  A well-known approach to the optical measure of oxygen is based on the
quenching of luminescence by molecular oxygen. The main challenge for this
measuring method is the development of an accurate mathematical model.
Typically, this is overcome by using an approximate empirical model where these
effects are parametrized ad hoc. The complexity increases further if multiple
parameters (like oxygen concentration and temperature) need to be extracted,
particularly if they are cross interfering. The common solution is to measure
the different parameters separately, for example, with different sensors, and
correct for the cross interferences. In this work, we propose a new approach
based on a learning sensor with parallel inference. We show how it is possible
to extract multiple parameters from a single set of optical measurements
without the need for any a priori mathematical model, and with unprecedented
accuracy. We also propose a new metrics to characterize the performance of
neural network based sensors, the Error Limited Accuracy. The proposed approach
is not limited to oxygen and temperture sensing. It can be applied to the
sensing with multiple luminophores, whenever the underlying mathematical model
is not known or too complex.
