AUROMA – Automatic analysis of aroma profiles using machine learning

In dairy research, people are constantly looking for new methods that can help to optimize and control the quality of dairy products. This requires, among other things, a detailed knowledge of all essential aroma components as well as precursors to such.

By: Grith Mortensen

Gas chromatography with mass spectrometric detection (GC-MS) is often used for aroma analyzes. Currently, the data processing of GC-MS data is handled manually, which is very extensive and time consuming especially in large storage attempts. As a result, large trials with GC-MS often focus on already known aroma components, which increases the risk of overlooking information about new biomarkers that might otherwise be available in data. This gives rise to research within optimization of these workflows.

In the AUROMA project, the data analysis from GC-MS is completely automated using machine learning and chemometric data analysis. The automated method extracts far more information from each measurement and at the same time will be more accurate because an automated method does not depend on the user, as is currently the case.

Project period: April 2019 - March 2022

Budget: 2.212.000 DKK

Financing: Milk Levy Fund, Arla Foods, University of Copenhagen

Project manager: Rasmus Bro

Institution: Department of Food Science, University of Copenhagen

Participants: Department of Food Science University of Copenhagen and Arla Foods




Huiwen Yu, Dillen Augustijn, Rasmus Bro. Accelerating PARAFAC2 algorithms for non-negative complex tensor decomposition, Chemometrics and Intelligent Laboratory Systems 214, 15 July 2021, 104312.