SAW sensor’s frequency shift characterization for odor recognition and concentration estimation

Olivier Hotel (1) , Jean-Philippe Poli (1) , Christine Mer-Calfati (2) , Emmanuel Scorsone (2) , Samuel Saada (2)

1 LI3A - Intelligence Artificielle et Apprentissage Automatique
2 LCD-LIST - Laboratoire Capteurs Diamant

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: odor concentration estimation; odor recognition; SAW sensor frequency shift characterization; viscoelasticity; surface acoustic wave sensors; feature extraction; electronic nose; deconvolution; chemical variables measurement; Frequency estimation; Convergence; Chemical compounds; Loading; Surface acoustic waves; Frequency modulation; concentration evaluation; odour recognition; SAW sensors; 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; detector; sensor; instrumentation; diamond; signal processing

Domains: Physics [physics]; Physics [physics]; Instrumentation and Detectors [physics.ins-det]; Chemical Sciences; Analytical chemistry; Computer Science [cs]; Signal and Image Processing; Physics [physics]; Physics [physics]; Data Analysis, Statistics and Probability [physics.data-an]

Olivier Hotel, Jean-Philippe Poli, Christine Mer-Calfati, Emmanuel Scorsone, Samuel Saada. SAW sensor’s frequency shift characterization for odor recognition and concentration estimation. IEEE Sensors Journal, 2017, 17 (21), pp.7011 - 7018. ⟨10.1109/JSEN.2017.2751666⟩. ⟨cea-01847213⟩