Generalized Gaussian reference curve measurement model for high-performance liquid chromatography with diode array detector separation and its solution by multi-target intermittent particle swarm optimization

Lizhi Cui, Zhihao Ling, Josiah Poon, Simon Poon, Hao Chen, Junbin Gao, Paul Kwan, Kei Fan

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

In order to separate a high-performance liquid chromatography with diode array detector (HPLC-DAD) data set to chromatogram peaks and spectra for all compounds, a separation method based on the model of generalized Gaussian reference curve measurement (GGRCM) and the algorithm of multi-target intermittent particle swarm optimization (MIPSO) is proposed in this paper. A parameter ? is constructed to generate a reference curve r(?) for a chromatogram peak based on its physical principle. The GGRCM model is proposed to calculate the fitness e(?) for every ?, which indicates the possibility for the HPLC-DAD data set to contain a chromatogram peak similar to the r(?). The smaller the fitness is, the higher the possibility. The algorithm of MIPSO is then introduced to calculate the optimal parameters by minimizing the fitness mentioned earlier. Finally, chromatogram peaks are constructed based on these optimal parameters, and the spectra are calculated by an estimator. Through the simulations and experiments, the following conclusions are drawn: (i) the GGRCM-MIPSO method can extract chromatogram peaks from simulation data set without knowing the number of the compounds in advance even when a severe overlap and white noise exist and (ii) the GGRCM-MIPSO method can be applied to the real HPLC-DAD data set.
Original languageEnglish
Pages (from-to)146-153
Number of pages8
JournalJournal of Chemometrics
Volume29
Issue number3
DOIs
Publication statusPublished - Mar 2015

Fingerprint Dive into the research topics of 'Generalized Gaussian reference curve measurement model for high-performance liquid chromatography with diode array detector separation and its solution by multi-target intermittent particle swarm optimization'. Together they form a unique fingerprint.

Cite this