scholarly journals Short Communication: Morphology and taxonomic relationships of shallot (Allium cepa L. group aggregatum) cultivars from Indonesia

2019 ◽  
Vol 20 (10) ◽  
Author(s):  
Nita Fitriana ◽  
Ratna Susandarini
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Allium Cepa ◽  
Short Communication ◽  
Agronomic Traits ◽  
Morphological Variability ◽  
Principal Component ◽  
Component Analysis ◽  
Taxonomic Relationships ◽  
Allium Cepa L

Abstract. Fitriana N, Susandarini R. 2019. Short Communication: Morphology and taxonomic relationships of shallot (Allium cepa L. group aggregatum) cultivars from Indonesia. Biodiversitas 20: 2809-2814. Shallot is an important vegetable crop commodity in Indonesia, and its ranks third in terms of cultivation area after chili (Capsicum annuum L.) and cabbage (Brassica oleracea L.). There are a number of superior cultivars of this species that have been studied in terms of their morpho-agronomic traits, but their morphological variability and its implication to taxonomic relationships have not been studied in depth. This study aimed at documenting morphological variability and taxonomic relationships of 12 superior shallot cultivars using numerical taxonomic approaches. Sixteen morphological characters from leaves and bulbs were used in cluster analysis and principal component analysis. The results revealed that twelve shallot cultivars from Indonesia showed significant variability in leaf and bulb morphology, particularly in quantitative characteristics as indicated from ANOVA. The grouping of cultivars and their taxonomic relationships generated from cluster analysis and principal component analysis revealed the role of bulb morphology as determining characteristics in the grouping of cultivars. This study suggested the importance of bulb morphology in the characterization of shallot cultivars.

scholarly journals Short Communication: Composition of terpenoid compounds in essential oils extracted from stems of eight Piper species and their role in taxonomic relationships

2020 ◽  
Vol 21 (8) ◽  
Author(s):  
Laurentius Hartanto Nugroho ◽  
ELIZABETH CINDY LEXINTA ◽  
YOSEP PRIYONO ◽  
RATNA SUSANDARINI
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Chemical Composition ◽  
Essential Oils ◽  
Short Communication ◽  
Principal Component ◽  
Component Analysis ◽  
Taxonomic Relationships ◽  
Species Cluster ◽  
Medicinal Properties

Abstract. Nugroho LH, Lexinta EC, Priyono Y, Susandarini R. 2020. Short Communication: Composition of terpenoid compounds in essential oils extracted from stems of eight Piper species and their role in taxonomic relationships. Biodiversitas 21: 3438-3443. Piper is widely used as source of essential oils from which many bioactive compounds with medicinal properties were used in traditional medicine. The objectives of this study were to explore the chemical composition of essential oils of eight Piper species and to identify its role in determining taxonomic relationships. Chemical composition of essential oils was analyzed using GC-MS analysis on petroleum ether extracts from stems of eight Piper species, while taxonomic relationship was determined using cluster analysis and principal component analysis. Results showed that 21 terpenoid compounds were identified as constituents of the essential oils, consisted of 4 monoterpenes, 14 sesquiterpenes, and 3 diterpenes. The composition of essential oils varied between species. Cluster analysis and principal component analysis showed that differences in the composition of essential oil compounds determined the grouping of species into two clusters. Five compounds were identified as having major role in the grouping of species. These compounds were α-selinene, α-caryophyllene, β-caryophyllene, farnesyl acetone, and α-amorphene. The results of this study offer opportunities for the use of Piper stems as source of essential oils. This study also confirms the interspecies variability in composition of essential oils, and at the same time supports the use of essential oils in chemotaxonomic studies.

scholarly journals Assessment of the genetic components and trait associations in diverse set of fieldpea (Pisum sativum L.) genotypes

2015 ◽  
Vol 43 (3) ◽  
pp. 323-330 ◽  
Author(s):  
AK Parihar ◽  
GP Dixit ◽  
V Pathak ◽  
D Singh
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Components ◽  
Agronomic Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Genetic Components ◽  
Wide Range ◽  
Cluster Distance ◽  
Biological Yield

