  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 classical 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 percent 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 of sensors.
