TY - CONF A1 - Anker, Marvin A1 - Krupitzer, Christian A1 - Zhang, Yanyan A1 - Borsum, Christine T1 - Prediction of Aroma Partitioning Using Machine Learning T2 - Engineering Proceedings N2 - 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 πΎπ‘šπ‘” 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. Y1 - 2023 UR - https://opus4.kobv.de/opus4-hs-kempten/frontdoor/index/index/docId/2084 SN - 2673-4591 VL - 37 IS - 1 SP - 1 EP - 7 PB - MDPI CY - Basel ER -