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Journal articles
SAW Sensor’s Frequency Shift Characterization for Odor Recognition and Concentration Estimation
Abstract : In this paper, we propose an approach to determine the time constants and the amplitudes of the mass loading effect and of the viscoelastic contribution of SAW sensor's frequency shift. This approach consists in optimizing a function of these parameters, which is independent of the concentration profile. We experimentally establish in laboratory conditions (T = 22 °C), on a data set composed of seven different gases, that these features are suitable for chemical compounds identification. In particular, we obtain a higher classification rate than the traditional amplitudes of the signals during the steady state, and we show that the classification success rate can be increased by using both of them in conjunction with a feature subset selection heuristic. We also propose a method based on deconvolution and kernel regression to estimate the temporal concentration profile.
Keywords :
SAW sensors
odour recognition
concentration evaluation
Frequency modulation
Surface acoustic waves
Loading
Chemical compounds
Convergence
Frequency estimation
chemical variables measurement
deconvolution
electronic noses
feature extraction
surface acoustic wave sensors
viscoelasticity
SAW sensor frequency shift characterization
odor recognition
odor concentration estimation
time constants
mass loading effect amplitudes
frequency shift viscoelastic effect
parameter optimization
chemical compounds identification
feature subset selection heuristic
deconvolution method
kernel regression method
temporal concentration profile estimation
https://hal-cea.archives-ouvertes.fr/cea-01847213
Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Monday, July 23, 2018 - 1:43:01 PM
Last modification on : Friday, June 25, 2021 - 10:00:02 AM
Contributor : Léna Le Roy Connect in order to contact the contributor
Submitted on : Monday, July 23, 2018 - 1:43:01 PM
Last modification on : Friday, June 25, 2021 - 10:00:02 AM