Estimation of source infrared spectra profiles of acetylspiramycin active components from troches using kernel independent component analysis

Wang, Guoqing; Ding, Qingzhu; Sun, Yuan; He, Linghao; Sun, Xiaoli

doi:10.1016/j.saa.2007.07.051

Cited by 15 publications

(9 citation statements)

References 25 publications

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“…By looking at the morphology of the mass spectra, e.g. references [9] and [10], this assumption appears to hold in practice. It allows to reduce number of mixtures to N = 2, hence reducing the computational complexity of the used data clustering algorithm [23] by reducing dimension of the concentration subspaces, that equals average number of active components, to 1.…”

Section: Data Clusteringmentioning

confidence: 99%

Multi‐component analysis: blind extraction of pure components mass spectra using sparse component analysis

Kopriva

Jerić

2009

J. Mass Spectrom.

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The paper presents sparse component analysis (SCA)-based blind decomposition of the mixtures of mass spectra into pure components, wherein the number of mixtures is less than number of pure components. Standard solutions of the related blind source separation (BSS) problem that are published in the open literature require the number of mixtures to be greater than or equal to the unknown number of pure components. Specifically, we have demonstrated experimentally the capability of the SCA to blindly extract five pure components mass spectra from two mixtures only. Two approaches to SCA are tested: the first one based on 1 norm minimization implemented through linear programming and the second one implemented through multilayer hierarchical alternating least square nonnegative matrix factorization with sparseness constraints imposed on pure components spectra. In contrast to many existing blind decomposition methods no a priori information about the number of pure components is required. It is estimated from the mixtures using robust data clustering algorithm together with pure components concentration matrix. Proposed methodology can be implemented as a part of software packages used for the analysis of mass spectra and identification of chemical compounds.

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Section: Data Clusteringmentioning

confidence: 99%

Multi‐component analysis: blind extraction of pure components mass spectra using sparse component analysis

Kopriva

Jerić

2009

J. Mass Spectrom.

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“…Alternatives to library matching approach are blind decomposition methods, wherein pure components' spectra are extracted using mixtures spectra only. Blind approaches to pure components spectra extraction have been reported in NMR spectroscopy [1], infrared (IR) [2][3][4] and near infrared (NIR) spectroscopy [4][5][6], EPR spectroscopy [7,8], mass spectrometry [4, 9,1 0] Raman spectroscopy [11,12] etc. In a majority of blind decomposition schemes independent component analysis (ICA) [13][14][15] is employed to solve related blind source separation (BSS) problem.…”

Section: Introductionmentioning

confidence: 99%

“…The two requirements: to have more linearly independent mixtures than pure components and to have statistically independent pure components seem to be most critical for the success of the BSS approach to blind decomposition of the mixtures spectra into pure components spectra [4,5,8,10]. Statistical independence assumption is certainly not fulfilled in the case of IR spectra [2][3][4][5][6] because they are highly correlated i.e. overlapped.…”

Section: Introductionmentioning

confidence: 99%

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Blind decomposition of infrared spectra using flexible component analysis

Kopriva¹,

Jerić

Cichocki

2009

Chemometrics and Intelligent Laboratory Systems

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The paper presents flexible component analysis-based blind decomposition of the mixtures of Fourier transform of infrared spectral (FT-IR) data into pure components, wherein the number of mixtures is less than number of pure components. The novelty of the proposed approach to blind FT-IR spectra decomposition is in use of hierarchical or local alternating least square nonnegative matrix factorization (HALS NMF) method with smoothness and sparseness constraints simultaneously imposed on the pure components. In contrast to many existing blind decomposition methods no a priori information about the number of pure components is required. It is estimated from the mixtures using robust data clustering algorithm in the wavelet domain. The HALS NMF method is compared favorably against 2 three sparse component analysis algorithms on experimental data with the known pure component spectra. Proposed methodology can be implemented as a part of software packages used for the analysis of FT-IR spectra and identification of chemical compounds.

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“…However, when feature parameters are extracted from essentially non-linear AE parameters by the ICA method, it is possible that comprehensive feature parameters cannot be obtained and useful information can be lost as well. As a result, kernel ICA (KICA) with strongly non-linear performance is applied gradually [3,4].…”

Section: Introductionmentioning

confidence: 99%

Damage localization in hydraulic turbine blades using kernel-independent component analysis and support vector machines

Wang

Mao

Zhu

et al. 2008

Proceedings of the Institution of Mechanical Engineers, Part C:

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A hydraulic turbine runner has a complex structure, and traditional source location methods do not have the higher accuracy to meet engineering requirements. The source location of crack acoustic emission (AE) signals in hydraulic turbine blades has been researched by combining it with kernel-independent component analysis (KICA) as feature extraction, with support vector machines (SVMs) as position recognition. This method is compared with those applied SVMs with feature extraction using kernel principal components analysis without feature extraction. The results show that the recognition rate in the crack region is 100 per cent by using both original AE parameters and feature parameters. Support vector regression by feature extraction using KICA can perform better than the other methods. As a result, it is a better method for source location of complex big size structures to combine KICA with SVM. It decreases the dimensionality of input signals and also improves the accuracy of location.

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Estimation of source infrared spectra profiles of acetylspiramycin active components from troches using kernel independent component analysis

Cited by 15 publications

References 25 publications

Multi‐component analysis: blind extraction of pure components mass spectra using sparse component analysis

Multi‐component analysis: blind extraction of pure components mass spectra using sparse component analysis

Blind decomposition of infrared spectra using flexible component analysis

Damage localization in hydraulic turbine blades using kernel-independent component analysis and support vector machines

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