Literature DB >> 34996311

Multivariate Analysis of Butterfly Pea (Clitoria ternatea L.) Genotypes With Potentially Healthy Nutraceuticals and Uses.

John Bradley Morris1.   

Abstract

Butterfly pea (Clitoria ternatea L.) is a legume used as tea, forage, ornamental, salad, and medicinal plant. The flowers range from white to dark purple with little known about the variation for seed and flower color in the United States Department of Agriculture, Agricultural Research Service, Plant Genetic Resources Conservation Unit germplasm collection. Therefore, 26 butterfly pea accessions were analyzed using a principal component analysis (PCA) and average linkage cluster analysis (ALCA). These butterfly pea genotypes ranged from 56% to 99% for viabilities, 2.57 to 5.88 g for 100 seed weight, 34.07 to 226.26 g for total seed weight, and 1,326 to 3,874 for total seed numbers. PCA accounted for 40%, 57%, 70%, 79%, and 86% of the variation using principal components (PCs) 1 through 5, respectively. PC1 was most correlated with 100 and total seed weight, while PC2 correlated with blue, white, and purple flowers. PC3 correlated mostly with germination, purple flowers, and total seed weight. PCs 4 and 5 primarily correlated with blue and purple flowers, respectively. Several significant correlations were also observed. ALCA grouped the 26 butterfly pea genotypes into four distinct seed number-producing clusters. Clusters 1 to 4 represent the lowest to highest seed numbers produced by the butterfly pea genotypes. Several potential health benefits from butterfly pea flowers, leaves, seeds, and roots for human use were identified from the literature.

Entities:  

Keywords:  butterfly pea; cluster analysis; health traits; principal component

Year:  2022        PMID: 34996311     DOI: 10.1080/19390211.2021.2022821

Source DB:  PubMed          Journal:  J Diet Suppl        ISSN: 1939-0211


  1 in total

1.  Study of nonlinear optical responses of phytochemicals of Clitoria ternatea by quantum mechanical approach and investigation of their anti-Alzheimer activity with in silico approach.

Authors:  Shradha Lakhera; Kamal Devlal; Meenakshi Rana; Ismail Celik
Journal:  Struct Chem       Date:  2022-06-16       Impact factor: 1.795

  1 in total

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