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dc.contributor.authorGalindo Luján, Rocío-
dc.contributor.authorPont, Laura-
dc.contributor.authorQuispe Jacobo, Fredy Enrique-
dc.contributor.authorSanz Nebot, Victoria-
dc.contributor.authorBenavente, Fernando-
dc.date.accessioned2024-09-30T18:41:18Z-
dc.date.available2024-09-30T18:41:18Z-
dc.date.issued2024-06-17-
dc.identifier.citationGalindo-Luján, R.; Pont, L.; Quispe-Jacobo, F.E.; Sanz-Nebot, V.; & Benavente, F. (2024). Matrix-assisted laser desorption ionization time-of-flight mass spectrometry combined with chemometrics for protein profiling and classification of boiled and extruded quinoa from conventional and organic crops. Foods,13(12),1906. doi:10.3390/foods13121906es_PE
dc.identifier.issn2304-8158-
dc.identifier.urihttps://hdl.handle.net/20.500.12955/2583-
dc.description.abstractQuinoa is an Andean crop that stands out as a high-quality protein-rich and gluten-free food. However, its increasing popularity exposes quinoa products to the potential risk of adulteration with cheaper cereals. Consequently, there is a need for novel methodologies to accurately characterize the composition of quinoa, which is influenced not only by the variety type but also by the farming and processing conditions. In this study, we present a rapid and straightforward method based on matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS) to generate global fingerprints of quinoa proteins from white quinoa varieties, which were cultivated under conventional and organic farming and processed through boiling and extrusion. The mass spectra of the different protein extracts were processed using the MALDIquant software (version 1.19.3), detecting 49 proteins (with 31 tentatively identified). Intensity values from these proteins were then considered protein fingerprints for multivariate data analysis. Our results revealed reliable partial least squares-discriminant analysis (PLS-DA) classification models for distinguishing between farming and processing conditions, and the detected proteins that were critical for differentiation. They confirm the effectiveness of tracing the agricultural origins and technological treatments of quinoa grains through protein fingerprinting by MALDI-TOF-MS and chemometrics. This untargeted approach offers promising applications in food control and the food-processing industry.es_PE
dc.description.sponsorshipThis study was supported by grant PID2021-127137OB-I00, unded byMCIN/AEI/10.13039/501100011033, and by “ERDF A way of making Europe”. The Bioanalysis group of the university of Barcelona is part of the INSA-UB Maria de Maeztu Unit of Excellence (Grant CEX2021-001234-M) funded by MCIN/AEI/FEDER, UE.es_PE
dc.formatapplication/pdfes_PE
dc.language.isoenges_PE
dc.publisherMDPIes_PE
dc.relation.ispartofurn:issn:2304-8158es_PE
dc.relation.ispartofseriesFoodses_PE
dc.rightsinfo:eu-repo/semantics/openAccesses_PE
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/es_PE
dc.sourceInstituto Nacional de Innovación Agrariaes_PE
dc.source.uriRepositorio Institucional - INIAes_PE
dc.subjectBoilinges_PE
dc.subjectConventional Farminges_PE
dc.subjectExtrusiones_PE
dc.subjectMaldiquantes_PE
dc.subjectMALDI-TOF-MSes_PE
dc.subjectMultivariatees_PE
dc.subjectData Analysises_PE
dc.subjectOrganic Farminges_PE
dc.subjectProteinses_PE
dc.subjectQuinoaes_PE
dc.titleMatrix-assisted laser desorption ionization time-of-flight mass spectrometry combined with chemometrics for protein profiling and classification of boiled and extruded quinoa from conventional and organic cropses_PE
dc.typeinfo:eu-repo/semantics/articlees_PE
dc.subject.ocdehttps://purl.org/pe-repo/ocde/ford#2.11.00es_PE
dc.publisher.countryCHes_PE
dc.identifier.doihttps://doi.org/10.3390/foods13121906-
dc.subject.agrovocBoilinges_PE
dc.subject.agrovocEbulliciónes_PE
dc.subject.agrovocConventional farminges_PE
dc.subject.agrovocAgricultura convencionales_PE
dc.subject.agrovocExtrusiones_PE
dc.subject.agrovocSpectrometryes_PE
dc.subject.agrovocEspectrometríaes_PE
dc.subject.agrovocMultivariate analysises_PE
dc.subject.agrovocAnálisis multivariantees_PE
dc.subject.agrovocData analysises_PE
dc.subject.agrovocAnálisis de datoses_PE
dc.subject.agrovocOrganic agriculturees_PE
dc.subject.agrovocAgricultura orgánicaes_PE
dc.subject.agrovocQuinoaes_PE
dc.subject.agrovocQuinuaes_PE
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