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Optical Oxygen Sensing with Artificial Intelligence

1
TOELT LLC, Birchlenstr. 25, 8600 Dübendorf, Switzerland
2
Institute of Applied Mathematics and Physics, Zurich University of Applied Sciences, Technikumstrasse 9, 8401 Winterthur, Switzerland
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(4), 777; https://doi.org/10.3390/s19040777
Received: 3 January 2019 / Revised: 8 February 2019 / Accepted: 10 February 2019 / Published: 14 February 2019
(This article belongs to the Section Intelligent Sensors)
Luminescence-based sensors for measuring oxygen concentration are widely used in both industry and research due to the practical advantages and sensitivity of this type of sensing. The measuring principle is the luminescence quenching by oxygen molecules, which results in a change of the luminescence decay time and intensity. In the classical approach, this change is related to an oxygen concentration using the Stern-Volmer equation. This equation, which in most cases is non-linear, is parameterized through device-specific constants. Therefore, to determine these parameters, every sensor needs to be precisely calibrated at one or more known concentrations. This study explored an entirely new artificial intelligence approach and demonstrated the feasibility of oxygen sensing through machine learning. The specifically developed neural network learns very efficiently to relate the input quantities to the oxygen concentration. The results show a mean deviation of the predicted from the measured concentration of 0.5% air, comparable to many commercial and low-cost sensors. Since the network was trained using synthetically generated data, the accuracy of the model predictions is limited by the ability of the generated data to describe the measured data, opening up future possibilities for significant improvement by using a large number of experimental measurements for training. The approach described in this work demonstrates the applicability of artificial intelligence to sensing technology and paves the road for the next generation of sensors. View Full-Text
Keywords: artificial intelligence; neural network; machine learning; oxygen sensor; luminescence; optical sensor; luminescence quenching; phase fluorimetry artificial intelligence; neural network; machine learning; oxygen sensor; luminescence; optical sensor; luminescence quenching; phase fluorimetry
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MDPI and ACS Style

Michelucci, U.; Baumgartner, M.; Venturini, F. Optical Oxygen Sensing with Artificial Intelligence. Sensors 2019, 19, 777. https://doi.org/10.3390/s19040777

AMA Style

Michelucci U, Baumgartner M, Venturini F. Optical Oxygen Sensing with Artificial Intelligence. Sensors. 2019; 19(4):777. https://doi.org/10.3390/s19040777

Chicago/Turabian Style

Michelucci, Umberto, Michael Baumgartner, and Francesca Venturini. 2019. "Optical Oxygen Sensing with Artificial Intelligence" Sensors 19, no. 4: 777. https://doi.org/10.3390/s19040777

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