Authentication of Coffea arabica: Comparative Multispectral Discrimination by NIR and UV–Vis Spectroscopy Aided by Artificial Neural Networks and Data Fusion

• Published in: Food frontiers Vol. 6; no. 2; pp. 763 - 774
• Main Authors: Moll, Vanessa, Beć, Krzysztof B., Handle, Lara, Huck, Christian W.
• Format: Journal Article
• Language: English
• Published: Beijing John Wiley & Sons, Inc 01.03.2025; Wiley
• Subjects: Artificial neural networks; Caffeine; Classification; Coffea arabica; Coffee; commercial Arabica coffee; Data integration; Food; Food quality; Fourier transforms; Fraud; Genetic diversity; handheld near‐infrared (NIR); Infrared analysis; machine learning; multispectral approach; Near infrared radiation; Neural networks; on‐site analysis; rapid discrimination; Spectral analysis; Spectroscopy; Spectrum analysis; Supply chains; Support vector machines
• ISSN: 2643-8429; 2643-8429
• DOI: 10.1002/fft2.529

ABSTRACT Coffea arabica L. (Arabica) is considered the highest quality coffee species and provides the majority of the worldwide production. Among its groups are rare and expensive coffees; consequently, Arabica is subject to fraudulent practices. Currently, a selection of methods allow to precisely discriminate between the primary commercial coffee species (Arabica and Robusta). However, Arabica coffees offer a very restricted genetic diversity, and the authentication of its groups by spectroscopy has not yet been demonstrated to be feasible. This study aimed to step beyond the current state‐of‐the‐art, evaluating the possibility of a multispectral approach to discriminate shelf‐ready coffee beans of the most significant Arabica groups, Typica and Bourbon. Spectral analysis was performed by a benchtop near‐infrared (NIR; 1000–2500 nm), a benchtop ultraviolet–visible (UV–Vis; 200–1000 nm), and a handheld Vis‐NIR (350–2500 nm) spectrometer, hyphenated with nonlinear classification techniques, including artificial neural networks (ANNs). Additionally, low‐level (LLDF) and mid‐level (MLDF) data fusion as well as variable selection (VS) methods were evaluated in their predictive performance. This study successfully demonstrates that closely related Arabica groups are discriminable from each other using a rapid multispectral approach, including direct on‐site analysis. The NIR region provided precise classification of the Arabica coffees (94%–98% accuracy), the Vis‐NIR (88%–93 %), and the UV–Vis (82%–93 %) region showed also good discrimination but left room for improvement. However, an LLDF of the Vis‐NIR and the UV–Vis region in combination with VS proved to be a potent tool to further refine the authentication of coffee groups and showed very good classification accuracies (91%–97%). This study evaluated the feasibility of a multispectral approach to perform rapid discrimination of shelf‐ready coffee beans of the most significant Arabica groups. Spectral analysis was conducted by a benchtop near‐infrared (NIR), UV‐Vis, and a handheld Vis‐NIR spectrometer. Classification performance was evaluated with non‐linear machine learning classification techniques including Artificial Neural Networks, low‐level‐ and mid‐level‐ data fusion as well as Variable Selection methods.