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Applied Computational Intelligence and Soft Computing
Volume 2014 (2014), Article ID 276741, 10 pages
http://dx.doi.org/10.1155/2014/276741
Research Article

A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization

1Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology, Shanghai 200237, China
2School of Information Technologies, The University of Sydney, Sydney, NSW 2006, Australia
3School of Computing and Mathematics, Charles Sturt University, Bathurst, NSW 2795, Australia
4School of Science and Technology, University of New England, Armidale, NSW 2350, Australia

Received 17 July 2014; Revised 1 November 2014; Accepted 1 November 2014; Published 25 November 2014

Academic Editor: Samuel Huang

Copyright © 2014 Lizhi Cui et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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 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.