Towards Gas Identification in Unknown Mixtures Using an Electronic Nose with One-Class Learning

Han Fan, Daniel Jonsson, Erik Schaffernicht, Achim J. Lilienthal

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

Abstract

Gas identification using an electronic nose (e-nose) typically relies on a multi-class classifier trained with extensive data of a limited set of target analytes. Usually, classification performance degrades in the presence of mixtures that include interferents not represented in the training data. This issue limits the applicability of e-noses in real-world scenarios where interferents are a priori unknown. This paper investigates the feasibility of tackling this particular gas identification problem using one-class learning. We propose several training strategies for a one-class support vector machine to deal with gas mixtures composed of a target analyte and an interferent at different concentration levels. Our evaluation indicates that accurate identification of the presence of a target analyte is achievable if it is dominant in a mixture. For interferent-dominant mixtures, extensive training is required, which implies that an improvement in the generalization ability of the one-class model is needed.

Original languageEnglish
Title of host publicationInternational Symposium on Olfaction and Electronic Nose, ISOEN 2022 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665458603
DOIs
StatePublished - 2022
Externally publishedYes
Event2022 IEEE International Symposium on Olfaction and Electronic Nose, ISOEN 2022 - Aveiro, Portugal
Duration: 29 May 20221 Jun 2022

Publication series

NameInternational Symposium on Olfaction and Electronic Nose, ISOEN 2022 - Proceedings

Conference

Conference2022 IEEE International Symposium on Olfaction and Electronic Nose, ISOEN 2022
Country/TerritoryPortugal
CityAveiro
Period29/05/221/06/22

Keywords

  • electronic nose
  • gas identification
  • gas mixture
  • one-class learning
  • unknown interferent

Fingerprint

Dive into the research topics of 'Towards Gas Identification in Unknown Mixtures Using an Electronic Nose with One-Class Learning'. Together they form a unique fingerprint.

Cite this