Towards Gas Identification in Unknown Mixtures Using an Electronic Nose with One-Class Learning
- Resource Type
- Conference
- Authors
- Fan, Han; Jonsson, Daniel; Schaffernicht, Erik; Lilienthal, Achim J.
- Source
- 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN) Olfaction and Electronic Nose (ISOEN), 2022 IEEE International Symposium on. :1-4 May, 2022
- Subject
- Bioengineering
Components, Circuits, Devices and Systems
Engineered Materials, Dielectrics and Plasmas
General Topics for Engineers
Robotics and Control Systems
Signal Processing and Analysis
Training
Support vector machines
Training data
Nose
Electronic noses
gas identification
gas mixture
unknown interferent
one-class learning
electronic nose
- Language
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.