Blind source separation in diffuse reflectance NIR spectroscopy using independent component analysis

Journal of Chemometrics

Volumn 24, Issue 7-8, 2010, Pages 514-522

  Toiviainen, M ^a   Corona, F ^b   Paaso, J ^a   Teppola, P ^a  

^a VTT TECHNICAL RESEARCH CENTRE OF FINLAND   (Finland)

^b AALTO UNIVERSITY   (Finland)

Author keywords

Blind source separation; Independent component analysis; Near infrared spectroscopy; Spectral preprocessing

Indexed keywords

BLIND SOURCE SEPARATION; INFRARED DEVICES; NEAR INFRARED SPECTROSCOPY; PARTICLE SIZE; PARTICLE SIZE ANALYSIS; REFLECTION; SPECTRUM ANALYSIS;

CONTACT ANALYSIS; DIFFUSE REFLECTANCE; DIFFUSE REFLECTANCE NEAR-INFRARED SPECTROSCOPY; INDEPENDENT COMPONENTS ANALYSIS; NEAR INFRARED SPECTRA; NON-CONTACT; SEPARATION METHODS; SOLID SAMPLES; SPECTRAL PREPROCESSING;

INDEPENDENT COMPONENT ANALYSIS;

EID: 77957070205     PISSN: 08869383     EISSN: 1099128X     Source Type: Journal    
DOI: 10.1002/cem.1316     Document Type: Article

References (21)

1. Workman JJ Jr. Review of process and non-invasive near-infrared and infrared spectroscopy: 1993-1999. Appl. Spectrosc. Rev. 1999; 34: 1-89.
2. de Juan A, Tauler R. Multivariate curve resolution (MCR) from 2000: progress in concepts and applications. Crit. Rev. Analyt. Chem. 2006; 36: 163-176.
3. Wang G, Ding Q, Hou Z. Independent component analysis and its applications in signal processing for analytical chemistry. Trends Analyt. Chem. 2008; 27: 368-376.
4. Paatero P, Hopke PK, Song XH, Ramadan Z. Understanding and controlling rotations in factor analytic models. Chemom. Intell. Lab. Syst. 2002; 60: 253-264.
5. Widjaja E, Garland M. Pure component spectral reconstruction from mixture data using SVD, global entropy minimization, and simulated annealing. Numerical investigations of admissible objective functions using a synthetic 7-species data set. J. Comput. Chem. 2002; 23: 911-919.
6. Visser E, Lee TW. An information-theoretic methodology for the resolution of pure component spectra without prior information using spectroscopic measurements. Chemom. Intell. Lab. Syst. 2004; 70: 147-155.
7. Hyvärinen A, Oja E. Independent component analysis: algorithms and applications. Neural Netw. 2000; 13: 411-430.
8. Bro R, Acar E, Kolda TG. Resolving the sign ambiguity in the singular value decomposition. J. Chemom. 2008; 22: 135-140.
9. Martens H, Nielsen JP, Engelsen SB. Light scattering and light absorbance separated by extended multiplicative signal correction. Application to near-infrared transmission analysis of powder mixtures. Analyt. Chem. 2003; 75: 394-404.
10. Chen ZP, Morris J, Martin E. Extracting chemical information from spectral data with multiplicative light scattering effects by optical path-length estimation and correction. Analyt. Chem. 2006; 78: 7674-7681.
11. Thennadil SN, Martin EB. Empirical preprocessing methods and their impact on NIR calibrations: a simulation study. J. Chemom. 2005; 19: 77-89.
12. Kohler A, Sulé-Suso J, Sockalingum GD, Tobin M, Bahrami F, Yang Y, Pijanka J, Dumas P, Cotte M, van Pittius DG, Parkes G, Martens H. Estimating and correctingMie scattering in synchrotron-based microscopic Fourier transform infrared spectra by extended multiplicative signal correction. Appl. Spectrosc. 2008; 62: 259-266.
13. Chen J, Wang XZ. A new approach to near-infrared spectral data analysis using independent component analysis. J. Chem. Inf. Comput. Sci. 2001; 41: 992-1001.
14. Pasadakis N, Kardamakis AA. Identifying constituents in commercial gasoline using Fourier transform-infrared spectroscopy and independent component analysis. Anal. Chim. Acta 2006; 578: 250-255.
15. Astakhov SA, Stögbauer H, Kraskov A, Grassberger P. Spectral mixture decomposition by least dependent component analysis. Arxiv preprint physics/0412029 2004.
16. Hyvärinen A. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. Neural Netw. 1999; 10: 626-634.
17. The FastICA package for MATLAB. http://www.cis.hut.fi/projects/ica/fastica/index.shtml [23 February 2010].
18. Särelä J, Valpola H. Denoising source separation. J. Mach. Learn. Res. 2005; 6: 233-272.
19. The DSS package for MATLAB. http://www.cis.hut.fi/projects/dss/package/ [23 February 2010].
20. Martens H, Naes T. Multivariate Calibration (2nd edn), vol. 1. Wiley: Chichester, UK, 1989.
21. Brown CD, Green RL. Critical factors limiting the interpretation of regression vectors in multivariate calibration. Trends Analyt. Chem. 2009; 28: 506-514.