Mid‐Infrared Spectroscopy and Machine Learning for Chlorogenic Acid Quantification in Coffee
Year: 2025
Type: article
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.
Source: Chemie Ingenieur Technik
DOI: 10.1002/cite.70049
Authors: Deborah Herdt, Felix Wühler, Thomas Kunz, Victoria Schiwek, Sarah Kühnemuth, Brian Gibson, Matthias Rädle
Institutions: Technische Hochschule Mannheim, Technische Universität Berlin
Language: English
Subfield: Analytical Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $4,030
Award: G6014720607
OpenAlex ID: W4416977180