Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/15095
Title: A parallel model of independent component analysis constrained by a 5-parameter reference curve and its solution by multi-target particle swarm optimization
Contributor(s): Cui, Lizhi (author); Ling, Zhihao (author); Poon, Josiah (author); Poon, Simon (author); Chen, Hao (author); Gao, Junbin (author); Kwan, Paul H  (author); Fan, Kei (author)
Publication Date: 2014
DOI: 10.1039/c3ay42196a
Handle Link: https://hdl.handle.net/1959.11/15095
Abstract: The separation technologies of 3D chromatograms have been researched for a long time to obtain spectra and chromatogram peaks for individual compounds. However, before applying most of the current methods, the number of compounds must be known in advance. Independent Component Analysis (ICA) is applied to separate 3D chromatograms without knowing the compounds' number in advance, but the existence of the noise component in the results makes it complex for computation. In this paper, a parallel model of Independent Component Analysis constrained by a 5-parameter Reference Curve (pICA5pRC) is proposed based on the ICA model. Introducing a priori knowledge from chromatogram peaks, the pICA5pRC model transformed the 3D chromatogram separation problem to a 5 parameters optimization issue. An algorithm named multi-target particle swarm optimization (mPSO) has been developed to solve the pICA5pRC model. Through simulations, the performance and explanation of our method were described. Through experiments, the practicability of our method is validated. The results show that: (1) our method could separate 3D chromatograms efficiently even with severe overlap without knowing the compounds' number in advance; (2) our method extracted chromatogram peaks from the dataset directly without noise components; (3) our method could be applied to the practical HPLC-DAD dataset.
Publication Type: Journal Article
Source of Publication: Analytical Methods, 6(8), p. 2679-2686
Publisher: RSC Publications
Place of Publication: United Kingdom
ISSN: 1759-9679
1759-9660
Fields of Research (FoR) 2008: 030101 Analytical Spectrometry
010303 Optimisation
080108 Neural, Evolutionary and Fuzzy Computation
Fields of Research (FoR) 2020: 340101 Analytical spectrometry
490304 Optimisation
460203 Evolutionary computation
Socio-Economic Objective (SEO) 2008: 970103 Expanding Knowledge in the Chemical Sciences
970108 Expanding Knowledge in the Information and Computing Sciences
970101 Expanding Knowledge in the Mathematical Sciences
Socio-Economic Objective (SEO) 2020: 280105 Expanding knowledge in the chemical sciences
280115 Expanding knowledge in the information and computing sciences
280118 Expanding knowledge in the mathematical sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article

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