Prediction of Aroma Partitioning Using Machine Learning

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...

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Bibliographic Details
Published inEngineering proceedings Vol. 37; no. 1; p. 48
Main Authors Marvin Anker, Christian Krupitzer, Yanyan Zhang, Christine Borsum
Format Journal Article
LanguageEnglish
Published MDPI AG 01.07.2023
Subjects
aroma release
explainable artificial intelligence
food reformulation
machine learning
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Summary: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.
ISSN:2673-4591
DOI:10.3390/ECP2023-14707