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This work deals with the problem of volatile chemical classification with an electronic nose (e-nose), and particularly focuses on the case where the e-nose is not collecting samples in a stationary fashion but is carried by a moving platform (mobile robot, car, bike, etc.). We bring to light that, under these specific circumstances, substantial changes in the transient response of the gas sensors arise (something that has not been considered until now). We experimentally demonstrate that these changes in the sensor's response have an important impact on the classification accuracy if not properly considered, resulting in a decrease of up to 30% in some configurations. We back our conclusions with an extensive experimental evaluation consisting of a mobile robot inspecting a long indoor corridor with two chemical volatiles sources (ethanol and acetone) more than 240 times, at four different motion speeds. The paper also reveals the relevance of training the classifiers with data collected in motion, and proposes different training schemes suitable to this problem. [ABSTRACT FROM AUTHOR]

Abstract: Gas quantification based on the response of an array of metal oxide (MOX) gas sensors in an Open Sampling System is a complex problem due to the highly dynamic characteristic of turbulent airflow and the slow dynamics of the MOX sensors. However, many gas related applications require to determine the gas concentration the sensors are being exposed to. Due to the chaotic nature that dominates gas dispersal, in most cases it is desirable to provide, together with an estimate of the mean concentration, an estimate of the uncertainty of the prediction. This work presents a probabilistic approach for gas quantification with an array of MOX gas sensors based on Gaussian Processes, estimating for every measurement of the sensors a posterior distribution of the concentration, from which confidence intervals can be obtained. The proposed approach has been tested with an experimental setup where an array of MOX sensors and a Photo Ionization Detector (PID), used to obtain ground truth concentration, are placed downwind with respect to the gas source. Our approach has been implemented and compared with standard gas quantification methods, demonstrating the advantages when estimating gas concentrations. [Copyright &y& Elsevier]