Design of Smart Chemical Sensor Arrays

189 Pages·2017·6.45 MB·English

by  Leonardo Tomazeli Duarte

Design of Smart Chemical Sensor Arrays: an Approach
Based on Source Separation Methods
Leonardo Tomazeli Duarte
To cite this version:
Leonardo Tomazeli Duarte. Design of Smart Chemical Sensor Arrays: an Approach Based on Source
Separation Methods. Signal and Image processing. Université de Grenoble, 2009. English. ￿NNT: ￿.
￿tel-00459333￿
HAL Id: tel-00459333
https://theses.hal.science/tel-00459333
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INSTITUT POLYTECHNIQUE DE GRENOBLE
N◦ attribu´e par la biblioth`eque:
THE`SE
pour obtenir le grade de
DOCTEUR de L’Institut Polytechnique de Grenoble
Sp´ecialit´e: Signal, Image, Parole, T´el´ecoms
pr´epar´ee au laboratoire
Grenoble Images Signal Parole et Automatique (GIPSA-lab)
dans le cadre de l’E´cole Doctorale
E´lectronique, E´lectrotechnique, Automatique et Traitement du Signal
pr´esent´ee et soutenue publiquement par
Leonardo TOMAZELI DUARTE
le 17 novembre 2009
Design of Smart Chemical Sensor Arrays: an Approach Based on Source
Separation Methods
Directeur de th`ese:
Christian JUTTEN
JURY
M. Jacques FOULETIER, Pr´esident
M. Yannick DEVILLE, Rapporteur
M. Ali MOHAMMAD-DJAFARI, Rapporteur
M. Christian JUTTEN, Directeur de th`ese
M. Sa¨ıd MOUSSAOUI, Examinateur
M. Pierre TEMPLE-BOYER, Examinateur
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Acknowledgments
First of all, I want to express my gratitude to my supervisor, Prof. Christian Jutten, for his
support, availability and kindness. His passion for science and teaching, and his devotion to his
students were really inspiring and helped me to grow as a researcher and as a person.
I wish to thank my thesis committee members: the president of the jury, Prof. Jacques
Fouletier, and the reviewers, Prof. Yannick Deville and Prof. Ali Mohammad-Djafari, for their
carefulreadingandsuggestions. IamgratefultoProf. PierreTemple-Boyerfortheirsuggestions
and for receiving me at the LAAS laboratory. Finally, I wish to thank Prof. Sa¨ıd Moussaoui for
his careful reading, insightful ideas, and for his collaboration.
I am grateful to the National Council for Scientific and Technological Development (CNPq-
Brazil) for funding my thesis. I also thank the R´egion Rhˆone-Alpes for providing me a research
fellowship.
I wish to thank all the people I met at the Gipsa-lab for providing a stimulating and friendly
environment. I am especially grateful to C´edric Gouy-Pailler, Ladan Amini, Reza Sameni and
Sophie Achard. I am indebted to Bertrand Rivet for the discussions and collaboration.
It is difficult to overstate my gratitude to Romis Attux, Ricardo Suyama and Jo˜ao Marcos
Travassos Romano for their support and for their guidance since my first steps as a researcher.
I would also like to thank my colleagues from the DSPCom laboratory for their support.
I am grateful to all researchers with whom I discussed some technical points. In particular,
I wish to thank Shahram Hosseini. I am grateful to Ahmed Benyahia and J´erˆome Launay for
their support during my stay at the LAAS laboratory.
I would like to thank all my friends, the long-standing ones and the ones I met at Grenoble,
fortheirsupport. IamindebtedtothevoluntarymembersoftheAllianceFranc¸aisedeGrenoble
for their kindness and for helping me during my studies in French language.
I would like to thank my uncle Luiz, my aunt Sueli and my cousins Alexandre and Bruno
for their love and encouragement. I also thank my parents-in-law, Agenor and Yara, my sister-
in-law, Alexandra, and my relatives for their support.
I cannot find words to thank my parents, Aparecida and Sebasti˜ao, for their love and contin-
ualsupportthroughoutmystudies. Withouttheirhelpthisthesiswouldnothavebeenpossible.
I also thank my brother, Ronaldo, for his friendship, love and for being an example to me.
Lastly, but most importantly, I would like to thank my dear wife, Camila, for her love,
unconditional support, encouragement, patience and for joining me in this adventure. To her, I
dedicate this thesis.
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R´esum´e
L’une des principales difficult´es dans l’utilisation de capteurs chimiques concerne le manque
de s´electivit´e inh´erent `a ces dispositifs. La strat´egie classique pour faire face `a ce probl`eme
est fond´ee sur le d´eveloppement de nouvelles membranes qui conduisent `a des capteurs plus
s´electifs. Toutefois, plus r´ecemment, on a d´emontr´e que ce probl`eme peut ´egalement ˆetre trait´e
par une autre approche, dans laquelle l’acquisition de donn´ees est effectu´ee par un r´eseau de
capteurs qui ne sont pas forc´ement s´electifs. Ainsi, dans une deuxi`eme ´etape, les informations
pertinentes sont r´ecup´er´ees `a l’aide des outils de traitement de signal. L’un des b´en´efices le plus
