Parallel model of independent component analysis constrained by reference curves for HPLC-DAD and its solution by multi-areas genetic algorithm

L. Cui; J. Poon; S.K. Poon; K. Fan; H Chen; Junbin Gao; P. Kwan; Z. Ling

doi:10.1109/BIBM.2013.6732631

Parallel model of independent component analysis constrained by reference curves for HPLC-DAD and its solution by multi-areas genetic algorithm

L. Cui, J. Poon, S.K. Poon, K. Fan, H Chen, Junbin Gao, P. Kwan, Z. Ling

Research output: Other contribution to conference › Poster › peer-review

3 Citations (Scopus)

Abstract

In order to separate a 3D chromatography, which is generated from High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD), into chromatograms and spectra, we proposed a model called parallel Independent Component Analysis constrained by Reference Curve (pICARC), which transforms the separation problem to a multi-parameter optimization issue. Then, A new algorithm named multi-areas Genetic Algorithm (mGA) is developed to search multiple solutions in parallel. It was demonstrated that our approach could successfully separate a given HPLC-DAD dataset into chromatograms and spectra with little errors and in short computational time.

Original language	English
Pages	27-28
Number of pages	2
DOIs	https://doi.org/10.1109/BIBM.2013.6732631
Publication status	Published - 2013
Event	IEEE International Conference on Bioinformatics and Biomedicine - Shanghai, China, China Duration: 18 Dec 2013 → 21 Dec 2013

Conference

Conference	IEEE International Conference on Bioinformatics and Biomedicine
Country/Territory	China
Period	18/12/13 → 21/12/13

Access to Document

10.1109/BIBM.2013.6732631

Cite this

Cui, L., Poon, J., Poon, S. K., Fan, K., Chen, H., Gao, J., Kwan, P., & Ling, Z. (2013). Parallel model of independent component analysis constrained by reference curves for HPLC-DAD and its solution by multi-areas genetic algorithm. 27-28. Poster session presented at IEEE International Conference on Bioinformatics and Biomedicine, China. https://doi.org/10.1109/BIBM.2013.6732631

@conference{865fd67566034716acb49680f6436d40,

title = "Parallel model of independent component analysis constrained by reference curves for HPLC-DAD and its solution by multi-areas genetic algorithm",

abstract = "In order to separate a 3D chromatography, which is generated from High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD), into chromatograms and spectra, we proposed a model called parallel Independent Component Analysis constrained by Reference Curve (pICARC), which transforms the separation problem to a multi-parameter optimization issue. Then, A new algorithm named multi-areas Genetic Algorithm (mGA) is developed to search multiple solutions in parallel. It was demonstrated that our approach could successfully separate a given HPLC-DAD dataset into chromatograms and spectra with little errors and in short computational time.",

keywords = "HPLC-DAD data set separation, MGA, PICARC",

author = "L. Cui and J. Poon and S.K. Poon and K. Fan and H Chen and Junbin Gao and P. Kwan and Z. Ling",

note = "Imported on 03 May 2017 - DigiTool details were: publisher = United States: IEEE, 2013. Event dates (773o) = 18-21 December, 2013; Parent title (773t) = IEEE International Conference on Bioinformatics and Biomedicine.; IEEE International Conference on Bioinformatics and Biomedicine ; Conference date: 18-12-2013 Through 21-12-2013",

year = "2013",

doi = "10.1109/BIBM.2013.6732631",

language = "English",

pages = "27--28",

}

Parallel model of independent component analysis constrained by reference curves for HPLC-DAD and its solution by multi-areas genetic algorithm. / Cui, L.; Poon, J.; Poon, S.K. et al.
2013. 27-28 Poster session presented at IEEE International Conference on Bioinformatics and Biomedicine, China.

Research output: Other contribution to conference › Poster › peer-review

TY - CONF

T1 - Parallel model of independent component analysis constrained by reference curves for HPLC-DAD and its solution by multi-areas genetic algorithm

AU - Cui, L.

AU - Poon, J.

AU - Poon, S.K.

AU - Fan, K.

AU - Chen, H

AU - Gao, Junbin

AU - Kwan, P.

AU - Ling, Z.

N1 - Imported on 03 May 2017 - DigiTool details were: publisher = United States: IEEE, 2013. Event dates (773o) = 18-21 December, 2013; Parent title (773t) = IEEE International Conference on Bioinformatics and Biomedicine.

PY - 2013

Y1 - 2013

N2 - In order to separate a 3D chromatography, which is generated from High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD), into chromatograms and spectra, we proposed a model called parallel Independent Component Analysis constrained by Reference Curve (pICARC), which transforms the separation problem to a multi-parameter optimization issue. Then, A new algorithm named multi-areas Genetic Algorithm (mGA) is developed to search multiple solutions in parallel. It was demonstrated that our approach could successfully separate a given HPLC-DAD dataset into chromatograms and spectra with little errors and in short computational time.

AB - In order to separate a 3D chromatography, which is generated from High Performance Liquid Chromatography-Diode Array Detector (HPLC-DAD), into chromatograms and spectra, we proposed a model called parallel Independent Component Analysis constrained by Reference Curve (pICARC), which transforms the separation problem to a multi-parameter optimization issue. Then, A new algorithm named multi-areas Genetic Algorithm (mGA) is developed to search multiple solutions in parallel. It was demonstrated that our approach could successfully separate a given HPLC-DAD dataset into chromatograms and spectra with little errors and in short computational time.

KW - HPLC-DAD data set separation

KW - MGA

KW - PICARC

U2 - 10.1109/BIBM.2013.6732631

DO - 10.1109/BIBM.2013.6732631

M3 - Poster

SP - 27

EP - 28

T2 - IEEE International Conference on Bioinformatics and Biomedicine

Y2 - 18 December 2013 through 21 December 2013

ER -

Parallel model of independent component analysis constrained by reference curves for HPLC-DAD and its solution by multi-areas genetic algorithm

Abstract

Conference

Access to Document

Fingerprint

Cite this