| Literature DB >> 32213986 |
Nerea Núñez1, Xavi Collado1, Clara Martínez1, Javier Saurina1,2, Oscar Núñez1,2,3.
Abstract
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.Entities:
Keywords: HPLC-UV fingerprinting; coffee; food adulteration; food authentication; non-targeted analysis; partial least squares regression (PLSR); principal component analysis (PCA)
Year: 2020 PMID: 32213986 DOI: 10.3390/foods9030378
Source DB: PubMed Journal: Foods ISSN: 2304-8158