by Keyword: Chemical sensing
Sheik, S., Marco, S., Huerta, R., Fonollosa, J., (2014). Continuous prediction in chemoresisitive gas sensors using reservoir computing Procedia Engineering 28th European Conference on Solid-State Transducers (EUROSENSORS 2014) , Eurosensors (Brescia, Italy) 87, 843-846
Although Metal Oxide (MOX) sensors are predominant choices to perform fundamental tasks of chemical detection, their use has been mainly limited to relatively controlled scenarios where a gas sensor array is first exposed to a reference, then to the gas sample, and finally to the reference again to recover the initial state. In this paper we propose the use of MOX sensors along with Reservoir Computing algorithms to identify chemicals of interest. Our approach allows continuous gas monitoring in simple experimental setups without the requirement of acquiring recovery transient of the sensors, thereby making the system specifically suitable for online monitoring applications.
JTD Keywords: Chemical sensing, Reservoir computing, Gas sensors, Dynamic gas mixtures, Electronic nose
Fonollosa, Jordi, Fernérndez, Luis, Huerta, Ramón, Gutiérrez-Gálvez, Agustín, Marco, Santiago, (2013). Temperature optimization of metal oxide sensor arrays using Mutual Information Sensors and Actuators B: Chemical Elsevier 187, (0), 331-339
The sensitivity and selectivity of metal oxide (MOX) gas sensors change significantly when the sensors operate at different temperatures. While previous investigations have presented systematic approaches to optimize the operating temperature of a single MOX sensor, in this paper we present a methodology to select the optimal operating temperature of all the MOX sensors constituent of a gas sensor array based on the multivariate response of all the sensing elements. Our approach estimates a widely used Information Theory measure, the so-called Mutual Information (MI), which quantifies the amount of information that the state of one random variable (response of the gas sensor array) can provide from the state of another random variable representing the gas quality. More specifically, our methodology builds sensor models from experimental data to solve the technical problem of populating the joint probability distribution for the MI estimation. We demonstrate the relevance of our approach by maximizing the MI and selecting the best operating temperatures of a four-sensor array sampled at 94 different temperatures to optimize the discrimination task of ethanol, acetic acid, 2-butanone, and acetone. In addition to being applicable in principle to sensor arrays of any size, our approach gives precise information on the ability of the system to discriminate odors according to the temperature of the MOX sensors, for either the optimal set of temperatures or the temperatures that may render inefficient operation of the system itself.
JTD Keywords: MOX gas sensor, Temperature optimization, Limit of detection, Mutual Information, E-nose, Sensor array, Information Theory, Chemical sensing