P49 - From Whisky to Aroma Investigating mixture data for odor prediction
- Event
- 16. Dresdner Sensor-Symposium 2022
2022-12-05 - 2022-12-07
Dresden - Band
- Poster
- Chapter
- Smart Sensors/Edge Computing/Künstliche Intelligenz in der Sensorik
- Author(s)
- S. Singh, A. Strube, A. Grasskamp, H. Haugh - Fraunhofer Institute for Process Engineering and Packaging IVV, Freising/D, S. Saloman, T. Scholz, B. Saha, S. Hettenkofer - Fraunhofer Institute for Integrated Circuits IIS, Erlangen/D, T. Gorges - Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen/D
- Pages
- 224 - 229
- DOI
- 10.5162/16dss2022/P49
- ISBN
- 978-3-9819376-7-1
- Price
- free
Abstract
Hedonic impression of food products largely depends on consistency, taste, and smell. While process control over other factors is quite feasible, perception of smell is not something easily predictable or measurable as volatile organic compounds (VOCs) are immensely diverse in odorant quality and detection threshold. Furthermore, investigating key aroma compounds in food products is costly in time and effort. Therefore, it is desirable to develop a system for the efficient and reliable interpretation of the decisive aroma components that influence consumer satisfaction. Recent years have seen considerable progress in computer aided analysis of molecules for different purposes. To avail ourselves of this progress, we utilized machine learning methods to predict the aroma qualities of whisky spirits, which are classically valued for their diverse aroma profiles depending on process parameters like aging duration, cask origin and blending.