文献类型：期刊文献

英文题名：A parallel model of independent component analysis constrained by a 5-parameter reference curve and its solution by multi-target particle swarm optimization

作者：Cui, Lizhi[1,2];Ling, Zhihao[1];Poon, Josiah[2];Poon, Simon K.[2];Chen, Hao[2];Gao, Junbin[3];Kwan, Paul[4];Fan, Kei[2]

机构：[1]E China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[2]Univ Sydney, Sch Informat Technol, Sydney, NSW 2006, Australia;[3]Charles Sturt Univ, Sch Comp & Math, Bathurst, NSW 2795, Australia;[4]Univ New England, Sch Sci & Technol, Armidale, NSW 2350, Australia

年份：2014

卷号：6

期号：8

起止页码：2679

外文期刊名：ANALYTICAL METHODS

收录：;EI(收录号：20141417547319);WOS:【SCI-EXPANDED(收录号:WOS:000333524200034)】；

基金：Lizhi Cui thanks the School of Information Technologies, The University of Sydney for providing him with a PhD fellowship, thanks the Chinese Scholarship Council for providing financial support, the student's number is 201206740061, and thanks Professor R. Tauler for providing the HPLC-DAD dataset for experiments.

语种：英文

外文关键词：Chromatographic analysis - Curve fitting - Constrained optimization - Separation - Particle swarm optimization (PSO)

摘要：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.