Separation model of Generalized Reference Curve Measurement for HPLC-DAD and it solution by multi-target Bare Bones Particle Swarm Optimization

In order to separate the chromatogram peaks and spectra from the High Performance Liquid Chromatography with Diode Array Detector (HPLC-DAD) data set, a separation model of Generalized Reference Curve Measurement and its solution by multitarget Bare Bones Particle Swarm Optimization (GRCMmBBPSO) is proposed in this paper. Firstly, parameters are constructed which will generate Reference Curves (RCs) for chromatogram peaks. Secondly, the GRCM model is proposed to transform all these parameters to scalar values, which indicate the possibility for the HPLC-DAD data set containing chromatogram peaks similar to the RCs constructed by corresponding parameters. Then, the algorithm of mBBPSO is introduced to calculate the optimal parameters by minimizing the scalar values. Finally, the spectra are estimated. Through simulations and experiments, following conclusions are drawn: (1) the GRCMmBBPSO method can extract chromatogram peaks from the simulation data set without knowing the number of the compounds in advance even when a severe overlap and white noise exist; (2) the GRCMmBBPSO method can be applied to real HPLC-DAD data set.

[1]  Li Sun,et al.  Immune algorithms in analytical chemistry , 2003 .

[2]  H. R. Keller,et al.  Peak purity control in liquid chromatography with photodiode-array detection by a fixed size moving window evolving factor analysis , 1991 .

[3]  Junbin Gao,et al.  Parallel model of independent component analysis constrained by reference curves for HPLC-DAD and its solution by multi-areas genetic algorithm , 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.

[4]  Hao Chen,et al.  A parallel model of independent component analysis constrained by a 5-parameter reference curve and its solution by multi-target particle swarm optimization , 2014 .

[5]  Benjamin Debrus,et al.  A new statistical method for the automated detection of peaks in UV-DAD chromatograms of a sample mixture. , 2009, Talanta.

[6]  Riccardo Poli,et al.  Particle Swarm Optimisation , 2011 .

[7]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[8]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[9]  Marcel Maeder,et al.  Evolving factor analysis, a new multivariate technique in chromatography , 1988 .

[10]  Edmund R. Malinowski,et al.  Theory of evolutionary factor analysis for resolution of ternary mixtures , 1990 .

[11]  James Kennedy,et al.  Bare bones particle swarms , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[12]  Yi-Zeng Liang,et al.  Diagnosis and resolution of multiwavelength chromatograms by rank map, orthogonal projections and sequential rank analysis , 1994 .

[13]  Romà Tauler,et al.  Application of multivariate self-modeling curve resolution to the quantitation of trace levels of organophosphorus pesticides in natural waters from interlaboratory studies , 1996 .

[14]  Romà Tauler,et al.  Exploratory data analysis of DNA microarrays by multivariate curve resolution. , 2006, Analytical biochemistry.