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Journal of Analysis and Testing

, Volume 2, Issue 3, pp 235–248 | Cite as

Comparison of Principal Components Analysis, Independent Components Analysis and Common Components Analysis

  • Douglas N. Rutledge
Original Paper
  • 178 Downloads

Abstract

The aim of this work is to describe and compare three exploratory chemometrical tools, principal components analysis, independent components analysis and common components analysis, the last one being a modification of the multi-block statistical method known as common components and specific weights analysis. The three methods were applied to a set of data to show the differences and similarities of the results obtained, highlighting their complementarity.

Keywords

Exploratory data analysis Chemometrics Principal components analysis Independent components analysis Common components analysis Common components and specific weights analysis 

References

  1. 1.
    Wold S, Esbensen K, Geladi P. Principal component analysis. Chemom Intell Lab Syst. 1987;2:37–52.CrossRefGoogle Scholar
  2. 2.
    Rutledge DN, Bouveresse JRD. Independent components analysis with the JADE algorithm. Trac-Trends Anal Chem. 2013;50:22–32.CrossRefGoogle Scholar
  3. 3.
    Rutledge DN, Jouan-Rimbaud Bouveresse D. Corrigendum to “Independent components analysis with the JADE algorithm”. Trac-Trends Anal Chem. 2015;67:220.CrossRefGoogle Scholar
  4. 4.
    Wang GQ, Ding QZ, Hou ZY. Independent component analysis and its applications in signal processing for analytical chemistry. Trac- Trends Anal Chem. 2008;27:368–76.CrossRefGoogle Scholar
  5. 5.
    Bouhlel J, Bouveresse DJR, Abouelkaram S, Baéza E, Jondreville C, Travel A, Ratel J, Engel E, Rutledge DN. Comparison of common components analysis with principal components analysis and independent components analysis: application to SPME-GC-MS volatolomic signatures. Talanta. 2018;178:854–63.CrossRefGoogle Scholar
  6. 6.
    Cardoso J-F, Souloumiac A. Blind beamforming for non-Gaussian signals. IEE ProcF. 1993;140(6):362–70.Google Scholar
  7. 7.
    Jouan-Rimbaud Bouveresse D, Rutledge DN. Independent components analysis: theory and applications. In: Ruckebusch C, editor. Resolving spectral mixtures. Amsterdam: Elsevier; 2017. p. 225–78.Google Scholar
  8. 8.
    Qannari E, Wakeling I, Courcoux P, MacFie HJH. Defining the underlying sensory dimensions. Food Qual Prefer. 2000;11:151–4.CrossRefGoogle Scholar
  9. 9.
    Cariou V, Jouan-Rimbaud Bouveresse D, Qannari EM, Rutledge DN. ComDim methods for the analysis of multiblock data in a data fusion perspective. In: Cocchi M, editor. Data fusion methodology and applications (Data Handling in Science and Technology). Amsterdam: Elsevier; 2018 (in press).Google Scholar
  10. 10.
    Mazerolles G, Devaux MF, Dufour E, Qannari EM, Courcoux P. Chemom Intell Lab Syst. 2002;63:57–68.CrossRefGoogle Scholar
  11. 11.
    Blackman J, Rutledge DN, Tesic D, Saliba A, Scollary GR. Anal Chim Acta. 2010;660:2.CrossRefGoogle Scholar
  12. 12.
    Martin JC, Maillot M, Mazerolles G, Verdu A, Lyan B, Migne C, Defoort C, Canlet C, Junot C, Guillou C, Manach C, Jabob D, Jouan-Rimbaud Bouveresse D, Paris E, Pujos-Guillot E, Jourdan F, Giacomoni F, Courant F, Fave G, Le Gall G, Chassaigne H, Tabet JC, Martin JF, Antignac JP, Shintu L, Defernez M, Philo M, Alexandre-Gouaubau MC, Amiot-Carlin MJ, Bossis M, Triba MN, Stojilkovic N, Banzet N, Molinie R, Bott R, Goulitquer S, Caldarelli S, Rutledge DN. Can we trust untargeted metabolomics? Metabolomics. 2015;11:807–21.CrossRefGoogle Scholar
  13. 13.
    Dubin E, Spiteri M, Dumas AS, Ginet J, Lees M, Rutledge DN. Chemom Intell Lab Syst. 2016;150:41–50.CrossRefGoogle Scholar
  14. 14.
    Rutledge DN. Characterisation of water in agro-food products by time domain-NMR. Food Control. 2001;12:437–45.CrossRefGoogle Scholar
  15. 15.
    Jouan-Rimbaud Bouveresse D, Moya-González A, Ammari F, Rutledge DN. Two novel methods for the determination of the number of components in independent components analysis models. Chemom Intell Lab Syst. 2012;112:24–32.CrossRefGoogle Scholar
  16. 16.
    Kassouf A, Jouan-Rimbaud Bouveresse D, Rutledge DN. Determination of the optimal number of components in independent components analysis. Talanta. 2018;179:538–45.CrossRefGoogle Scholar

Copyright information

© The Nonferrous Metals Society of China 2018

Authors and Affiliations

  1. 1.INRA, UMR GENIAL, AgroParisTech, Université Paris-SaclayMassyFrance

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