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Analysis of Volatile Organic Compounds in Exhaled Breath by Gas Chromatography-Mass Spectrometry Combined with Chemometric Analysis

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Mass Spectrometry in Metabolomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1198))

Abstract

Analysis of exhaled breath samples reveals the presence of many volatile organic compounds (VOCs). The VOC composition of the breath, the so-called breath profile, contains a variety of information including the health status and condition of the organism that produced the sample. Therefore, breath profiling can be used in diagnosing and monitoring disease and other characteristics of the organism, such as phenotype, diet, and exercise. Among various techniques available for breath analysis, GC-MS provides the most extensive information with regard to the qualitative and quantitative presence of VOCs in breath.

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References

  1. Phillips M, Altorki N, Austin JHM et al (2005) Prediction of lung cancer using volatile biomarkers in breath. J Clin Oncol 23:839S

    Article  Google Scholar 

  2. Verdam FJ, Dallinga JW, Driessen A et al (2013) Non-alcoholic steatohepatitis: a non-invasive diagnosis by analysis of exhaled breath. J Hepatol 58:543–548

    Article  PubMed  CAS  Google Scholar 

  3. Boots AW, van Berkel JJBN, Dallinga JW et al (2012) The versatile use of exhaled volatile organic compounds in human health and disease. J Breath Res 6:027108

    Article  PubMed  Google Scholar 

  4. Van Berkel JJ, Dallinga JW, Moller GM et al (2010) A profile of volatile organic compounds in breath discriminates COPD patients from controls. Respir Med 104:557–563

    Article  PubMed  Google Scholar 

  5. Krzanowski WJ (2000) Principles of multivariate analysis (rev. ed). Oxford, New York, NY

    Google Scholar 

  6. Smolinska A, Hauschild AC, Fijten RRR et al (2014) Current Breathomics - a review on data preprocessing techniques and machine learning in metabolomics breath analysis. J Breath Res 8:027105

    Google Scholar 

  7. Peters S, van Velzen E, Janssen HG (2009) Parameter selection for peak alignment in chromatographic sample profiling: objective quality indicators and use of control samples. Anal Bioanal Chem 394:1273–1281

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  8. Lommen A (2009) MetAlign: Interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Anal Chem 81:3079–3086

    Article  PubMed  CAS  Google Scholar 

  9. Hoffmann N, Keck M, Neuweger H et al (2012) Combining peak- and chromatogram-based retention time alignment algorithms for multiple chromatography-mass spectrometry datasets. BMC Bioinformatics 13:214

    Article  PubMed  PubMed Central  Google Scholar 

  10. Ho TJ, Kuo CH, Wang SY et al (2013) True ion pick (TIPick): a denoising and peak picking algorithm to extract ion signals from liquid chromatography/mass spectrometry data. J Mass Spectrom 48:234–242

    Article  PubMed  CAS  Google Scholar 

  11. Leco ChromaTOF. http;//leco.com/products/separation-science/software-accessoires/chromatof-software

  12. Eilers PHC, Marx BD (1996) Flexible smoothing with B-splines and penalties. Stat Sci 11:89–102

    Article  Google Scholar 

  13. Eilers PHC (2003) A perfect smoother. Anal Chem 75:3631–3636

    Article  PubMed  CAS  Google Scholar 

  14. Eilers PHC, Currie ID, Durban M (2006) Fast and compact smoothing on large multidimensional grids. Comput Stat Data Anal 50:61–76

    Article  Google Scholar 

  15. Nielsen NPV, Carstensen JM, Smedsgaard J (1998) Aligning of single and multiple wavelength chromatographic profiles for chemometric data analysis using correlation optimised warping. J Chromatograph A 805:17–35

    Article  CAS  Google Scholar 

  16. Dieterle F, Ross A, Schlotterbeck G et al (2006) Probabilistic quotient normalization as robust method to account for dilution of complex biological mixtures. Application in H-1 NMR metabonomics. Anal Chem 78:4281–4290

    Article  PubMed  CAS  Google Scholar 

  17. Torgrip RJO, Aberg KM, Alm E et al (2008) A note on normalization of biofluid 1D H-1-NMR data. Metabolomics 4:114–121

