Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/175436
Full metadata record
DC FieldValueLanguage
dc.contributor.advisorNúñez Burcio, Oscar-
dc.contributor.advisorSaurina, Javier-
dc.contributor.authorRodríguez Javier, Luis R.-
dc.date.accessioned2021-03-19T12:42:17Z-
dc.date.available2021-03-19T12:42:17Z-
dc.date.issued2021-01-
dc.identifier.urihttp://hdl.handle.net/2445/175436-
dc.descriptionTreballs Finals de Grau de Química, Facultat de Química, Universitat de Barcelona, Any: 2021, Tutors: Oscar Núñez Burcio, Javier Saurina Purroyca
dc.description.abstractIn recent years, there has been a growing interest in the quality of natural foods from society. Consequently, the demand for food traceability has increased and, along with it, measures to satisfy consumer concerns. One of these tools is the Protected Designation of Origin (PDO), which guarantees the quality and geographical origin of a product. In the case of paprika, a red spice obtained after drying and grinding certain varieties of red peppers, there are seven PDOs in the European Union. Even with this quality seal, the products do not escape from food fraud like adulterations with chemicals or paprika from other geographical origins. To avoid this, it is essential to develop new techniques to solve these problems. This project aims to create a non-targeted method that allows the classification of paprika according to their production region. To this end, chromatographic fingerprints obtained by highperformance liquid chromatography with fluorescence (HPLC-FLD) and ultraviolet (HPLC-UV) detection were proposed as chemical descriptors. The chromatographic separation was performed on a C18 reversed-phase column under gradient elution using acidified water (0.1% formic acid) and acetonitrile as the mobile phase components. The obtained chromatographic fingerprints were analyzed using chemometric techniques such as principal component analysis (PCA) and partial least squares - discriminant analysis (PLS-DA). In these studies, apart from ensuring that the proposed methods were robust and reproducible, a classification of the samples with 95.8% accuracy was obtained for both HPLCUV and HPLC-FLD fingerprintsca
dc.format.extent49 p.-
dc.format.mimetypeapplication/pdf-
dc.language.isoengca
dc.rightscc-by-nc-nd (c) Rodríguez, 2021-
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.sourceTreballs Finals de Grau (TFG) - Química-
dc.subject.classificationPebre vermellcat
dc.subject.classificationFrau alimentaricat
dc.subject.classificationCromatografia de líquids d'alta resoluciócat
dc.subject.classificationTreballs de fi de graucat
dc.subject.otherPaprikaeng
dc.subject.otherFood fraudeng
dc.subject.otherHigh performance liquid chromatographyeng
dc.subject.otherBachelor's theses-
dc.titleHPLC-FLD and HPLC-UV Fingerprinting for the Characterization, Classification and Authentication of Paprikaeng
dc.title.alternativeCaracterización, Clasificación y Autenticación de Pimentones mediante huellas HPLC-FLD y HPLC-UVspa
dc.typeinfo:eu-repo/semantics/bachelorThesisca
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca
Appears in Collections:Treballs Finals de Grau (TFG) - Química

Files in This Item:
File Description SizeFormat 
TFG_QU Rodríguez Javier, Luis R.pdf717.53 kBAdobe PDFView/Open


This item is licensed under a Creative Commons License Creative Commons