The simultaneous determination of multiple physical or chemical parameters can be very advantageous in many sensor applications. In some cases, it is unavoidable because the parameters of interest display cross sensitivities or depend on multiple quantities varying simultaneously. One notable example is the determination of oxygen partial pressure via luminescence quenching. The measuring principle is based on the measurement of the luminescence of a specific molecule, whose intensity and decay time are reduced due to collisions with oxygen molecules. Since both the luminescence and the quenching phenomena are strongly temperature-dependent, this type of sensor needs continuous monitoring of the temperature. This is typically achieved by adding temperature sensors and employing a multi-parametric model (Stern–Volmer equation), whose parameters are all temperature- dependent. As a result, the incorrect measurement of the temperature of the indicator is a major source of error. In this work a new approach based on multi-task learning (MTL) artificial neural networks (ANN) was successfully implemented to achieve robust sensing for industrial applications. These were integrated in a sensor that not only does not need the separate detection of temperature but even exploits the intrinsic cross-interferences of the sensing principle to predict simultaneously oxygen partial pressure and temperature. A detailed analysis of the robustness of the method was performed to demonstrate its potential for industrial applications. This type of sensor could in the future significantly simplify the design of the sensor and at the same time increase its performance.
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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.