Design of Smart Ion-Selective Electrode Arrays Based on Source Separation through Nonlinear Independent Component Analysis

The development of chemical sensor arrays based on Blind Source Separation (BSS) provides a promising solution to overcome the interference problem associated with Ion-Selective Electrodes (ISE). The main motivation behind this new approach is to ease the time-...

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Main Authors: Duarte L.T., Jutten C.
Format: Article
Language:English
Published: EDP Sciences 2014-03-01
Series:Oil & Gas Science and Technology
Online Access:http://dx.doi.org/10.2516/ogst/2013194
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spelling doaj-287f1bcbbf9e4a4ebbb4bd1f66d761192021-02-02T06:29:22ZengEDP SciencesOil & Gas Science and Technology1294-44751953-81892014-03-0169229330610.2516/ogst/2013194ogst130105Design of Smart Ion-Selective Electrode Arrays Based on Source Separation through Nonlinear Independent Component AnalysisDuarte L.T.0Jutten C.1School of Applied Sciences, University of Campinas (UNICAMP)GIPSA-lab, UMR 5216 CNRS, Institut Polytechnique de Grenoble The development of chemical sensor arrays based on Blind Source Separation (BSS) provides a promising solution to overcome the interference problem associated with Ion-Selective Electrodes (ISE). The main motivation behind this new approach is to ease the time-demanding calibration stage. While the first works on this problem only considered the case in which the ions under analysis have equal valences, the present work aims at developing a BSS technique that works when the ions have different charges. In this situation, the resulting mixing model belongs to a particular class of nonlinear systems that have never been studied in the BSS literature. In order to tackle this sort of mixing process, we adopted a recurrent network as separating system. Moreover, concerning the BSS learning strategy, we develop a mutual information minimization approach based on the notion of the differential of the mutual information. The method works requires a batch operation, and, thus, can be used to perform off-line analysis. The validity of our approach is supported by experiments where the mixing model parameters were extracted from actual data. http://dx.doi.org/10.2516/ogst/2013194
collection DOAJ
language English
format Article
sources DOAJ
author Duarte L.T.
Jutten C.
spellingShingle Duarte L.T.
Jutten C.
Design of Smart Ion-Selective Electrode Arrays Based on Source Separation through Nonlinear Independent Component Analysis
Oil & Gas Science and Technology
author_facet Duarte L.T.
Jutten C.
author_sort Duarte L.T.
title Design of Smart Ion-Selective Electrode Arrays Based on Source Separation through Nonlinear Independent Component Analysis
title_short Design of Smart Ion-Selective Electrode Arrays Based on Source Separation through Nonlinear Independent Component Analysis
title_full Design of Smart Ion-Selective Electrode Arrays Based on Source Separation through Nonlinear Independent Component Analysis
title_fullStr Design of Smart Ion-Selective Electrode Arrays Based on Source Separation through Nonlinear Independent Component Analysis
title_full_unstemmed Design of Smart Ion-Selective Electrode Arrays Based on Source Separation through Nonlinear Independent Component Analysis
title_sort design of smart ion-selective electrode arrays based on source separation through nonlinear independent component analysis
publisher EDP Sciences
series Oil & Gas Science and Technology
issn 1294-4475
1953-8189
publishDate 2014-03-01
description The development of chemical sensor arrays based on Blind Source Separation (BSS) provides a promising solution to overcome the interference problem associated with Ion-Selective Electrodes (ISE). The main motivation behind this new approach is to ease the time-demanding calibration stage. While the first works on this problem only considered the case in which the ions under analysis have equal valences, the present work aims at developing a BSS technique that works when the ions have different charges. In this situation, the resulting mixing model belongs to a particular class of nonlinear systems that have never been studied in the BSS literature. In order to tackle this sort of mixing process, we adopted a recurrent network as separating system. Moreover, concerning the BSS learning strategy, we develop a mutual information minimization approach based on the notion of the differential of the mutual information. The method works requires a batch operation, and, thus, can be used to perform off-line analysis. The validity of our approach is supported by experiments where the mixing model parameters were extracted from actual data.
url http://dx.doi.org/10.2516/ogst/2013194
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AT juttenc designofsmartionselectiveelectrodearraysbasedonsourceseparationthroughnonlinearindependentcomponentanalysis
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