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Chromatograms separation using Matrix decomposition

by S.Anbumalar, P.Rameshbabu, R.Anandanatarajan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 27 - Number 3
Year of Publication: 2011
Authors: S.Anbumalar, P.Rameshbabu, R.Anandanatarajan
10.5120/3281-4469

S.Anbumalar, P.Rameshbabu, R.Anandanatarajan . Chromatograms separation using Matrix decomposition. International Journal of Computer Applications. 27, 3 ( August 2011), 24-32. DOI=10.5120/3281-4469

@article{ 10.5120/3281-4469,
author = { S.Anbumalar, P.Rameshbabu, R.Anandanatarajan },
title = { Chromatograms separation using Matrix decomposition },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 27 },
number = { 3 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 24-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume27/number3/3281-4469/ },
doi = { 10.5120/3281-4469 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:12:51.007488+05:30
%A S.Anbumalar
%A P.Rameshbabu
%A R.Anandanatarajan
%T Chromatograms separation using Matrix decomposition
%J International Journal of Computer Applications
%@ 0975-8887
%V 27
%N 3
%P 24-32
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Non – negative matrix factorization (NMF) was generally used to obtain representation of data using non – negativity constraints. It lead to parts – based (or) region based representation in the vector space because they allow only additive combinations of original data. NMF has been applied so far in image and text data analysis, audio signal separation, signal separation in bio-medical applications and spectral resolution. The original Lee and Seung ‘s NMF has to be modified for chemical analysis, based on the characteristics of that signal. In this paper, sparse NMF (sNMF) has been used for the deconvolution of overlapping chromatograms of chemical mixture. Before applying sNMF, the number of components in mixture was determined using Principal Component Analysis (PCA). The experimental overlapping chromatograms were obtained using Gas Chromatography –Flame Ionization Detector (GC-FID) for the chemical mixture of acetone and acrolein and they have been soundly resolved by sNMF algorithm. The proposed algorithm has also been tested with simulated two, three and four component chromatograms of severely overlapped and embedded peaks. Even though there are three or four components, the results are encouraging. The correlation coefficient is greater than 0.99 and signal to noise ratio is greater than 29dB always.

