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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Phillips M, Altorki N, Austin JHM et al (2005) Prediction of lung cancer using volatile biomarkers in breath. J Clin Oncol 23:839S
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
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
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
Krzanowski WJ (2000) Principles of multivariate analysis (rev. ed). Oxford, New York, NY
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
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
Lommen A (2009) MetAlign: Interface-driven, versatile metabolomics tool for hyphenated full-scan mass spectrometry data preprocessing. Anal Chem 81:3079–3086
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
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
Leco ChromaTOF. http;//leco.com/products/separation-science/software-accessoires/chromatof-software
Eilers PHC, Marx BD (1996) Flexible smoothing with B-splines and penalties. Stat Sci 11:89–102
Eilers PHC (2003) A perfect smoother. Anal Chem 75:3631–3636
Eilers PHC, Currie ID, Durban M (2006) Fast and compact smoothing on large multidimensional grids. Comput Stat Data Anal 50:61–76
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
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
Torgrip RJO, Aberg KM, Alm E et al (2008) A note on normalization of biofluid 1D H-1-NMR data. Metabolomics 4:114–121
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
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
Eriksson L, Johansson E, Kettaneh-Wold N, et al. (2006) Multi- and megavariate data analysis (2nd rev. ed). Umetrics AB, Umeå, Sweden
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
Madsen R, Lundstedt T, Trygg J (2010) Chemometrics in metabolomics–a review in human disease diagnosis. Anal Chim Acta 659:23–33
Jackson TE (1991) A user’s guide to principal components. Wiley, Hoboken, NJ
Wold S, Sjostrom M, Eriksson L (2001) PLS-regression: a basic tool of chemometrics. Chem Intel Lab Sys 58:109–130
Webb A (2002) Statistical pattern recognition. Wiley, Hoboken, NJ
Breiman L (2001) Random forests. Mach Learn 45:5–32
Snee RD (1977) Validation of regression models: methods and examples. Technom 19:415–428
Kennard RW, Stone LA (1969) Computer aided design of experiments. Technom 11:137–148
Westerhuis JA, Hoefsloot HCJ, Smit S et al (2008) Assessment of PLSDA cross validation. Metabolomics 4:81–89
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
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
Bloemberg TG, Gerretzen J, Wouters HJP et al (2010) Improved parametric time warping for proteomics. Chemo Intel Lab Sys 104: 65–74
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
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this protocol
Cite this protocol
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
Download citation
DOI: https://doi.org/10.1007/978-1-4939-1258-2_16
Published:
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