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 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.
Author: | Marvin AnkerORCiD, Christian KrupitzerORCiD, Yanyan ZhangORCiD, Christine BorsumORCiDGND |
---|---|
DOI: | https://doi.org/10.3390/ECP2023-14707 |
Identifier: | 2673-4591 OPAC HS OPAC extern |
Parent Title (English): | Engineering Proceedings |
Publisher: | MDPI |
Place of publication: | Basel |
Document Type: | conference proceeding (article) |
Conference: | 2nd International Electronic Conference on Processes: Process Engineering—Current State and Future Trends (ECP 2023), 17–31 May 2023, ONLINE |
Language: | English |
Date of Publication (online): | 2023/07/10 |
Year of first Publication: | 2023 |
Volume: | 37 |
Issue: | 1 |
Number of pages: | 7 Seiten |
First Page: | 1 |
Last Page: | 7 |
Institutes: | Fakultät Maschinenbau |
KLEVERTEC | |
Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften |
Open Access: | open_access |
Research focus: | FSP3: Produktion |
Publication Lists: | Borsum, Christine |
Publication reviewed: | begutachtet |
Licence (German): | Creative Commons - CC BY - Namensnennung 4.0 International |
Release Date: | 2024/01/29 |