A primary study on resolution of overlapping GC-MS signal using mean-field approach independent component analysis

Independent component analysis (ICA) has been found to be powerful to separate complex signals. However, chemical signals are generally correlated, instead of independent as hypothesized in ICA. In this study, mean-field independent component analysis (MF-ICA) was investigated to resolve the overlapping gas chromatographic-mass spectrometric (GC-MS) signal. In MF-ICA, the sources are estimated from the mean of their posterior distribution. The mixing matrix and noise level are found through the maximum a posterior (MAP) solution. By simulated signals, results show that for cases of the slightly correlated (or overlapped) sources, both the sources (MS) and mixing matrix (chromatogram) can be almost correctly estimated by specification of the nonnegative (positive) priors for the mixing matrix and sources. However, when the sources are highly correlated, no good results can be obtained, although acceptable estimated sources can be obtained somehow for database matching. For experimental overlapping GC-MS data, reasonable results are obtained, because MS spectra of different homologous compounds in GC-MS analysis of a mixture are not generally correlated very much. Therefore, ICA should be an alternate tool for resolution of overlapping chemical signals, although further works are still needed.

[1]  M. Opper,et al.  Tractable approximations for probabilistic models: the adaptive Thouless-Anderson-Palmer mean field approach. , 2001, Physical review letters.

[2]  Paul J. Gemperline,et al.  A priori estimates of the elution profiles of the pure components in overlapped liquid chromatography peaks using target factor analysis , 1984, J. Chem. Inf. Comput. Sci..

[3]  J. Cardoso.  Infomax and maximum likelihood for blind source separation , 1997, IEEE Signal Processing Letters.

[4]  ChangKyoo Yoo,et al.  Statistical Process Monitoring with Multivariate Exponentially Weighted Moving Average and Independent Component Analysis , 2003 .

[5]  T. Sejnowski,et al.  Dynamic Brain Sources of Visual Evoked Responses , 2002, Science.

[6]  Soo-Young Lee,et al.  FPGA implementation of ICA algorithm for blind signal separation and adaptive noise canceling , 2003, IEEE Trans. Neural Networks.

[7]  ChangKyoo Yoo,et al.  On-line monitoring of batch processes using multiway independent component analysis , 2004 .

[8]  J. Foley,et al.  Equations for chromatographic peak modeling and calculation of peak area , 1987 .

[9]  T. Sejnowski,et al.  Independent component analysis at the neural cocktail party , 2001, Trends in Neurosciences.

[10]  W. Windig,et al.  Interactive self-modeling mixture analysis , 1991 .

[11]  J. R. Torres-Lapasió,et al.  Resolution of multicomponent peaks by orthogonal projection approach, positive matrix factorization and alternating least squares , 2000 .

[12]  Manabu Kano,et al.  Monitoring independent components for fault detection , 2003 .

[13]  X. Shao,et al.  Quantitative determination of the components in overlapping chromatographic peaks using wavelet transform. , 1997, Analytical chemistry.

[14]  J. Cardoso,et al.  Blind beamforming for non-gaussian signals , 1993 .

[15]  Ole Winther,et al.  Mean-Field Approaches to Independent Component Analysis , 2002, Neural Computation.

[16]  Xueguang Shao,et al.  Quantitative Prediction of Normal Boiling Points for Organic Compounds Using Gas Chromatographic Retention Times on Two Columns of Different Polarity , 2004 .

[17]  Edmund R. Malinowski,et al.  Investigation of window factor analysis and matrix regression analysis in chromatography , 1993 .

[18]  Xueguang Shao,et al.  Resolution of multicomponent chromatograms by window factor analysis with wavelet transform preprocessing , 1998 .

[19]  Xueguang Shao,et al.  Determination of the component number in overlapping multicomponent chromatogram using wavelet transform , 1998 .

[20]  Aapo Hyvärinen,et al.  Gaussian moments for noisy independent component analysis , 1999, IEEE Signal Processing Letters.

[21]  Xueguang Shao,et al.  Extraction of mass spectra and chromatographic profiles from overlapping GC/MS signal with background. , 2004, Analytical chemistry.

[22]  Li Sun,et al.  Immune algorithms in analytical chemistry , 2003 .

[23]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[24]  X. Z. Wang,et al.  A New Approach to Near-Infrared Spectral Data Analysis Using Independent Component Analysis , 2001, J. Chem. Inf. Comput. Sci..

[25]  Te-Won Lee,et al.  An information-theoretic methodology for the resolution of pure component spectra without prior information using spectroscopic measurements , 2004 .

[26]  Terrence J. Sejnowski,et al.  An Information-Maximization Approach to Blind Separation and Blind Deconvolution , 1995, Neural Computation.

[27]  Alexander Kai-man Leung,et al.  Wavelet: a new trend in chemistry. , 2003, Accounts of chemical research.

[28]  W. Windig.  Spectral data files for self-modeling curve resolution with examples using the Simplisma approach , 1997 .

[29]  L Sun,et al.  A two-dimensional immune algorithm for resolution of overlapping two-way chromatograms , 2001, Fresenius' journal of analytical chemistry.

[30]  Yi-Zeng Liang,et al.  Subwindow factor analysis , 1999 .

[31]  Yizeng Liang,et al.  Heuristic evolving latent projections: resolving two-way multicomponent data. 1. Selectivity, latent-projective graph, datascope, local rank, and unique resolution , 1992 .