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https://hdl.handle.net/1959.11/16394
Title: | A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization | Contributor(s): | Cui, Lizhi (author); Ling, Zhihao (author); Poon, Josiah (author); Poon, Simon (author); Gao, Junbin (author); Kwan, Paul H (author) | Publication Date: | 2014 | Open Access: | Yes | DOI: | 10.1155/2014/276741 | Handle Link: | https://hdl.handle.net/1959.11/16394 | Abstract: | This paper proposes a separation method, based on the model of Generalized Reference Curve Measurement and the algorithm of Particle Swarm Optimization (GRCM-PSO), for the High Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD) data set. Firstly, initial parameters are generated to construct reference curves for the chromatogram peaks of the compounds based on its physical principle.Then, a General ReferenceCurveMeasurement (GRCM)model is designed to transform these parameters to scalar values, which indicate the fitness for all parameters. Thirdly, rough solutions are found by searching individual target for every parameter, and reinitialization only around these rough solutions is executed. Then, the Particle Swarm Optimization (PSO) algorithm is adopted to obtain the optimal parameters by minimizing the fitness of these new parameters given by the GRCM model. Finally, spectra for the compounds are estimated based on the optimal parameters and the HPLC-DAD data set. Through simulations and experiments, following conclusions are drawn: (1) the GRCM-PSO method can separate the chromatogram peaks and spectra from the HPLC-DAD data set without knowing the number of the compounds in advance even when severe overlap and white noise exist; (2) the GRCM-PSO method is able to handle the real HPLC-DAD data set. | Publication Type: | Journal Article | Source of Publication: | Applied Computational Intelligence and Soft Computing, v.2014, p. 1-10 | Publisher: | Hindawi Publishing Corporation | Place of Publication: | United States of America | ISSN: | 1687-9732 1687-9724 |
Fields of Research (FoR) 2008: | 080205 Numerical Computation 030101 Analytical Spectrometry 080108 Neural, Evolutionary and Fuzzy Computation |
Fields of Research (FoR) 2020: | 461304 Concurrency theory 340101 Analytical spectrometry 460203 Evolutionary computation |
Socio-Economic Objective (SEO) 2008: | 970108 Expanding Knowledge in the Information and Computing Sciences 890201 Application Software Packages (excl. Computer Games) 970103 Expanding Knowledge in the Chemical Sciences |
Socio-Economic Objective (SEO) 2020: | 280115 Expanding knowledge in the information and computing sciences 220401 Application software packages 280105 Expanding knowledge in the chemical sciences |
Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Journal Article |
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