Analytical assurance of coffees' geographical indication (GI) authenticity is essential for producers and consumers. In this way, chemometric methods, electrochemical techniques, and 3D printed sensors become attractive to assure the coffee's quality. These sensors are low-cost, fast, and simple, with the possibility of miniaturization and portability. Therefore, 3D printed electrodes with chemometrics were used to classify-three Brazilian coffees from regions with GI. Further, Au/Gpt-PLA electrodes with partial least squares regression were used to detect the blending of GI coffee with traditional coffee. Soft independent modelling of class analogies coupled with cyclic voltammetry had the best performance, with 91-95% accuracy, specificity of 94-100%, and 80-83% sensitivity. Furthermore, the calibration models detected and quantified traditional coffee in all three coffees from regions with GI. The detection limits ranged from 1.4 to 10% (w/w), and quantification 4.6-32%, depending on the specific coffee.
Keywords: Coffee geographical indication; Cyclic voltammetry; Food authenticity; Multivariate calibration; Multivariate classification.
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