文献类型：期刊文献

英文题名：An improved independent component analysis model for 3D chromatogram separation and its solution by multi-areas genetic algorithm

作者：Cui, Lizhi[1,4];Poon, Josiah[1];Poon, Simon K.[1];Chen, Hao[1];Gao, Junbin[2];Kwan, Paul[3];Fan, Kei[1];Ling, Zhihao[4]

机构：[1]Univ Sydney, Sch Informat Technol, Sydney, NSW 2006, Australia;[2]Charles Sturt Univ, Sch Comp & Math, Bathurst, NSW 2795, Australia;[3]Univ New England, Sch Sci & Technol, Armidale, NSW 2350, Australia;[4]E China Univ Sci & Technol, Key Lab Adv Control & Optimizat Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2014

卷号：15

期号：SUPPL.2

外文期刊名：BMC BIOINFORMATICS

收录：;EI(收录号：20161302174026);WOS:【SCI-EXPANDED(收录号:WOS:000345685000008)】；

基金：Lizhi Cui thanks school of information technologies, the University of Sydney for providing him with a Ph.D. fellowship. Lizhi Cui thanks China Scholarship Council for providing living expenses during the study in Sydney, and the students No. is 201206740061.

语种：英文

外文关键词：Curve fitting - White noise - High performance liquid chromatography - Chromatographic analysis - Constrained optimization - Three dimensional computer graphics - Independent component analysis

摘要：Background: The 3D chromatogram generated by High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD) has been researched widely in the field of herbal medicine, grape wine, agriculture, petroleum and so on. Currently, most of the methods used for separating a 3D chromatogram need to know the compounds' number in advance, which could be impossible especially when the compounds are complex or white noise exist. New method which extracts compounds from 3D chromatogram directly is needed. Methods: In this paper, a new separation model named parallel Independent Component Analysis constrained by Reference Curve (pICARC) was proposed to transform the separation problem to a multi-parameter optimization issue. It was not necessary to know the number of compounds in the optimization. In order to find all the solutions, an algorithm named multi-areas Genetic Algorithm (mGA) was proposed, where multiple areas of candidate solutions were constructed according to the fitness and distances among the chromosomes. Results: Simulations and experiments on a real life HPLC-DAD data set were used to demonstrate our method and its effectiveness. Through simulations, it can be seen that our method can separate 3D chromatogram to chromatogram peaks and spectra successfully even when they severely overlapped. It is also shown by the experiments that our method is effective to solve real HPLC-DAD data set. Conclusions: Our method can separate 3D chromatogram successfully without knowing the compounds' number in advance, which is fast and effective.