Please use this identifier to cite or link to this item: 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: 10.1155/2014/276741Open Access Link
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
Appears in Collections:Journal Article

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