Cargando…

A Practical Method to Estimate the Resolving Power of a Chemical Sensor Array: Application to Feature Selection

A methodology to calculate analytical figures of merit is not well established for detection systems that are based on sensor arrays with low sensor selectivity. In this work, we present a practical approach to estimate the Resolving Power of a sensory system, considering non-linear sensors and hete...

Descripción completa

Detalles Bibliográficos
Autores principales: Fernandez, Luis, Yan, Jia, Fonollosa, Jordi, Burgués, Javier, Gutierrez, Agustin, Marco, Santiago
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6005889/
https://www.ncbi.nlm.nih.gov/pubmed/29946537
http://dx.doi.org/10.3389/fchem.2018.00209
_version_ 1783332747147739136
author Fernandez, Luis
Yan, Jia
Fonollosa, Jordi
Burgués, Javier
Gutierrez, Agustin
Marco, Santiago
author_facet Fernandez, Luis
Yan, Jia
Fonollosa, Jordi
Burgués, Javier
Gutierrez, Agustin
Marco, Santiago
author_sort Fernandez, Luis
collection PubMed
description A methodology to calculate analytical figures of merit is not well established for detection systems that are based on sensor arrays with low sensor selectivity. In this work, we present a practical approach to estimate the Resolving Power of a sensory system, considering non-linear sensors and heteroscedastic sensor noise. We use the definition introduced by Shannon in the field of communication theory to quantify the number of symbols in a noisy environment, and its version adapted by Gardner and Barlett for chemical sensor systems. Our method combines dimensionality reduction and the use of algorithms to compute the convex hull of the empirical data to estimate the data volume in the sensor response space. We validate our methodology with synthetic data and with actual data captured with temperature-modulated MOX gas sensors. Unlike other methodologies, our method does not require the intrinsic dimensionality of the sensor response to be smaller than the dimensionality of the input space. Moreover, our method circumvents the problem to obtain the sensitivity matrix, which usually is not known. Hence, our method is able to successfully compute the Resolving Power of actual chemical sensor arrays. We provide a relevant figure of merit, and a methodology to calculate it, that was missing in the literature to benchmark broad-response gas sensor arrays.
format Online
Article
Text
id pubmed-6005889
institution National Center for Biotechnology Information
language English
publishDate 2018
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-60058892018-06-26 A Practical Method to Estimate the Resolving Power of a Chemical Sensor Array: Application to Feature Selection Fernandez, Luis Yan, Jia Fonollosa, Jordi Burgués, Javier Gutierrez, Agustin Marco, Santiago Front Chem Chemistry A methodology to calculate analytical figures of merit is not well established for detection systems that are based on sensor arrays with low sensor selectivity. In this work, we present a practical approach to estimate the Resolving Power of a sensory system, considering non-linear sensors and heteroscedastic sensor noise. We use the definition introduced by Shannon in the field of communication theory to quantify the number of symbols in a noisy environment, and its version adapted by Gardner and Barlett for chemical sensor systems. Our method combines dimensionality reduction and the use of algorithms to compute the convex hull of the empirical data to estimate the data volume in the sensor response space. We validate our methodology with synthetic data and with actual data captured with temperature-modulated MOX gas sensors. Unlike other methodologies, our method does not require the intrinsic dimensionality of the sensor response to be smaller than the dimensionality of the input space. Moreover, our method circumvents the problem to obtain the sensitivity matrix, which usually is not known. Hence, our method is able to successfully compute the Resolving Power of actual chemical sensor arrays. We provide a relevant figure of merit, and a methodology to calculate it, that was missing in the literature to benchmark broad-response gas sensor arrays. Frontiers Media S.A. 2018-06-12 /pmc/articles/PMC6005889/ /pubmed/29946537 http://dx.doi.org/10.3389/fchem.2018.00209 Text en Copyright © 2018 Fernandez, Yan, Fonollosa, Burgués, Gutierrez and Marco. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Chemistry
Fernandez, Luis
Yan, Jia
Fonollosa, Jordi
Burgués, Javier
Gutierrez, Agustin
Marco, Santiago
A Practical Method to Estimate the Resolving Power of a Chemical Sensor Array: Application to Feature Selection
title A Practical Method to Estimate the Resolving Power of a Chemical Sensor Array: Application to Feature Selection
title_full A Practical Method to Estimate the Resolving Power of a Chemical Sensor Array: Application to Feature Selection
title_fullStr A Practical Method to Estimate the Resolving Power of a Chemical Sensor Array: Application to Feature Selection
title_full_unstemmed A Practical Method to Estimate the Resolving Power of a Chemical Sensor Array: Application to Feature Selection
title_short A Practical Method to Estimate the Resolving Power of a Chemical Sensor Array: Application to Feature Selection
title_sort practical method to estimate the resolving power of a chemical sensor array: application to feature selection
topic Chemistry
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6005889/
https://www.ncbi.nlm.nih.gov/pubmed/29946537
http://dx.doi.org/10.3389/fchem.2018.00209
work_keys_str_mv AT fernandezluis apracticalmethodtoestimatetheresolvingpowerofachemicalsensorarrayapplicationtofeatureselection
AT yanjia apracticalmethodtoestimatetheresolvingpowerofachemicalsensorarrayapplicationtofeatureselection
AT fonollosajordi apracticalmethodtoestimatetheresolvingpowerofachemicalsensorarrayapplicationtofeatureselection
AT burguesjavier apracticalmethodtoestimatetheresolvingpowerofachemicalsensorarrayapplicationtofeatureselection
AT gutierrezagustin apracticalmethodtoestimatetheresolvingpowerofachemicalsensorarrayapplicationtofeatureselection
AT marcosantiago apracticalmethodtoestimatetheresolvingpowerofachemicalsensorarrayapplicationtofeatureselection
AT fernandezluis practicalmethodtoestimatetheresolvingpowerofachemicalsensorarrayapplicationtofeatureselection
AT yanjia practicalmethodtoestimatetheresolvingpowerofachemicalsensorarrayapplicationtofeatureselection
AT fonollosajordi practicalmethodtoestimatetheresolvingpowerofachemicalsensorarrayapplicationtofeatureselection
AT burguesjavier practicalmethodtoestimatetheresolvingpowerofachemicalsensorarrayapplicationtofeatureselection
AT gutierrezagustin practicalmethodtoestimatetheresolvingpowerofachemicalsensorarrayapplicationtofeatureselection
AT marcosantiago practicalmethodtoestimatetheresolvingpowerofachemicalsensorarrayapplicationtofeatureselection