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...
Autores principales: | , , , , , |
---|---|
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 |