Automated odor-blending with one-pot Bayesian optimization

The creation of new odors by blending existing ones is usually done manually based on the human sense. To enable robots to perform this automatically, we developed an automated odor-blending system. In this system, an olfactory sensor system composed of an array of Membrane-type Surface stress Sensors (MSSs) performs odor measurement of a blended liquid, and Bayesian optimization controls the blending concentration. The actual blending of the liquid samples is performed by automated syringe pumps. Our system performs odor-blending by injecting liquid samples into a pot or by draining some of the liquid from the pot. The one-pot strategy has the advantage of reducing the amount of liquid samples used in the entire optimization task and reduces the problem of pot replacement. To implement this one-pot strategy effectively, a Drainable One-Pot Bayesian Optimization (DOPBO) algorithm was developed and applied to our system. The system was tested using a ternary liquid mixture. To enable robots to perform the creation of new odors by blending existing ones automatically, we developed an automated odor-blending system using membrane-type surface stress sensors and machine learning.