Please use this identifier to cite or link to this item:
Full metadata record
DC FieldValueLanguage
dc.creatorBarros, Hanna Elisia Araújo de-
dc.creatorAlexandre, Ana Cláudia Silveira-
dc.creatorCampolina, Gabriela Aguiar-
dc.creatorAlvarenga, Gabriela Fontes-
dc.creatorSilva, Lara Maria dos Santos Ferraz e-
dc.creatorNatarelli, Caio Vinicius Lima-
dc.creatorCarvalho, Elisângela Elena Nunes-
dc.creatorVilas Boas, Eduardo Valério de Barros-
dc.identifier.citationBARROS, H. E. A. de et al. Edible seeds clustering based on phenolics and antioxidant activity using multivariate analysis. LWT - Food Science and Technology, [S.I.], v. 152, Dec. 2021. DOI:
dc.description.abstractEdible seeds, especially those known by the population as nuts, have their consumption associated with functional appeal. The present study aimed to compare and group nine different seeds, traditional and regional, according to their similarities, in terms of moisture, total phenolic compounds (TPC) and antioxidant activity, through multivariate analyses. All results were submitted to Principal Component Analysis (PCA), Hierarchical Clusters (HCA) and Kohonen's self-organizing maps (ANN/KSOM). The seeds differed in terms of moisture content, TPC and antioxidant activity. The walnut butterfly stood out with the highest levels of TPC and antioxidant activity. In the multivariate analyses application, three groups were formed: i) hazel, baru, Brazil, macadamia, almond and cashew; ii) pequi and marolo; iii) walnut butterfly. It is concluded that the seeds can be separated into three groups, with ANN/KSOMs being the most self-explanatory analysis and that regional seeds are nutritionally similar to those traditionally consumed.pt_BR
dc.rightsacesso abertopt_BR
dc.sourceLWT - Food Science and Technologypt_BR
dc.subjectBioactive compoundspt_BR
dc.subjectPrincipal component analysispt_BR
dc.subjectHierarchical clusters analysispt_BR
dc.subjectArtificial neural networkpt_BR
dc.subjectCompostos bioativospt_BR
dc.subjectAnálise de Componentes Principaispt_BR
dc.subjectMétodos hierárquicos da análise de clusterpt_BR
dc.subjectRede neural artificialpt_BR
dc.titleEdible seeds clustering based on phenolics and antioxidant activity using multivariate analysispt_BR
Appears in Collections:DCA - Artigos publicados em periódicos

This item is licensed under a Creative Commons License Creative Commons