title: Comparison of efficiency of  distance measurement methodologies in mango (Mangifera indica) progenies  based on physicochemical descriptors.
abstract: We  investigated seven distance measures in a set of observations of  physicochemical variables of mango (Mangifera indica) submitted to  multivariate analyses (distance, projection and grouping). To estimate the  distance measurements, five mango progeny (total of 25 genotypes) were  analyzed, using six fruit physicochemical descriptors (fruit weight, equatorial  diameter, longitudinal diameter, total soluble solids in °Brix, total  titratable acidity, and pH). The distance measurements were compared by the  Spearman correlation test, projection in two-dimensional space and grouping  efficiency. The Spearman correlation coefficients between the seven distance  measurements were, except for the Mahalanobis' generalized distance (0.41 ≤ rs  ≤ 0.63), high and significant (rs ≥ 0.91; P < 0.001). Regardless of the  origin of the distance matrix, the unweighted pair group method with arithmetic  mean grouping method proved to be the most adequate. The various distance measurements  and grouping methods gave different values for distortion (-116.5 ≤ D ≤ 74.5),  cophenetic correlation (0.26 ≤ rc ≤ 0.76) and stress (-1.9 ≤ S ≤ 58.9). Choice  of distance measurement and analysis methods influence the.
authors.affiliation: Departamento de Ciências Biológicas, Universidade Estadual de Santa Cruz, Ilhéus, BA, Brasil.
authors.forename: E O S
authors.initials: EO
authors.lastname: Alves
authors.forename: C B M
authors.initials: CB
authors.lastname: Cerqueira-Silva
authors.forename: A M
authors.initials: AM
authors.lastname: Souza
authors.forename: C A F
authors.initials: CA
authors.lastname: Santos
authors.forename: F P
authors.initials: FP
authors.lastname: Lima Neto
authors.forename: R X
authors.initials: RX
authors.lastname: Corrêa
mesh_terms: D005638:Fruit / Q000235:genetics; D014644:Genetic Variation; D005838:Genotype; D031022:Mangifera / Q000145:classification / Q000235:genetics*; D008957:Models, Genetic; D015233:Models, Statistical; D010641:Phenotype; D018709:Statistics, Nonparametric