Preprint Article Version 1 This version is not peer-reviewed

Optical oxygen sensing with artificial intelligence

Version 1 : Received: 3 January 2019 / Approved: 4 January 2019 / Online: 4 January 2019 (14:39:44 CET)

A peer-reviewed article of this Preprint also exists.

Michelucci, U.; Baumgartner, M.; Venturini, F. Optical Oxygen Sensing with Artificial Intelligence. Sensors 2019, 19, 777. Michelucci, U.; Baumgartner, M.; Venturini, F. Optical Oxygen Sensing with Artificial Intelligence. Sensors 2019, 19, 777.

Journal reference: Sensors 2019, 19, 777
DOI: 10.3390/s19040777

Abstract

Luminescence-based sensors for measuring oxygen concentration are widely used both in 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 standard approach, this change is related to an oxygen concentration using the Stern–Volmer equation. This equation, which in most of the cases is non-linear, is parametrized through device-specific constants. Therefore, to determine these parameters every sensor needs to be precisely calibrated at one or more known concentrations. This work explores an entirely new artificial intelligence approach and demonstrates 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.

Subject Areas

artificial intelligence; neural network; machine learning; oxygen sensor; luminescence; optical sensor; luminescence quenching; phase fluorimetry

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