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ScientificWorldJournal. 2014 Jan 12;2014:767018. doi: 10.1155/2014/767018. eCollection 2014.

The application of multiobjective genetic algorithm to the parameter optimization of single-well potential stochastic resonance algorithm aimed at simultaneous determination of multiple weak chromatographic peaks.

Author information

  • 1Department of Pharmacy, College of Pharmacy, Nanjing University of Chinese Medicine, No. 138 Xianlin Avenue, Nanjing 210023, China.
  • 2Nanjing Changao Pharmaceutical Technology Limited, No. 1 Hengfei Road, Economic and Technological Development Zone, Nanjing 210038, China.
  • 3Center for Instrumental Analysis, China Pharmaceutical University, No. 24 Tongjiaxiang, Nanjing 210009, China.
  • 4Zhongda Hospital Affiliated to Southeast University, Nanjing 210009, China.
  • 5Department of Engineering and Technology, Jiangsu Institute of Economic and Trade Technology, Nanjing 210007, China.

Abstract

Simultaneous determination of multiple weak chromatographic peaks via stochastic resonance algorithm attracts much attention in recent years. However, the optimization of the parameters is complicated and time consuming, although the single-well potential stochastic resonance algorithm (SSRA) has already reduced the number of parameters to only one and simplified the process significantly. Even worse, it is often difficult to keep amplified peaks with beautiful peak shape. Therefore, multiobjective genetic algorithm was employed to optimize the parameter of SSRA for multiple optimization objectives (i.e., S/N and peak shape) and multiple chromatographic peaks. The applicability of the proposed method was evaluated with an experimental data set of Sudan dyes, and the results showed an excellent quantitative relationship between different concentrations and responses.

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