A decomposition model for HPLC-DAD data set and its solution by particle swarm optimization

Lizhi Cui, Zhihao Ling, Josiah Poon, Simon K. Poon, Junbin Gao, Wing Hing Paul Kwan

Research output: Contribution to journalArticlepeer-review

10 Downloads (Pure)


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 Reference Curve Measurement (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.
Original languageEnglish
Article number276741
Pages (from-to)1-10
Number of pages10
JournalApplied Computational Intelligence and Soft Computing
Publication statusPublished - 2014


Dive into the research topics of 'A decomposition model for HPLC-DAD data set and its solution by particle swarm optimization'. Together they form a unique fingerprint.

Cite this