文献类型：期刊文献

英文题名：Generalized Gaussian reference curve measurement model for high-performance liquid chromatography with diode array detector separation and its solution by multi-target intermittent 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, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, 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：2015

卷号：29

期号：3

起止页码：146

外文期刊名：JOURNAL OF CHEMOMETRICS

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000351534900002)】；

基金：Lizhi Cui would like to thank school of information technologies and the University of Sydney for providing me with a PhD fellowship. Lizhi Cui would like to thank the Chinese Scholarship Council for their financial support (the student number is 201206740061). The authors also would like to thank the reviewers for their constructive comments and helpful suggestions.

语种：英文

外文关键词：HPLC-DAD data set separation; generalized Gaussian reference curve measurement model; multi-target intermittent particle swarm optimization

摘要：In order to separate a high-performance liquid chromatography with diode array detector (HPLC-DAD) data set to chromatogram peaks and spectra for all compounds, a separation method based on the model of generalized Gaussian reference curve measurement (GGRCM) and the algorithm of multi-target intermittent particle swarm optimization (MIPSO) is proposed in this paper. A parameter is constructed to generate a reference curve r() for a chromatogram peak based on its physical principle. The GGRCM model is proposed to calculate the fitness epsilon() for every , which indicates the possibility for the HPLC-DAD data set to contain a chromatogram peak similar to the r(). The smaller the fitness is, the higher the possibility. The algorithm of MIPSO is then introduced to calculate the optimal parameters by minimizing the fitness mentioned earlier. Finally, chromatogram peaks are constructed based on these optimal parameters, and the spectra are calculated by an estimator. Through the simulations and experiments, the following conclusions are drawn: (i) the GGRCM-MIPSO method can extract chromatogram peaks from simulation data set without knowing the number of the compounds in advance even when a severe overlap and white noise exist and (ii) the GGRCM-MIPSO method can be applied to the real HPLC-DAD data set. Copyright (c) 2014 John Wiley & Sons, Ltd. In this paper, a separation method based on generalized Gaussian reference curve measurement (GGRCM) model and multi-target intermittent particle swarm optimization algorithm is proposed. A parameter is constructed to generate a reference curve r(). The generalized Gaussian reference curve measurement model is proposed to calculate the fitness for every parameter . The multi-target intermittent particle swarm optimization algorithm is adopted to calculate the optimal parameter by minimizing the fitness mentioned earlier.