Self-learning multichannel olfactory display

Recreating odors is considerably more challenging than, for example, recreating colors. Odor recreation necessitates knowledge of chemistry, sensory perception, sensors, automation, artificial intelligence, and machine learning. In general, recreating an odor requires analyzing its chemical composition by means of gas chromatography and/or mass spectrometry. However, odors can consist of hundreds of chemicals, which makes recreating them a complex, time-consuming and expensive task. Alternatively, one could try to create synthetic odors from a limited number of chemicals and compare them to the original odor. For the comparison, original and synthetic odors could be measured by an electronic nose, such as differential mobility spectrometry (DMS). In this article, a self-learning multichannel olfactory display that enables recreation of a target odor by mixing up to five chemicals was developed and tested. The display incorporates a machine learning module based on differential evolution algorithms for automatically finding a composition of a synthetic odor yielding a DMS measurement similar to the DMS measurement of the target odor. The article demonstrates via simulations and tests with real odors that the self-learning multichannel odor display can find synthetic odors resembling the target odors. Simulations proved that the selected algorithm is capable of yielding synthetic odors almost identical to the target odor. For two-component mixtures, the average deviations in flow rates between the target and trial odors were 0.8 sccm after ten and 0.3 sccm after 20 iterations, while for five-component mixtures the average deviations were 2.8 sccm after ten and 1.4 sccm after 20 iterations. Tests with isopropanol–ethanol mixtures also resulted in accurately reproduced synthetic mixtures but demonstrated challenges due to the unequal chemical imprints in DMS measurements. For ethanol, the average offset in the estimated flow rate was 2.2 sccm, while it was 7.0 sccm for isopropanol due to two runs with large offsets of 15 and 16 sccm respectively.