scholarly journals 33 Aplicação da análise exploratória de dados na discriminação geográfica do quiabo do Rio Grande do Norte e Pernambuco

2018 ◽  
Vol 34 (3) ◽  
pp. 33
Author(s):  
Francisco Dos Santos Panero ◽  
Maria de Fátima Pereira Vieira ◽  
Ângela Maria Paiva Cruz ◽  
Maria de Fátima Vitória De Moura ◽  
Henrique Eduardo Bezerra Da Silva

Samples of okra from Caruaru and Vitória of Santo Antão, in the State of Pernambuco, and Ceará-Mirim, Macaíba and Extremoz in the State of Rio Grande do Norte have been analysed. Two different methods were applied in the data treatment allowing to geographically discriminate samples from different origins: Principal Component Analysis - PCA and Hierarquical Cluster Analysis - HCA.

Author(s):  
Hyeuk Kim

Unsupervised learning in machine learning divides data into several groups. The observations in the same group have similar characteristics and the observations in the different groups have the different characteristics. In the paper, we classify data by partitioning around medoids which have some advantages over the k-means clustering. We apply it to baseball players in Korea Baseball League. We also apply the principal component analysis to data and draw the graph using two components for axis. We interpret the meaning of the clustering graphically through the procedure. The combination of the partitioning around medoids and the principal component analysis can be used to any other data and the approach makes us to figure out the characteristics easily.


Author(s):  
S.R. Singh ◽  
S. Rajan ◽  
Dinesh Kumar ◽  
V.K. Soni

Background: Dolichos bean occupies a unique position among the legume vegetables of Indian origin for its high nutritive value and wider climatic adaptability. Despite its wide genetic diversity, no much effort has been undertaken towards genetic improvement of this vegetable crop. Knowledge on genetic variability is an essential pre-requisite as hybrid between two diverse parental lines generates broad spectrum of variability in segregating population. The current study aims to assess the genetic diversity in dolichos genotypes to make an effective selection for yield improvement.Methods: Twenty genotypes collected from different regions were evaluated during year 2016-17 and 2017-18. Data on twelve quantitative traits was analysed using principal component analysis and single linkage cluster analysis for estimation of genetic diversity.Result: Principal component analysis revealed that first five principal components possessed Eigen value greater than 1, cumulatively contributed greater than 82.53% of total variability. The characters positively contributing towards PC-I to PC-V may be considered for dolichos improvement programme as they are major traits involved in genetic variation of pod yield. All genotypes were grouped into three clusters showing non parallelism between geographic and genetic diversity. Cluster-I was best for earliness and number of cluster/plant. Cluster-II for vine length, per cent fruit set, pod length, pod width, pod weight and number of seed /pod, cluster III for number of pods/cluster and pod yield /plant. Selection of parent genotypes from divergent cluster and component having more than one positive trait of interest for hybridization is likely to give better progenies for development of high yielding varieties in Dolichos bean.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Tong Chen ◽  
Xingpu Qi ◽  
Zaiyong Si ◽  
Qianwei Cheng ◽  
Hui Chen

Abstract In this work, a method was established for discriminating geographical origins of wheat flour based on energy dispersive X-ray fluorescence spectrometry (ED-XRF) and chemometrics. 68 wheat flour samples from three different origins were collected and analyzed using ED-XRF technology. Firstly, the principal component analysis method was applied to analyze the feasibility of discrimination and reduce data dimensionality. Then, Competitive Adaptive Reweighted Sampling (CARS) was used to further extract feature variables, and 12 energy variables (corresponding to mineral elements) were identified and selected to characterize the geographical attributes of wheat flour samples. Finally, a non-linear model was constructed using principal component analysis and quadratic discriminant analysis (QDA). The CARS-PCA-QDA model showed that the accuracy of five-fold cross-validation was 84.25%. The results showed that the established method was able to select important energy channel variables effectively and wheat flour could be classified based on geographical origins with chemometrics, which could provide a theoretical basis for unveiling the relationship between mineral element composition and wheat origin.


2012 ◽  
Vol 36 (4) ◽  
pp. 1073-1082 ◽  
Author(s):  
Mariana dos Reis Barrios ◽  
José Marques Junior ◽  
Alan Rodrigo Panosso ◽  
Diego Silva Siqueira ◽  
Newton La Scala Junior

The agricultural potential is generally assessed and managed based on a one-dimensional vision of the soil profile, however, the increased appreciation of sustainable production has stimulated studies on faster and more accurate evaluation techniques and methods of the agricultural potential on detailed scales. The objective of this study was to investigate the possibility of using soil magnetic susceptibility for the identification of landscape segments on a detailed scale in the region of Jaboticabal, São Paulo State. The studied area has two slope curvatures: linear and concave, subdivided into three landscape segments: upper slope (US, concave), middle slope (MS, linear) and lower slope (LS, linear). In each of these segments, 20 points were randomly sampled from a database with 207 samples forming a regular grid installed in each landscape segment. The soil physical and chemical properties, CO2 emissions (FCO2) and magnetic susceptibility (MS) of the samples were evaluated represented by: magnetic susceptibility of air-dried fine earth (MS ADFE), magnetic susceptibility of the total sand fraction (MS TS) and magnetic susceptibility of the clay fraction (MS Cl) in the 0.00 - 0.15 m layer. The principal component analysis showed that MS is an important property that can be used to identify landscape segments, because the correlation of this property within the first principal component was high. The hierarchical cluster analysis method identified two groups based on the variables selected by principal component analysis; of the six selected variables, three were related to magnetic susceptibility. The landscape segments were differentiated similarly by the principal component analysis and by the cluster analysis using only the properties with higher discriminatory power. The cluster analysis of MS ADFE, MS TS and MS Cl allowed the formation of three groups that agree with the segment division established in the field. The grouping by cluster analysis indicated MS as a tool that could facilitate the identification of landscape segments and enable the mapping of more homogeneous areas at similar locations.


1990 ◽  
Vol 1 (3) ◽  
pp. 131-144
Author(s):  
María Coscarón

Cluster analysis by four methods and a principal component analysis were performed using data on 24 morphological characters of 27 species of the genus Rasahus (Peiratinae). The results obtained by the different techniques show general agreement. They confirm the present number of taxa and reveal the existence within the genus of three groups of species: scutellaris , hamatus and vittatus. The scutellaris group is constituted by R. aeneus (Walker), R. maculipennis (Lepelletier and Serville), R. bifurcatas Champion, R. castaneus Coscarón, R. guttatipennis (Stål), R. flavovittarus Stål, R. costarricensis Coscarón, R. scutellaris (Fabricius), R. atratus Coscarón, R. peruensis Coscarón, R. paraguayensis Coscarón, R. surinamensis Coscarón, R. albomaculatus Mayr, R. brasiliensis Coscarón and R. sulcicollis (Serville).The hamatus group contains R. rufiventris (Walker), R. hamatus (Fabricius), R. amapaensis Coscarón, R. arcitenens Stål, R. limai Pinto, R. angulatus coscarón, R. thoracicus Stål, R. biguttatus (Say), R. arcuiger (Stål), R. argentinensis Coscarón and R. grandis Fallou. The vittatus group contains R. vittatus Coscarón. The characters used to separate the groups of species are: shape of the pygophore, shape of the parameres, basal plate complexity, shape of the postocular region and hemelytra pattern. Illustrations of the structures of major diagnostic importance are included.


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