New accepted paper at The 2nd International Electronic Conference on Processes (2023) [14.07.23]
Marvin Anker, Christian Krupitzer und Christine Borsum from the Department of Food Informatics are Co-Authors of the peer reviewed conference paper "Prediction of Aroma Partitioning Using Machine Learning" at The 2nd International Electronic Conference on Processes (2023)(CORE Rating: IT; Peer reviewed).
The publication "Prediction of Aroma Partitioning Using Machine Learning" by Marvin Anker (Department of Food Informatics and Computational Science Hub, University of Hohenheim, 70599 Stuttgart, Germany) with the co-authors, Christian Krupitzer (Department of Food Informatics and Computational Science Hub, University of Hohenheim, 70599 Stuttgart, Germany), Yanyan Zhang (Department of Flavor Chemistry, University of Hohenheim, 70599 Stuttgart, Germany), Christine Borsum (Department of Food Informatics and Computational Science Hub, University of Hohenheim, 70599 Stuttgart, Germany and Department of Process Engineering (Essential Oils, Natural Cosmetics), University of Applied Sciences Kempten, 87406 Kempten, Germany) was accepted at The 2nd International Electronic Conference on Processes (2023)(Peer reviewed).
Intensive research in the field over the past decades highlighted the complexity of aroma partition. Still, no general model for predicting aroma matrix interactions could be described. The vision outlined here is to discover the blueprint for the prediction of aroma partitioning behavior in complex foods by using machine learning techniques. Therefore, known physical relationships governing aroma release are combined with machine learning to predict the Kmg value of aroma compounds in foods of different compositions. The approach will be optimized on a data set of a specific food product. Afterward, the model should be transferred using explainable artificial intelligence (XAI) to a different food category to validate its applicability. Furthermore, we can transfer our approach to other relevant questions in the food field such as aroma quantification, extraction processes, or food spoilage.
The publication is available at: www.mdpi.com/2673-4591/37/1/48
