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Prediction of aroma partitioning using machine learning
1 , * 1, 2 , 3 , 1
1  University of Hohenheim, Dept. of Food Informatics, Stuttgart, Germany
2  University of Applied Sciences, Chair of Process Engineering (Essential Oils), Kempten, Germany
3  University of Hohenheim, Dept. of Flavor Chemistry, Stuttgart, Germany
Academic Editor: Chi-Fai Chau


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 like aroma quantification, extraction processes, or food spoilage.

Keywords: aroma release; food reformulation; machine learning; explainable artificial intelligence
Comments on this paper
helen dam
Thank you for providing the abstract and sharing information about your research on aroma partitioning in complex foods using machine learning techniques. It seems that your study aims to develop a predictive model for aroma partitioning behavior by combining known physical relationships with machine Pokemon Infinite Fusion learning algorithms. The model will be optimized using a specific food product dataset and then validated on a different food category to assess its applicability.