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Combining multielement analysis and chemometrics to trace the geographical origin of Rocha pear

2019-04Journal article Restricted access
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dc.contributor.authorCoelho, Inês
dc.contributor.authorMatos, Ana Sofia
dc.contributor.authorTeixeira, Rita
dc.contributor.authorNascimento, Ana
dc.contributor.authorBordado, João
dc.contributor.authorDonard, Olivier
dc.contributor.authorCastanheira, Isabel
dc.date.accessioned2020-04-22T12:28:32Z
dc.date.available2020-04-22T12:28:32Z
dc.date.issued2019-04
dc.descriptionThe authors would like to thank Instituto Politécnico de Castelo Branco for their collaboration in collecting samples.pt_PT
dc.description.abstractThis work aims at characterizing the mineral profile of a Portuguese variety of pear, Rocha pear, and study the relationship between the nutritional profile and geographical origin. Multielement analysis of twenty-four elements (Al, As, B, Be, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, P, Pb, Se, Sn, Sr, Tl, Zn) was performed using an ICP-MS and ICP-OES, on 50 samples originating from two regions, one with Protected Designation of Origin (PDO), known as “Pera Rocha do Oeste”, and the other without (nPDO). Correlation analysis was performed to check for potential relationships between elements. Results from PDO and nPDO regions were compared using chemometric tools, namely Principal Component Analysis, Hierarquical Cluster Analysis and Linear Discriminant Analysis (LDA). The predictive model, built and validated, classified samples according to their regional origin with 100% accuracy. The present study shows that multielement analysis combined with the appropriate statistical tools can be a valuable contribution from the identification of the geographical provenance of Rocha Pears. It provides important nutritional information regarding the mineral composition of Rocha pears, not available until know, promoting the linkage between the nutritional profile and geographical origin, filling the gap of absent values in FCDBs.pt_PT
dc.description.abstractHighlights: Mineral characterization of Rocha pear was done by ICP-MS; Chemometric techniques (PCA, HCA and LDA) were applied to the data; Geographical origins were discriminated based on mineral content; Tl, Mn, Al, Ni and Na were the elements with highest variability between regions.pt_PT
dc.description.sponsorshipThis project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement Nº 739568.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationJ Food Compost Anal. 2019;77:1-8. doi:10.1016/j.jfca.2018.12.005pt_PT
dc.identifier.doi10.1016/j.jfca.2018.12.005pt_PT
dc.identifier.issn0889-1575
dc.identifier.urihttp://hdl.handle.net/10400.18/6479
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherElsevierpt_PT
dc.relationProgressing towards the construction of METROFOOD-RI
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/abs/pii/S0889157518312717pt_PT
dc.subjectFood Analysispt_PT
dc.subjectFood Compositionpt_PT
dc.subjectAuthenticitypt_PT
dc.subjectRocha Pearpt_PT
dc.subjectClassificationpt_PT
dc.subjectTraceabilitypt_PT
dc.subjectInorganic Markerspt_PT
dc.subjectFruits (agro-product)pt_PT
dc.subjectMultielement Analysispt_PT
dc.subjectChemometricspt_PT
dc.subjectComposição dos Alimentospt_PT
dc.titleCombining multielement analysis and chemometrics to trace the geographical origin of Rocha pearpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardTitleProgressing towards the construction of METROFOOD-RI
oaire.awardURIinfo:eu-repo/grantAgreement/EC/H2020/739568/EU
oaire.citation.endPage8pt_PT
oaire.citation.startPage1pt_PT
oaire.citation.titleJournal of Food Composition and Analysispt_PT
oaire.citation.volume77pt_PT
oaire.fundingStreamH2020
project.funder.identifierhttp://doi.org/10.13039/501100008530
project.funder.nameEuropean Commission
rcaap.embargofctDe acordo com política editorial da revista.pt_PT
rcaap.rightsembargoedAccesspt_PT
rcaap.typearticlept_PT
relation.isProjectOfPublication27905750-7224-4d46-9b63-35b5e2cc834a
relation.isProjectOfPublication.latestForDiscovery27905750-7224-4d46-9b63-35b5e2cc834a

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