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

• 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.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences (hsv//eng)

Nyckelord

gas identification

gas mixture

unknown interferent

one-class learning

electronic nose

Computer Science

Datavetenskap

Publikations- och innehållstyp

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