The optical determination of oxygen partial pressure is of great interest in numerous areas, like medicine, biotechnology, and chemistry. A well-known optical measuring approach is based on the quenching of luminescence by the oxygen molecules. The conventional approach consists in measuring the intensity decay time and relate it to the oxygen concentration through a multi-parametric model (Stern–Volmer equation). The parameters of this equation are, however, all temperature-dependent. Therefore the temperature needs to be known to determine the oxygen concentration and is measured separately, either optically or with a completely different sensor. This work proposes a new approach based on a multi-task learning (MTL) neural network. Using the luminescence data of one single indicator, which is sensitive to both oxygen and temperature, the neural network achieves predictions of both parameters which are comparable to the accuracy of commercial senors. The impact of the new proposed approach is however not limited to dual oxygen and temperature sensing, but can be applied to all those cases in which the sensor response is too complex, to be comfortably described by a mathematical model.
Luminescence sensors are based on the determination of emitted intensity or decay time when a luminophore is in contact with its environment. Changes of the environment, like temperature or analyte concentration cause a change in the intensity and decay rate of the emission. Typically, since the absolute values of the measured quantities depend on the specific sensing element and scheme used, a sensor needs an analytical model to describe the dependence of the quantity to be determined, for example the oxygen concentration, from sensed quantity, for example the decay time. Additionally, since the details of this dependence are device specific, a sensor needs to be calibrated at known reference conditions. This work explores an entirely new artificial intelligence approach and demonstrates the feasibility of oxygen sensing through machine learning. The new developed neural network is used for optical oxygen sensing based on luminescence quenching. After training the neural network on synthetic data, it was tested on measured data to verify the prediction of the model. The results show a mean deviation of the predicted from the measured concentration of 0.5 % air, which is comparable to many commercial and low-cost sensors. 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 performing the training on experimental data. In this work the approach is tested at different temperatures, showing its applicability in the entire range relevant for biological applications. This work demonstrates the applicability of this new approach based on machine learning for the development of a new generation of optical luminescence oxygen sensors without the need of an analytical model of the sensing element and sensing scheme.