Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/17792
Title: | An improved independent component analysis model for 3D chromatogram separation and its solution by multi-areas genetic algorithm | Contributor(s): | Cui, Lizhi (author); Poon, Josiah (author); Poon, Simon K (author); Chen, Hao (author); Gao, Junbin (author); Kwan, Paul H (author); Fan, Kei (author); Ling, Zhihao (author) | Publication Date: | 2014 | Open Access: | Yes | DOI: | 10.1186/1471-2105-15-S12-S8 | Handle Link: | https://hdl.handle.net/1959.11/17792 | Abstract: | 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. | Publication Type: | Journal Article | Source of Publication: | BMC Bioinformatics, 15(Supplement 12), p. 1-10 | Publisher: | BioMed Central Ltd | Place of Publication: | United Kingdom | ISSN: | 1471-2105 | Fields of Research (FoR) 2008: | 080106 Image Processing 060102 Bioinformatics 080109 Pattern Recognition and Data Mining |
Fields of Research (FoR) 2020: | 460306 Image processing 310299 Bioinformatics and computational biology not elsewhere classified 461199 Machine learning not elsewhere classified |
Socio-Economic Objective (SEO) 2008: | 890201 Application Software Packages (excl. Computer Games) 970108 Expanding Knowledge in the Information and Computing Sciences 860799 Agricultural Chemicals not elsewhere classified |
Socio-Economic Objective (SEO) 2020: | 220401 Application software packages 280115 Expanding knowledge in the information and computing sciences |
Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Journal Article |
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