Towards Artificial Intelligent Olfactory Systems - Zeh, Gina; Koehne, Maximilian; Grasskamp, Andreas; Haug, Helen; Singh, Satnam; Sauerwald, Tilman (International Symposium on Olfaction and Electronic Nose, ISOEN 2022 - Proceedings, 2022)
Mimicking the human sense of olfaction with technical systems is a challenging task that requires consideration and understanding of biological olfactory systems, high quality gas analysis, sensor technology and data science. We present an approach for such an artificial intelligent olfactory system, which is just as the olfactory system of humans divided in four units: a sampling unit, a gas chromatograph, the detector/sensor unit and an artificial intelligence as brain. Model-based approaches for characterizing tools and system compounds are used to optimize the sampling of odorants, to predict the chromatographical behavior of analytes and to find the ideal composition of system compounds for an application-specific sensor system. This is complemented with machine-learning software for discriminating and identifying single compounds in an odor mixture. Therefore, a comprehensive toolbox, consisting of different software tools as well as hardware tools is considered as the key to artificial intelligent olfactory systems. The integration of these four units brings together all information to make sense of what is measured. The four system compounds have been evaluated and optimized individually. The interplay between the components has been described and herein1 we demonstrate our roadmap towards a small, inexpensive system.
CRIS-Team Scopus Importer:2022-07-08
Keywords: artificial intelligence; automatization; data science; gas chromatography; olfaction; sensor systems