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dc.contributor.authorHuerta, Ramon
dc.contributor.authorMosqueiro, Thiago
dc.contributor.authorFonollosa Magrinyà, Jordi
dc.contributor.authorRulkov, Nikolai
dc.contributor.authorRodriguez Lujan, Irene
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2018-07-09T08:03:46Z
dc.date.available2018-07-09T08:03:46Z
dc.date.issued2016-07-15
dc.identifier.citationHuerta, R., Mosqueiro, T., Fonollosa, J., Rulkov, N., Rodriguez-Lujan, I. Online decorrelation of humidity and temperature in chemical sensors for continuous monitoring. "Chemometrics and intelligent laboratory systems", 15 Juliol 2016, vol. 157, p. 169-176.
dc.identifier.issn0169-7439
dc.identifier.otherhttps://www.arxiv.org/pdf/1608.01719v1
dc.identifier.urihttp://hdl.handle.net/2117/119106
dc.description.abstractA method for online decorrelation of chemical sensor signals from the effects of environmental humidity and temperature variations is proposed. The goal is to improve the accuracy of electronic nose measurements for continuous monitoring by processing data from simultaneous readings of environmental humidity and temperature. The electronic nose setup built for this study included eight metal-oxide sensors, temperature and humidity sensors with a wireless communication link to external computer. This wireless electronic nose was used to monitor the air for two years in the residence of one of the authors and it collected data continuously during 537 days with a sampling rate of 1 sample per second. To estimate the effects of variations in air humidity and temperature on the chemical sensors' signals, we used a standard energy band model for an n-type metal-oxide (MOX) gas sensor. The main assumption of the model is that variations in sensor conductivity can be expressed as a nonlinear function of changes in the semiconductor energy bands in the presence of external humidity and temperature variations. Fitting this model to the collected data, we confirmed that the most statistically significant factors are humidity changes and correlated changes of temperature and humidity. This simple model achieves excellent accuracy with a coefficient of determination R2 close to 1. To show how the humidity–temperature correction model works for gas discrimination, we constructed a model for online discrimination among banana, wine and baseline response. This shows that pattern recognition algorithms improve performance and reliability by including the filtered signal of the chemical sensors.
dc.format.extent8 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria electrònica::Instrumentació i mesura::Sensors i actuadors
dc.subject.lcshChemical detectors
dc.subject.otherElectronic nose
dc.subject.otherChemical sensors
dc.subject.otherHumidity
dc.subject.otherTemperature
dc.subject.otherDecorrelation
dc.subject.otherWireless e-nose
dc.subject.otherMOX sensors
dc.subject.otherEnergy band model
dc.subject.otherHome monitoring
dc.titleOnline decorrelation of humidity and temperature in chemical sensors for continuous monitoring
dc.typeArticle
dc.subject.lemacDetectors -- Aparells i instruments
dc.subject.lemacSensors químics
dc.contributor.groupUniversitat Politècnica de Catalunya. B2SLab - Bioinformatics and Biomedical Signals Laboratory
dc.identifier.doi10.1016/j.chemolab.2016.07.004
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0169743916301666
dc.rights.accessOpen Access
local.identifier.drac21473888
dc.description.versionPostprint (author's final draft)
local.citation.authorHuerta, R.; Mosqueiro, T.; Fonollosa, J.; Rulkov, N.; Rodriguez-Lujan, I.
local.citation.publicationNameChemometrics and intelligent laboratory systems
local.citation.volume157
local.citation.startingPage169
local.citation.endingPage176


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