References
  1. Gampp, H., Maeder, M., Meyer, C.J., and Zuberbuhler, A.D. “Calculation of equilibrium constants from multiwavelength spectroscopic data. III. Model-free analysis of spectrophotometric and ESR titrations”, Talanta, 32 (1985)1133-1139.
  2. Maeder, M. “Evolving Factor Analysis for the resolution of overlapping chromatographic peaks”, Anal. Chem., 59 (1987) 527-530 .
  3. Keller,H.R., and Massart, D.L. “Peak purity control in liquid chromatography with photodiode array detection by fixed size moving window evolving factor analysis”, Anal. Chim. Acta, 246(1991) 379-390.
  4. Windig, W., and Guilment, J. “Interactive self-modeling mixture analysis”, Anal. Chem., 63(1991) 1425-1432.
  5. Tauler, R., Smilde, A.K., and Kowalski, B.R. “Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution”, J. Chemometr. 9(1995) 31-58.
  6. Tauler, R. “Multivariate curve resolution applied to second order data”, Chemom. Intell. Lab. Sys., 30(1995)133-146.
  7. Tauler, R., Lacorte, S., and Barcelo, D. “Application of multivariate curve self-modeling curve resolution for the quantitation of trace levels of organophosphorous pesticides in natural waters from interlaboratory studies”, J. of Chromatogr. A, 730(1996)177-183 .
  8. Gemperline, P.J. “A priori estimates of the elution profiles of the pure components in overlapped liquid chromatography peaks using target factor analysis”, J. Chem. Inf. Comput. Sci. 24 (1984) 206-212.
  9. Vandeginste, B.G.M., Derks, W., and Kateman, G. “Multicomponent self-modelling curve resolution in high-performance liquid chromatography by iterative target transformation analysis”, Anal. Chim. Acta, 173 (1985) 253-264.
  10. Kvalheim, O.M., and Liang, Y.Z. “Heuristic evolving latent projections: resolving two-way multicomponent data. 1. Selectivity, latent-projective graph, datascope, local rank, and unique resolution”, Anal. Chem., 64 (1992) 936-946.
  11. Liang, Y.Z, Kvalheim, O.M., Keller,H.R., Massart, D.L, Kiechle, P., and Erni, F. “Heuristic evolving latent projections: resolving two-way multicomponent data. 2. Detection and resolution of minor constituents”, Anal. Chem., 64 (1992) 946-953.
  12. Van Zomeren, P.V., Darwinkel, H., Coenegracht, P. M. J., and de Jong, G.J. “Comparison of several curve resolution methods for drug impurity profiling using high-performance liquid chromatography with diode array detection”, Anal. Chim. Acta, 487(2003)155–170.
  13. Lee, DD and Seung, H. “Learning the parts of objects by non-negative matrix factorization”, Nature, 401(1999) 788-791.
  14. Lee, DD. and Seung, H. “Algorithms for non-negative matrix factorization”, Adv. Neural Inf. Process. Syst., 13 (2001) 556-562.
  15. Hoyer, P. O. “Non-negative Matrix Factorization with Sparseness Constraints”, Journal of Machine Learning Research, 5 (2004) 1457-1469.
  16. Li, S. Z., Hou, X. W., Zhang, H. J., and Cheng, Q. S. “Learning Spatially Localized, Parts- based Representation”, International Conference on Computer Vision and Pattern Recognition, (2001) 207- 212.
  17. Zafeiriou,S., Tefas,A., Buciu, I., and Pitas, I. “Exploiting Discriminant Information in Nonnegative Matrix Factorization with Application to Frontal Face Verification”, IEEE Trans. on Neural Networks, 17(3) (2006) 683-695.
  18. Buciu, I., and Pitas, I. “A New Sparse Image Representation Algorithm Applied to Facial Expression Recognition”, IEEE Workshop on Machine Learning for Signal Processing, (2004) 539- 548.
  19. Schmidt, M. N. and Morup, M. “Nonnegative Matrix Factor 2- D deconvolution for blind single channel source separation, Independent Component Analysis and Blind Signal Separation” Lecture Notes in Computer Science, 3889/2006 (2006) 700-707.
  20. Hong-Tao Gao, Tong-Hua Li, Kai Chen, Wei-Guang Li, and Xian Bi ,”Overlapping spectra resolution using non-negative matrix factorization”, Talanta , 66 (2005) 65– 73.
  21. Liu Mingyu , Ji Hongbing, and Zhao Chunhong, Non negative Matrix Factorization and Its Application in EEG Signal Processing, IEEE Xplore, 978-1-4244-1748-3/08.
  22. Li, H., Adali, T., Wang, W., Emge., D., and Cichocki, A. “Non-negative matrix factorization with orthogonality constraints and its application to Raman spectroscopy”, J. of VLSI Signal Processing, 48 (2007) 83–97.
  23. Shin-Do Kim, Chang-Hwan Kim, Jin-Su Park, Jeong-Joo Lee, “A Study on the Peak Separation of Acetone and Acrolein based on High-Performance Liquid Chromatography (HPLC) Method”, Bull. Korean Chem. Soc. 30 (2009) 2011-2016.
  24. Yuan, Z., and Oja, E “Projective nonnegative matrix factorization for image compression and feature extraction”, 14th Scandinavian Conference on Image Analysis, (2005) 333–342.
  25. Morup, M., Madsen, K.H. and Hansen, L.K. “Shifted Non-negative Matrix Factorization”, IEEE Workshop on Machine Learning for Signal Processing, 2007.
  26. Bucak, S.S., Gunsel.B., and Gursoy, O.”Incremental non-negative matrix factorization for dynamic background modeling”, ICEIS 8th International Workshop on Pattern Recognition in Information Systems, 2007.
  27. Morup, M., Hansen, L.K., and Arnfred, S.M. “Algorithms for sparse higher order non-negative matrix factorization(HONMF)”, Technical Report, 2006
  28. Olshausen, B.A., and Field, D.J. “Emergence of simple-cell receptive field properties by learning a sparse code for natural images” , Nature, 381(1996)607-609.
  29. Boutsidis. C., and Gallopoulos, E., “On SVD-based initialization for nonnegative Matrix factorization”, Tech. Report, HPCLAB-SCG-6/08-05, University of Patras, Patras, Greece, 2005.
Index Terms

Computer Science
Information Sciences

Keywords

sparse Non-negative Matrix Factorization (sNMF) Principal Component Analysis (PCA) resolution overlapping chromatograms GC– FID acetone acrolein mixture

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