In this work, non-targeted approaches relying on HPLC-UV chromatographic fingerprints were evaluated to address coffee characterization, classification, and authentication by chemometrics. In general, high-performance liquid chromatography with ultraviolet detection (HPLC-UV) fingerprints were good chemical descriptors for the classification of coffee samples by partial least squares regression-discriminant analysis (PLS-DA) according to their country of origin, even for nearby countries such as Vietnam and Cambodia. Good classification was also observed according to the coffee variety (Arabica vs. Robusta) and the coffee roasting degree. Sample classification rates higher than 89.3% and 91.7% were obtained in all the evaluated cases for the PLS-DA calibrations and predictions, respectively. Besides, the coffee adulteration studies carried out by partial least squares regression (PLSR), and based on coffees adulterated with other production regions or variety, demonstrated the good capability of the proposed methodology for the detection and quantitation of the adulterant levels down to 15%. Calibration, cross-validation, and prediction errors below 2.9%, 6.5%, and 8.9%, respectively, were obtained for most of the evaluated cases.

BACKGROUND Coffee is one of the most popular beverages around the world, consumed as an infusion of ground roasting coffee beans with a characteristic taste and flavor. Two main varieties, Arabica and Robusta, are produced worldwide. Furthermore, interest of consumers in quality attributes related to coffee production region and varieties is increasing. Thus, it is necessary to encourage the development of simple methodologies to authenticate and guarantee the coffee origin, variety and roasting degree, aiming to prevent fraudulent practices. RESULTS C18 high‐performance liquid chromatography with fluorescence detection (HPLC‐FLD) fingerprints obtained after brewing coffees without any sample treatment other than filtration (i.e. considerably reducing sample manipulation) were employed as sample chemical descriptors for subsequent coffee characterization and classification by principal component analysis (PCA) and partial least squares regression‐discriminant analysis (PLS‐DA). PLS‐DA showed good classification capabilities regarding coffee origin, variety and roasting degree when employing HPLC‐FLD fingerprints, although overlapping occurred for some sample groups. However, the discrimination power increased when selecting HPLC‐FLD fingerprinting segments richer in discriminant features, which were deduced from PLS‐DA loading plots. In this case, excellent separation was observed and 100% classification rates for both PLS‐DA calibrations and predictions were obtained (all samples were correctly classified within their corresponding groups). CONCLUSION HPLC‐FLD fingerprinting segments were3 found to be suitable chemical descriptors for discriminating the origin (country of production), variety (Arabica and Robusta) and roasting degree of coffee. Therefore, HPLC‐FLD fingerprinting can be proposed as a feasible, simple and cheap methodology to address coffee authentication, especially for developing coffee production countries. © 2020 Society of Chemical Industry

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