remarquable dans cette d´emarche concerne la flexibilit´e du syst`eme de mesure : le mˆeme r´eseau
de capteurs peut ˆetre utilis´e pour r´ealiser diff´erents types d’analyse.
Dans cette th`ese, nous ´etudions l’utilisation de r´eseaux de capteurs dans le probl`eme de
l’analyse chimique quantitative. Cependant, contrairement `a la grande majorit´e des travaux
dans cette ligne, notre approche envisage des solutions non-supervis´ees, n’ayant pas besoin
d’une ´etape d’´etalonnage. Cette situation peut ˆetre formul´ee comme un probl`eme de s´eparation
aveugle de sources. Puisque les capteurs chimiques consid´er´es dans cette recherche pr´esentent
des r´eponses non-lin´eaires, le processus de m´elange sous-jacent au r´eseau de capteurs est non-
lin´eaire, ce qui rend le probl`eme difficile.
Les principales contributions de cette recherche sont li´ees justement au d´eveloppement de
m´ethodes de s´eparation des m´elanges non-lin´eaires sur mesure pour les r´eseaux d’´electrodes
s´electives potentiom´etriques. Nous consid´erons des solutions fond´ees sur l’analyse en com-
posantes ind´ependantes, mais ´egalement sur d’autres strat´egies qui nous permettent de prendre
en compte des connaissances a priori typiques dans l’application cibl´ee dans cette recherche,
comme la positivit´e des activit´es chimiques.
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Abstract
Chemical sensors usually lack selectivity, that is, they may respond to interfering species other
than the target one. The conventional strategy to cope with this problem is based on the
development of new sensitive membranes that lead to more selective sensors. More recently,
however,muchattentionhasbeengiventoanalternativeapproach,inwhichthedataacquisition
is conducted through an array of sensors that are not necessarily selective. Then, in a second
stage, signalprocessingtoolsareemployedtoextracttherelevantinformationfromtheacquired
data. Among the benefits brought by this approach, is the flexibility inherent in a sensor array,
which allows one to consider different analytes, or even different types of analysis, by using the
same measuring system.
In this thesis, we study the problem of quantitative chemical analysis through sensor arrays.
However, unlike the majority of the works in this line, we consider an unsupervised approach
in which the adjustment of the signal processing method does not require a set of training
(or calibration) points. This situation can be formulated as a Blind Source Separation (BSS)
problem. The difficulty here lies in the fact that the chemical sensors considered in this research
are clearly nonlinear devices, thus resulting in nonlinear mixing models.
The main contributions of this research are related to the development of nonlinear BSS
methods tailor-made for arrays of ion-selective electrodes. We consider a paradigm based upon
the Independent Component Analysis (ICA) but also upon other strategies that allow us to
incorporatesomeinformationtypicaloftheapplicationconsideredinthisresearch,likepositivity
of chemical activities.
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Contents
List of figures xv
List of tables xvii
List of abbreviations xix
Introduction 1
1 State of the art 5
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Chemical sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2.1 Ion-selective electrodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.2 Gas sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.2.3 Selectivity of chemical sensors . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.2.4 Dealing with the interference: the sensor array approach . . . . . . . . . . 12
1.3 Blind source separation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.3.1 Mathematical formulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.3.2 Linear instantaneous models . . . . . . . . . . . . . . . . . . . . . . . . . 17
1.3.3 Nonlinear models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
1.4 Application of BSS techniques to chemical sensor arrays . . . . . . . . . . . . . . 32
1.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2 Experiments with ISE arrays 33
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.2 Experimental . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.2.1 Materials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.2.2 Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.2.3 Experimental details and data set organization . . . . . . . . . . . . . . . 34
2.3 Data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.3.1 First scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
2.3.2 Second scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.3.3 Third scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
2.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
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