This research demonstrates how machine learning estimates chlorogenic acid in coffee, suggesting enhanced accuracy via mid-infrared techniques.
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.
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Herdt et al. (2025) studied this question.
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