Prediction of Aroma Partitioning Using Machine Learning

Engineering Proceedings

article

Marvin Anker¹  Christian Krupitzer¹  Yanyan Zhang²  Christine Borsum¹ 

[1] Department of Food Informatics and Computational Science Hub, University of Hohenheim;Department of Flavor Chemistry, University of Hohenheim;Department of Process Engineering ,(Essential Oils, Natural Cosmetics), University of Applied Sciences Kempten

关键词: aroma release;    food reformulation;    machine learning;    explainable artificial intelligence;   

DOI  :  10.3390/ECP2023-14707

来源: mdpi

【 摘 要 】

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 Km gvalue 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.

【 授权许可】

Unknown