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https://doi.org/10.1016/J.FOODCONT.2024.110397


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Campo DC | Valor | Lengua/Idioma |
---|---|---|
dc.contributor.author | Castell, Ana | - |
dc.contributor.author | Arroyo-Manzanares, Natalia | - |
dc.contributor.author | López-García, Ignacio | - |
dc.contributor.author | Zapata, Félix | - |
dc.contributor.author | Viñas, Pilar | - |
dc.date.accessioned | 2025-07-10T10:43:29Z | - |
dc.date.available | 2025-07-10T10:43:29Z | - |
dc.date.issued | 2024-02-17 | - |
dc.identifier.citation | Food Control 161 (2024) 110397 | es |
dc.identifier.issn | 0956-7135 | - |
dc.identifier.uri | http://hdl.handle.net/10201/157130 | - |
dc.description | © 2024 The Authors. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/. This document is the Published version of a Published Work that appeared in final form in Food Control. To access the final edited and published work see https://doi.org/10.1016/J.FOODCONT.2024.110397 | - |
dc.description.abstract | Paprika is a spice whose composition and characteristics vary with its geographical origin and additionally is illegally adulterated with dyes to improve its appearance. This work proposes a strategy based on Fourier transform near infrared (FT-NIR) analysis and chemometric tools for its authentication and detection of fraud. A total of 115 paprika samples were analyzed, including paprika with protected designation of origin (PDO) labels from Spain, France and Hungary, and samples from China and Zambia. The proposed orthogonal partial least squares-discriminant analysis (OPLS-DA) models allow to distinguish paprika according to its PDO and variety, as well as to identify adulteration with Sudan dyes or Congo red. Partial least squares regressions allow to quantify the adulterant in paprika from 0.1 to 5 %. Chemometric models achieved high classification success rates and suitable linearities. The proposed strategy is presented as a comprehensive and effective tool to ensure paprika quality and authenticity, including the detection and quantification of adulteration with commercial dyes. | es |
dc.format | application/pdf | es |
dc.format.extent | 11 | es |
dc.language | eng | es |
dc.publisher | Elsevier | es |
dc.relation | Spanish MCIN (Project PID 2021-123201NB-I00 financed by MCIN/AEI/10.13039/ 501100011033/FEDER, EU), and University of Murcia (AEIP 2020-0005). | es |
dc.rights | info:eu-repo/semantics/openAccess | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Spices | es |
dc.subject | Sudan dyes | es |
dc.subject | Congo red | es |
dc.subject | Food fraud | es |
dc.subject | Food analysis | es |
dc.subject | NIR spectroscopy | es |
dc.subject.other | CDU::5 - Ciencias puras y naturales::54 - Química::543 - Química analítica | es |
dc.title | Authentication strategy for paprika analysis according to geographical origin and study of adulteration using near infrared spectroscopy and chemometric approaches | es |
dc.type | info:eu-repo/semantics/article | es |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S0956713524001142?via%3Dihub | es |
dc.identifier.doi | https://doi.org/10.1016/J.FOODCONT.2024.110397 | - |
dc.contributor.department | Departamento de Química Analítica | es |
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2024_Authentication strategy for paprika analysis using NIR.pdf | 2024_AuthenticationStrategyPaprikaAnalysisNIR | 4,24 MB | Adobe PDF | ![]() Visualizar/Abrir |
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