A novel approach for gas discrimination in natural environments with open sampling systems

Victor Hernandez Bennetts, Erik Schaffernicht, Victor Pomareda Sesé, Achim J. Lilienthal, Marco Trincavelli

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

17 Scopus citations


This work presents a gas discrimination approach for Open Sampling Systems (OSS), composed of non-specific metal oxide sensors only. In an OSS, as used on robots or in sensor networks, the sensors are exposed to the dynamics of the environment and thus, most of the data corresponds to highly diluted samples while high concentrations are sparse. In addition, a positive correlation between class separability and concentration level can be observed. The proposed approach computes the class posteriors by coupling the pairwise probabilities between the compounds to a confidence model based on an estimation of the concentration. In this way a rejection posterior, analogous to the detection limit of the human nose, is learned. Evaluation was conducted in indoor and outdoor sites, with an OSS equipped robot, in the presence of two gases. The results show that the proposed approach achieves a high classification performance with a low sensitivity to the selection of meta parameters.

Original languageEnglish
Title of host publicationIEEE SENSORS 2014, Proceedings
EditorsFrancisco J. Arregui
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781479901616
StatePublished - 12 Dec 2014
Externally publishedYes
Event13th IEEE SENSORS Conference, SENSORS 2014 - Valencia, Spain
Duration: 2 Nov 20145 Nov 2014

Publication series

NameProceedings of IEEE Sensors
ISSN (Print)1930-0395
ISSN (Electronic)2168-9229


Conference13th IEEE SENSORS Conference, SENSORS 2014


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