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Relationships between yellow and purple passion fruit variables1 1 Research developed at Universidade de São Paulo, Escola Superior de Agricultura “Luiz de Queiroz”, Departamento de Ciências Exatas, Piracicaba, SP, Brazil

Relações entre variáveis do maracujá amarelo e roxo

ABSTRACT

Brazil is the world’s largest producer of passion fruit, with the species Passiflora edulis Sims (yellow and purple passion fruit) being the most popular commercially. Due to the great economic importance of this culture, seeking high productivity, phytotechnical quality, and maintenance of existing germplasms, among others, it is necessary to carry out studies of the most diverse types of this culture. In this crop, the use of multivariate techniques has increasingly driven studies on genetic differences between passion fruit species. Therefore, this research aimed to identify the variables that deserve greater emphasis in experiments with two commercial species of Passiflora: Passiflora edulis Sims f. edulis (purple passion fruit) and Passiflora edulis Sims (yellow passion fruit), verifying if the species differ from each other. For this purpose, principal component analysis, multivariate analysis of variance (MANOVA), and Hotelling’s T2 test were performed. The principal component analysis proved to be effective, allowing the removal of five variables for yellow passion fruit and six variables for purple passion fruit and yellow passion fruit present contrasting differences between them, being the explanatory variables for the purple passion fruit the external and internal color and for the yellow passion fruit length, diameter, and citric acid.

Key words:
Passiflora edulis Sims f. edulis; Passiflora edulis Sims; multivariate analysis of variance; Hotelling’s T² test; principal component analysis

HIGHLIGHTS:

PCA efficiently identifies key variables, optimizing resource allocation and saving time and costs.

Young plants efficiently give information for the identification of promising variables for the as-sessment of passion fruit.

Reliable inferences require retain a small number of variables to explain the highest data variabil-ity.

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