Development of Machine Learning Models for Gas Identification Based on Transfer Functions

Abstract

Various applications of gas sensors have been envisioned in many fields along with the recent development in information and communication technology (ICT). Gas Identification plays a central role in gas sensor applications including artificial olfaction. In the conventional gas identification protocol, however, a strict gas flow control is required to reproduce comparable sensing signals. To eliminate such a severe constraint and identify gas species with an arbitrary gas injection pattern, here we report an analysis approach based on transfer function, which represents the relationship between inputs and outputs (i.e. a gas input pattern and the resultant sensing signals). In this study, we developed machine learning models which can identify gas species from an arbitrary gas injection pattern. Even though the sample gases were randomly injected, we successfully identified solvent vapors by the transfer functions with the classification accuracy of 0.98±0.03. This study provides a versatile data analysis platform which is independent of gas flow control.