Self-learning multichannel olfactory display

Recreating odors is considerably more challenging than, for example, recreating colors. In general, it requires analyzing the chemical composition of the odor to be recreated 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 comparison, original and synthetic odor could be measured by an electronic nose, such as differential mobility spectrometry (DMS). In this article a self-learning multichannel olfactory display that enables mixing of 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 mea- surement of the original odor. The article demonstrates via simulations and tests with real odors that the self-learning multichannel odor display can find synthetic odors resembling the original odors. Simulations proved that the selected algorithm is capable of yielding synthetic odors almost identical to the original odor. Tests with isopropanol-ethanol mixtures also resulted in accurately reproduced synthetic mixtures, but demonstrated challenges due to the unequal chemical imprints in DMS measurements.