Finding the composition of gas mixtures by a phthalocyanine-coated QCM sensor array and an artificial neural network

This paper presents a system, which is made of an array of eight phthalocyanine-coated QCM sensors and an ANN to find the corresponding composition of a gas mixture. The digital data collected from the sensor responses were preprocessed by a sliding window algorithm, and then used to train a three l...

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Bibliographic Details
Published inSensors and actuators. B, Chemical Vol. 115; no. 1; pp. 450 - 454
Main Authors Özmen, A., Tekce, F., Ebeoğlu, M.A., Taşaltın, C., Öztürk, Z.Z.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 23.05.2006
Subjects
Actuators
Arrays
Digital data
Gas mixtures
Gas sensor
Learning theory
Neural network
Neural networks
QCM
Sensor array
Sensors
Trains
QCM
Gas sensor
Neural network
Sensor array
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Summary:This paper presents a system, which is made of an array of eight phthalocyanine-coated QCM sensors and an ANN to find the corresponding composition of a gas mixture. The digital data collected from the sensor responses were preprocessed by a sliding window algorithm, and then used to train a three layer ANN to determine the gas compositions. The system is tested with the following gas mixtures: (1) ethanol–acetone, (2) ethanol–trichloroethylene, (3) acetone–trichloroethylene. The success rate of the system in identifying the constituent component amounts is 84.5 and 94.3%. Similarly, overall average prediction error is 10.6%.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0925-4005
1873-3077
DOI:10.1016/j.snb.2005.10.007