resolution of overlapping GC-MS signal using mean-field approach independent component analysis. Chemometr. Intell. Lab. Syst. 82(12), 137–144 (2006)CrossRefGoogle Scholar
  9. 9.
    Liu, Z., Cai, W., Shao, X.: Sequential extraction of mass spectra and chromatographic profiles from overlapping gas chromatographymass spectroscopy signals. J. Chromatogr. A 1190(12), 358–364 (2008)CrossRefGoogle Scholar
  10. 10.
    Shao, X., Liu, Z., Cai, W.: Resolving multi-component overlapping GC-MS signals by immune algorithms. TrAC Trends Anal. Chem. 28(11), 1312–1321 (2009)CrossRefGoogle Scholar
  11. 11.
    Domingo-Almenara, X., Perera, A., Ramirez, N., Canellas, N., Correig, X., Brezmes, J.: Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation. J. Chromatogr. A 28(1409), 226–233 (2015)Google Scholar
  12. 12.
    Hummel, J., Strehmel, N., Selbig, J., Walther, D., Kopka, J.: Decision tree supported substructure prediction of metabolites from GC-MS profiles. Metabolomics 6(2), 322–333 (2010)CrossRefGoogle Scholar
  13. 13.
    Cardoso, J.F., Souloumiac, A.: Blind beamforming for non-gaussian signals. IEE Proc. F Radar Signal Proc. 140(6), 362–370 (1993)CrossRefGoogle Scholar
  14. 14.
    Wan, K.X., Vidavsky, I., Gross, M.L.: Comparing similar spectra: from similarity index to spectral contrast angle. J. Am. Soc. Mass Spectrom. 13(1), 85–88 (2002)CrossRefGoogle Scholar
  15. 15.
    de Juan, A., Vander Heyden, Y., Tauler, R., Massart, D.L.: Assessment of new constraints applied to the alternating least squares method. Anal. Chim. Acta 346(3), 307–318 (1997)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Xavier Domingo-Almenara
    • 1
    • 3
    Email author
  • Alexandre Perera
    • 2
  • Noelia Ramírez
    • 1
    • 3
  • Jesus Brezmes
    • 1
    • 3
  1. 1.Metabolomics Platform, IISPVUniversitat Rovira i Virgili, Campus SesceladesTarragona, CataloniaSpain
  2. 2.B2SLAB. Department d’Enginyeria de Sistemes, Automàtica i Informàtica IndustrialCIBER-BBN, Universitat Politècnica de CatalunyaBarcelona, CataloniaSpain
  3. 3.CIBERDEMBiomedical Research Networking Center in Diabetes and Associated Metabolic DisordersBarcelona, CataloniaSpain

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