    Article  CAS  Google Scholar 

  18. Spraul M, Neidig P, Klauck U et al (1994) Automatic reduction of NMR spectroscopic data for statistical and pattern-recognition classification of samples. J Pharm Biomed Anal 12:1215–1225

    Article  PubMed  CAS  Google Scholar 

  19. van den Berg RA, Hoefsloot HCJ, Westerhuis JA et al (2006) Centering, scaling, and transformations: improving the biological information content of metabolomics data. BMC Genomics 7:142

    Article  PubMed  PubMed Central  Google Scholar 

  20. Eriksson L, Johansson E, Kettaneh-Wold N, et al. (2006) Multi- and megavariate data analysis (2nd rev. ed). Umetrics AB, Umeå, Sweden

    Google Scholar 

  21. Xia JG, Psychogios N, Young N et al (2009) MetaboAnalyst: a web server for metabolomic data analysis and interpretation. Nucleic Acids Res 37:W652–W660

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  22. Madsen R, Lundstedt T, Trygg J (2010) Chemometrics in metabolomics–a review in human disease diagnosis. Anal Chim Acta 659:23–33

    Article  PubMed  CAS  Google Scholar 

  23. Jackson TE (1991) A user’s guide to principal components. Wiley, Hoboken, NJ

    Book  Google Scholar 

  24. Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chem Intel Lab Sys 58:109–130

    Article  CAS  Google Scholar 

  25. Webb A (2002) Statistical pattern recognition. Wiley, Hoboken, NJ

    Book  Google Scholar 

  26. Breiman L (2001) Random forests. Mach Learn 45:5–32

    Article  Google Scholar 

  27. Snee RD (1977) Validation of regression models: methods and examples. Technom 19:415–428

    Article  Google Scholar 

  28. Kennard RW, Stone LA (1969) Computer aided design of experiments. Technom 11:137–148

    Article  Google Scholar 

  29. Westerhuis JA, Hoefsloot HCJ, Smit S et al (2008) Assessment of PLSDA cross validation. Metabolomics 4:81–89

    Article  CAS  Google Scholar 

  30. Anderssen E, Dyrstad K, Westad F et al (2006) Reducing over-optimism in variable selection by cross-model validation. Chemo Intel Lab Sys 84:69–74

    Article  CAS  Google Scholar 

  31. Tomasi G, van den Berg F, Andersson C (2004) Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data. J Chemom 18: 231–241

    Article  CAS  Google Scholar 

  32. Bloemberg TG, Gerretzen J, Wouters HJP et al (2010) Improved parametric time warping for proteomics. Chemo Intel Lab Sys 104: 65–74

    Article  CAS  Google Scholar 

  33. Savorani F, Tomasi G, Engelsen SB (2010) Icoshift: a versatile tool for the rapid alignment of 1D NMR spectra. J Magn Reson 202: 190–202

    Article  PubMed  CAS  Google Scholar 

  34. Dong JY, Cheng KK, Xu JJ et al (2011) Group aggregating normalization method for the preprocessing of NMR-based metabolomic data. Chemo Intel Lab Sys 108:123–132

    Article  CAS  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Toxicology, Maastricht University, 616, Maastricht, MD, 6200, The Netherlands

    Jan W. Dallinga, Agnieszka Smolinska & Frederik-Jan van Schooten

Authors
  1. Jan W. Dallinga
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  2. Agnieszka Smolinska
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  3. Frederik-Jan van Schooten
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Corresponding author

Correspondence to Frederik-Jan van Schooten .

Editor information

Editors and Affiliations

  1. University of Washington Fred Hutchinson Cancer Rsrch Ctr, Seattle, Washington, USA

    Daniel Raftery

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© 2014 Springer Science+Business Media New York

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Dallinga, J.W., Smolinska, A., van Schooten, FJ. (2014). Analysis of Volatile Organic Compounds in Exhaled Breath by Gas Chromatography-Mass Spectrometry Combined with Chemometric Analysis. In: Raftery, D. (eds) Mass Spectrometry in Metabolomics. Methods in Molecular Biology, vol 1198. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-1258-2_16

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  • DOI: https://doi.org/10.1007/978-1-4939-1258-2_16

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-1257-5

  • Online ISBN: 978-1-4939-1258-2

  • eBook Packages: Springer Protocols

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