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