Caracterização da pasta de frutas de Camu-Camu (Myrciaria dubia) de sete diferentes genótipos e seus rankings usando métodos estatísticos PCA e HCA

Universidade Federal do Pará. Instituto de Ciências Exatas e Naturais. Belém, PA, Brasil.Universidade Federal do Pará. Instituto de Ciências Exatas e Naturais. Belém, PA, Brasil.Universidade Federal do Pará. Instituto de Ciências Exatas e Naturais. Belém, PA, Brasil.Embrapa Amazônia Oriental. Laboratório de Propagação de Plantas. Belém, PA, Brasil.Universidade Federal do Pará. Instituto de Tecnologia. Belém, PA, Brasil.Ministério da Saúde. Secretaria de Vigilância em Saúde. Instituto Evandro Chagas. Ananindeua, PA, Brasil.Universidade Federal do Pará. Instituto de Ciências Exatas e Naturais. Belém, PA, Brasil.Due to the economic potential of camu-camu, considering its high concentration of vitamin C, the aim of this work was to evaluate the quality and genetic variability seven accessions of camucamu conserved in genebank (BAG) of Embrapa Amazônia Oriental, in the state of Pará, Amazon region, Brazil. The fruits of camucamuzeiro were analyzed for physicochemical characterization (standard methodologies) and mineral composition. The data were subjected to multivariate statistical analysis, using the techniques of cluster analysis and principal component analysis. The formation of different groups for each genotype, which shows the genetic variability and the dissimilarity of the genotypes in the species data, may be used to guide the selection of promising genotypes to enrich the programs of genetic improvement of the camucamuzeiro. Ascorbic acid showed levels above 1000 mg 100 g-1, however, genotypes 4, 1 and 2 show the highest potential and the most promising nutritional capacity, but genotype 4 showed good characteristics for the moisture, acidity, carbohydrates, Cu and Zn and differs totally from others about the total soluble solids (TSS) and flavon-3-ol, features that make it the most promising genotype. It was possible to separate the seven different genotypes using multivariate analysis (hierarchical cluster analysisHCA and principal component analysis-PCA)