A gas sensor array for environmental air monitoring: A study case of application of artificial neural networks

Michele Penza, Domenico Suriano, Gennaro Cassano, Riccardo Rossi, Marco Alvisi, Valerio Pfister, Livia Trizio, Magda Brattoli, Gianluigi De Gennaro

Research output: Contribution to conferencePaper

Abstract

An array of commercial gas sensors and nanotechnology sensors has been integrated to quantify gas concentration of air-pollutants. A variety of chemoresistive gas sensors, commercial (Figaro and Fis) and developed at ENEA laboratories (metal-modified carbon nanotubes) were tested to implement a database useful for applied artificial neural networks (ANNs). The ANN algorithm used is the common perceptron multi-layer feed-forward network based on error back-propagation. Electronic Noses based on various sensor arrays related to mammalian olfactory systems have been largely reported [1,2]. Here, we reported on the perceptron-based ANNs applied to a large database of 3875 datapoints for environmental air monitoring. The ANNs performance has been individually assessed for any targeted gas. The response of the classifier has been measured for NO2, CO, CO2, SO2, and H2S gas. The NO2characteristics exhibit that real concentrations and predicted concentrations are very close with a normalized mean square error (NMSE) in the test set as low as 6%. © 2011 American Institute of Physics.
Original languageEnglish
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event14th International Symposium on Olfaction and Electronic Nose, ISOEN 2011 - , United States
Duration: 1 Jan 2011 → …

Conference

Conference14th International Symposium on Olfaction and Electronic Nose, ISOEN 2011
CountryUnited States
Period1/1/11 → …

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All Science Journal Classification (ASJC) codes

  • Physics and Astronomy(all)

Cite this

Penza, M., Suriano, D., Cassano, G., Rossi, R., Alvisi, M., Pfister, V., ... De Gennaro, G. (2011). A gas sensor array for environmental air monitoring: A study case of application of artificial neural networks. Paper presented at 14th International Symposium on Olfaction and Electronic Nose, ISOEN 2011, United States. https://doi.org/10.1063/1.3626360