Classification and Authentication of Paprika by UHPLC-HRMS Fingerprinting and Multivariate Calibration Methods (PCA and PLS-DA)
1
Department of Chemical Engineering and Analytical Chemistry, University of Barcelona, Martí i Franquès 1-11, E08028 Barcelona, Spain
2
Research Institute in Food Nutrition and Food Safety, University of Barcelona, Recinte Torribera, Av. Prat de la Riba 171, Edifici de Recerca (Gaudí), Santa Coloma de Gramenet, E08921 Barcelona, Spain
3
Serra Húnter Fellow, Generalitat de Catalunya, Rambla de Catalunya 19-21, E08007 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Foods 2020, 9(4), 486; https://doi.org/10.3390/foods9040486
Received: 18 March 2020 / Revised: 30 March 2020 / Accepted: 31 March 2020 / Published: 13 April 2020
(This article belongs to the Special Issue Recent Developments in the Applications of Fingerprinting Technology in the Food Field)
In this study, the feasibility of non-targeted UHPLC-HRMS fingerprints as chemical descriptors to address the classification and authentication of paprika samples was evaluated. Non-targeted UHPLC-HRMS fingerprints were obtained after a simple sample extraction method and C18 reversed-phase separation. Fingerprinting data based on signal intensities as a function of m/z values and retention times were registered in negative ion mode using a q-Orbitrap high-resolution mass analyzer, and the obtained non-targeted UHPLC-HRMS fingerprints subjected to unsupervised principal component analysis (PCA) and supervised partial least squares regression-discriminant analysis (PLS-DA) to study sample discrimination and classification. A total of 105 paprika samples produced in three different regions, La Vera PDO and Murcia PDO, in Spain, and the Czech Republic, and all of them composed of samples of at least two different taste varieties, were analyzed. Non-targeted UHPLC-HRMS fingerprints demonstrated to be excellent sample chemical descriptors to achieve the authentication of paprika production regions with 100% sample classification rates by PLS-DA. Besides, the obtained fingerprints were also able to perfectly discriminate among the different paprika taste varieties in all the studied cases, even in the case of the different La Vera PDO paprika tastes (sweet, bittersweet, and spicy) which are produced in a very small region.
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Keywords:
UHPLC-HRMS fingerprinting; non-targeted analysis; food authentication; paprika; product designation of origin; principal component analysis; partial least squares regression-discriminant analysis
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MDPI and ACS Style
Barbosa, S.; Saurina, J.; Puignou, L.; Núñez, O. Classification and Authentication of Paprika by UHPLC-HRMS Fingerprinting and Multivariate Calibration Methods (PCA and PLS-DA). Foods 2020, 9, 486. https://doi.org/10.3390/foods9040486
AMA Style
Barbosa S, Saurina J, Puignou L, Núñez O. Classification and Authentication of Paprika by UHPLC-HRMS Fingerprinting and Multivariate Calibration Methods (PCA and PLS-DA). Foods. 2020; 9(4):486. https://doi.org/10.3390/foods9040486
Chicago/Turabian StyleBarbosa, Sergio; Saurina, Javier; Puignou, Lluís; Núñez, Oscar. 2020. "Classification and Authentication of Paprika by UHPLC-HRMS Fingerprinting and Multivariate Calibration Methods (PCA and PLS-DA)" Foods 9, no. 4: 486. https://doi.org/10.3390/foods9040486
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