ORCID
- Brian Gibson: 0000-0002-1270-6532
Abstract
To predict chlorogenic acid (CGA) concentration in coffee during roasting, a machine learning algorithm was applied to mid-infrared (MIR) data. A total of 44 roasting samples between 140 and 220 °C, along with an unroasted control, were dry-heated in an eddy current roaster and subsequently ground and measured. CGA concentrations were predicted from MIR spectroscopy data using a multilayer perceptron (MLP) regressor and validated against a high-performance liquid chromatography with diode array detector reference. The algorithm performed spectral preprocessing and selected relevant wavenumber regions. The MLP-based model achieved a high coefficient of determination, outperforming classic peak evaluation, indicating that automated wavelength selection improves predictive accuracy.
Keywords
Artificial neuronal network, Chlorogenic acid quantification, Coffee roasting analysis, Machine learning, Mid-infrared spectroscopy
DOI Link
Publication Date
2026-01-01
Publication Title
Chemie-Ingenieur-Technik
Volume
98
Issue
3
ISSN
0009-286X
First Page
85
Last Page
97
Deposit Date
2026-05-11
Funding
This research was funded by Bundesministerium für Wirtschaft und Energie (BMWi) and the German Federation of Industrial Research Associations (AiF,ZF4013933DB7). We acknowledge Bestmalz (Wallertheim, Rheinland-Pfalz, Germany) for providing a laboratory fluidized bed roaster. Open access funding enabled and organized by Projekt DEAL.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Additional Links
Recommended Citation
Herdt, D., Wühler, F., Kunz, T., Schiwek, V., Kühnemuth, S., Gibson, B., & Rädle, M. (2026) 'Mid-Infrared Spectroscopy and Machine Learning for Chlorogenic Acid Quantification in Coffee', Chemie-Ingenieur-Technik, 98(3), pp. 85-97. Available at: 10.1002/cite.70049