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Gas identification is one of the most important functions of gas sensorsystems. To identify gas species from sensing signals, however, gas inputpatterns (e.g. the gas flow sequence) must be controlled or monitored preciselywith additional instruments such as pumps or mass flow controllers; otherwise,effective signal features for analysis are difficult to be extracted. Toward acompact and easy-to-use gas sensor system that can identify gas species, it isnecessary to overcome such restrictions on gas input patterns. Here we developa novel gas identification protocol that is applicable to arbitrary gas inputpatterns without controlling or monitoring any gas flow. By combining theprotocol with newly developed MEMS-based sensors (i.e. Membrane-type Surfacestress Sensors (MSS)), we have realized the gas identification with thefree-hand measurement, in which one can simply hold a small sensor chip nearsamples. From sensing signals obtained through the free-hand measurement, wehave developed machine learning models that can identify not only solventvapors but also odors of spices and herbs with high accuracies. Since no bulkygas flow control units are required, this protocol will expand theapplicability of gas sensors to portable electronics and wearable devices,leading to practical artificial olfaction.

Free-hand gas identification based on transfer function ratios without gas flow control