scholarly journals Polymorphism analysis in some Algerian Opuntia species using morphological and phenological UPOV descriptors

2017 ◽  
Vol 95 (3) ◽  
pp. 391 ◽  
Author(s):  
Boubakr Hadjkouider ◽  
Ammar Boutekrabt ◽  
Bahia Lallouche ◽  
Salim Lamine ◽  
Néjia Zoghlami
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Phenotypic Diversity ◽  
Principal Component ◽  
Morphological Diversity ◽  
Hierarchical Cluster ◽  
Relative Magnitude ◽  
Polymorphism Analysis ◽  
Opuntia Ficus Indica ◽  
New Varieties

<p><strong>Background: </strong>In the present study, we have investigated the morphological variation in a set of five <em>Opuntia</em> species from the Algerian steppes using 49 UPOV descriptors.</p><p><strong>Questions: </strong>which of the 49 descriptors that can be used as powerful estimators of the phenotypic diversity within <em>Opuntia</em> species? How is the morphological diversity patterned in Algerian <em>Opuntia</em>?</p><p><strong>Species study/ Mathematical model: </strong><em>Opuntia ficus-indica, Opuntia amycleae, Opuntia streptacantha, Opuntia engelmannii, Opuntia robusta</em><strong>.</strong> Principal Components Analysis (PCA) and Hierarchical Cluster Analysis were used.</p><p><strong>Study site: </strong>Four counties were studied located in the Algerian steppes. The present research was carried out during 2014.</p><p><strong>Methods:</strong> 49 descriptors adopted by the International Union for the Protection of New Varieties of Plants (UPOV) were employed in the present research, where cladode, flower and fruit traits were used to determine the overall degree of polymorphism among 5 <em>Opuntia</em> species.</p><p><strong>Results:</strong> Principal Component Analysis and Hierarchical Cluster Analysis indicated a consistent differentiation between all studied species. The relative magnitude of the first two PCA eigenvectors showed that 8 descriptors out of 49 were identified as the most important descriptors for the classification of the species. The dendrogram performed on the calculated Euclidean distances between all species pairs allowed the identification of 3 groups, unlike the PCA that identified 4 groups. The species <em>Opuntia ficus-indica </em>and <em>Opuntia amycleae</em> were identified as very close morphologically.</p><p><strong>Conclusions: </strong>The present outcome represents a paramount step towards the fast selection of interesting species and for their best management and conservation.</p>

scholarly journals Multivariate characteristics of selected grass varieties for seed production

2019 ◽  
Vol 55 (No. 2) ◽  
pp. 83-86
Author(s):  
Marzena Iwańska ◽  
Danuta Martyniak ◽  
Marcin Martyniak ◽  
Dariusz Gozdowski
Keyword(s):  
Principal Component Analysis ◽  
Growth Rate ◽  
Cluster Analysis ◽  
Seed Production ◽  
Plant Resistance ◽  
Hierarchical Cluster Analysis ◽  
Multivariate Analyses ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Festuca Rubra

Data were obtained in a field experiment carried out at Plant Breeding and Acclimatization Institute Radzikow (central Poland) in 2009–2011. The aim of this study was a multivariate evaluation of 13 advanced lines and cultivars of Festuca rubra, taking into account traits important in seed production. Eleven traits of the grasses and plant resistance to diseases were evaluated. On the basis of multivariate analyses, i.e. hierarchical cluster analysis and principal component analysis, groups of varieties were separated and described, relationships between the traits were evaluated as well. The traits with the biggest influence on multivariate diversity of examined varieties were correlated with the first principal component i.e. height of plants, seeds yield, growth rate of plants, leaf width and time to beginning of earing.  

scholarly journals Groundwater quality assessment using principal component analysis and hierarchical cluster analysis in Alaçam, Turkey

2021 ◽  
Author(s):  
Mehmet Taşan ◽  
Yusuf Demir ◽  
Sevda Taşan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Groundwater Quality ◽  
Seawater Intrusion ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Quality Parameters ◽  
Multivariate Statistical

Abstract This study assessed groundwater quality in Alaçam, where irrigations are performed solely with groundwaters and samples were taken from 35 groundwater wells at pre and post irrigation seasons in 2014. Samples were analyzed for 18 water quality parameters. SAR, RSC and %Na values were calculated to examine the suitability of groundwater for irrigation. Hierarchical cluster analysis and principal component analysis were used to assess the groundwater quality parameters. The average EC value of groundwater in the pre-irrigation period was 1.21 dS/m and 1.30 dS/m after irrigation in the study area. It was determined that there were problems in two wells pre-irrigation and one well post-irrigation in terms of RSC, while there was no problem in the wells in terms of SAR. Piper diagram and cluster analysis showed that most groundwaters had CaHCO3 type water characteristics and only 3% was NaCl- as the predominant type. Seawater intrusion was identified as the primary factor influencing groundwater quality. Multivariate statistical analyses to evaluate polluting sources revealed that groundwater quality is affected by seawater intrusion, ion exchange, mineral dissolution and anthropogenic factors. The use of multivariate statistical methods and geographic information systems to manage water resources will be beneficial for both planners and decision-makers.

scholarly journals Discrimination of Thymus, Origanum, Satureja and Thymbra species from the family Labiatae by untargeted metabolomic analysis

2020 ◽  
Vol 38 (No. 3) ◽  
pp. 151-157
Author(s):  
Nurcan Ayşar Güzelsoy ◽  
Filiz Çavuş ◽  
Oya Kaçar
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Single Species ◽  
Metabolomic Analysis ◽  
Methanolic Extracts ◽  
The Family ◽  
Extraction Algorithm

The term “thyme” does not refer to herbs that belong to a single species. The genera Thymus, Origanum, Satureja and Thymbra of the family Labiatae are traditionally named as thyme and locally known as ‘kekik’. Unlike Turkey, these species are globally called differently. Spices made of Origanum, Thymus and Satureja are called oregano, thyme and savory, respectively. It is often difficult to differentiate them because of their similar smell and appearance. Most commercial products traded as a mixture of those genera and the mixing together of different species may lead to economically motivated adulteration and a product of reduced value. The species were analysed by LC-ESI-QTOF-MS and a comprehensive statistical workflow was designed. The data of methanolic extracts were assessed and an extraction algorithm was employed for the processing of raw data. Five species were discriminated using principal component analysis (PCA) and hierarchical cluster analysis (HCA). The results of PCA and HCA were consistent with each other. Twenty-one metabolites were determined for the discrimination.

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