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Title: Optical oxygen sensing with artificial intelligence
Authors : Michelucci, Umberto
Baumgartner, Michael
Venturini, Francesca
Published in : Sensors
Volume(Issue) : 19
Issue : 4
Pages : 777
Publisher / Ed. Institution : MDPI
Issue Date: 2019
License (according to publishing contract) : CC BY 4.0: Attribution 4.0 International
Type of review: Peer review (Publication)
Language : English
Subjects : Artificial intelligence; Luminescence; Luminescence quenching; Machine learning; Neural network; Optical sensor; Oxygen sensor; Phase fluorimetry
Subject (DDC) : 004: Computer science
600: Technology
Abstract: 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.
Departement: School of Engineering
Organisational Unit: Institute of Applied Mathematics and Physics (IAMP)
Publication type: Article in scientific Journal
DOI : 10.3390/s19040777
ISSN: 1424-8220
Appears in Collections:Publikationen School of Engineering

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