Amb motiu del tancament d'estiu, la validació de documents es reprendrà a partir del 28 d'agost de 2026. Disculpeu les molèsties.
Con motivo del cierre de verano, la validación de documentos se reanudará a partir del 28 de agosto de 2026. Disculpad las molestias
Due to the summer closure, document validation will resume starting August 28, 2026. We apologize for any inconvenience.

Document type

Article

Version

Accepted version

Publication date

Publication license

cc-by-nc-nd (c) Elsevier B.V., 2021
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/177134

Non-targeted high-performance liquid chromatography with ultraviolet and fluorescence detection fingerprinting for the classification, authentication, and fraud quantitation of instant coffee and chicory by multivariate chemometric methods

Journal Title

Director/Tutor

Journal ISSN

Volume Title

Abstract

Non-targeted strategies based on high-performance liquid chromatography with ultraviolet detection (HPLC-UV) and fluorescence detection (HPLC-FLD) fingerprints were evaluated to accomplish the classification and authentication of instant coffee (40 samples), instant decaf coffee (26 samples), and chicory (22 samples, including both ground and instant), as well as to detect and quantify frauds based on chicory adulteration by multivariate chemometric methods. HPLC-UV and HPLC-FLD fingerprints were simultaneously obtained with a HPLC-UV-FLD instrument, and they proved to be excellent chemical descriptors for the classification of coffee and decaf coffee against chicory samples by partial least squares regression-discriminant analysis (PLS-DA). In contrast, HPLC-UV fingerprints improved the classification results when addressing coffee against decaf coffee samples (94.4% classification rate in comparison to 83.3% for HPLC-FLD fingerprints). Besides, the proposed methodologies resulted to be excellent to detect and quantify fraud levels in coffee and decaf coffee samples adulterated with chicory by using partial least squares (PLS) regression, exhibiting good calibration linearities, calibration errors, and prediction errors. In this case, improved capabilities were observed with HPLC-FLD fingerprints, providing better PLS calibration linearities (R2>0.999), lower calibration errors (≤0.8%), and similar to better prediction errors (2.9-3.2%) in comparison to HPLC-UV fingerprints.

Citation

Citation

NÚÑEZ, Nerea, et al. Non-targeted high-performance liquid chromatography with ultraviolet and fluorescence detection fingerprinting for the classification, authentication, and fraud quantitation of instant coffee and chicory by multivariate chemometric methods. LWT Food Science and Technology. 2021. Vol. 147, num. 111646. ISSN 0023-6438. [consulted: 17 of August of 2026]. Available at: https://hdl.handle.net/2445/177134

Export metadata

JSON - METS

Share record