• search hit 73 of 502
Back to Result List

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.

Download full text files

Export metadata

Additional Services

Search Google Scholar
Metadaten
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
Verstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.