One hundred and 40 genotypes of fieldpea were used to assess the genetic divergence for various agronomic traits. The study revealed that all the accessions were significantly different for the traits and a wide range of variability exists for most of the traits. Correlation studies exhibited that seed yield had positive significant correlation with most of the traits. Cluster analysis classified 140 genotypes into 12 distinct groups. A large number of genotypes (30) were placed in cluster IV followed by cluster III with 24 genotypes. The maximum inter-cluster distance was observed between clusters III and IV indicating the possibility of high heterotic effect if the individuals from these clusters are cross-bred. Principal component analysis yielded 12 Eigen vectors and PCA analysis revealed significant variations among traits with seven major principal components explaining about 90% of variations. The estimates of Eigen value associated with the principal components and their respective relative and accumulated variances explained 50.16% of total variation in the first two components. The characters with highest weight in component first were biological yield, pods/plant, yield/plant and branches/plant which explained 34.22% of the total variance. The results of principal component analysis were closely in line with those of the cluster analysis. The grouping of genotypes and hybridization among genetically diverse genotypes of different cluster would be helpful in broadening the genetic base of fieldpea and producing desirable recombinants for developing new fieldpea varieties. DOI: http://dx.doi.org/10.3329/bjb.v43i3.21605 Bangladesh J. Bot. 43(3): 323-330, 2014 (December)

scholarly journals Morphological variability of evergreen oaks (Quercus) in Turkey

2017 ◽  
Vol 24 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Yilmaz Aykut ◽  
Uslu Emel ◽  
Babaç M. Tekin
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Morphological Variability ◽  
Principal Component ◽  
Component Analysis ◽  
The South ◽  
Plant Taxon ◽  
Evergreen Oaks

The genus Quercus L. has a problematic taxonomy because of widespreadhybridization among them. Evergreen Quercus contain three species in section Ilex Loudon namely, Q. ilex L., Q. coccifera L. and Q. aucheri Jaub. et Spach in Turkey. Here, two species, Q. coccifera and Q. aucheri are usually confused with each other. However, Q. coccifera and Q. calliprinos are accepted as different species but this subject is still controversial. Morphometric leaf and fruit variations of Q. ilex, Q. coccifera and Q. aucheri in 26 populations were measured for 25 characters. Variations within and among populations of species were detected by cluster analysis and principal component analysis. This study shows that populations of Q. coccifera from the south region of Turkey form a second group within Q. coccifera. Secondly, Q. coccifera show more similarity to Q. aucheri than Q. ilex, and finally there are two groups within Q. coccifera, which may be evaluated as Q. coccifera and Q. calliprinos.Keywords: Ilex; Morphometric, UPGMA; Turkey.Bangladesh J. Plant Taxon. 24(1): 39–47, 2017 (June)

scholarly journals KARAKTERISASI EMPAT POPULASI IKAN GURAMI (Osphronemus goramy Lac.) DAN PERSILANGANNYA BERDASARKAN METODE TRUSS MORFOMETRIKS

2016 ◽  
Vol 11 (2) ◽  
pp. 125
Author(s):  
Suharyanto Suharyanto ◽  
Rita Febrianti ◽  
Sularto Sularto
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Morphological Variability ◽  
Similarity Index ◽  
Principal Component ◽  
Component Analysis ◽  
The Body ◽  
Female Population ◽  
Dorsal Fin ◽  
Pure Lines

Tahap awal yang dilakukan dalam rangka pembentukan populasi ikan gurami cepat tumbuh adalah koleksi dan karakterisasi populasi-populasi ikan gurami yang akan digunakan sebagai sumber genetik pembentukan varietas tersebut. Kegiatan ini dilakukan untuk mengevaluasi keragaman morfologi dan hubungan kekerabatan empat populasi ikan gurami, yaitu Jambi (J), Kalimantan Selatan (K), Majalengka (M), dan Tasikmalaya (T). Metode truss morfometrik digunakan untuk karakterisasi morfologi dilanjutkan dengan analisis komponen utama (principal component analysis) dan analisis pengelompokan (cluster analysis). Hasil karakterisasi menunjukkan bahwa diagram pencar populasi ikan gurami tanpa melihat jenis kelamin menunjukkan adanya pengelompokan populasi menjadi dua kelompok, yaitu kelompok pertama adalah persilangan JxM dan MxK, sedangkan kelompok kedua terdiri atas persilangan JxK, KxJ, TxJ, KxM, MxJ, KxT, galur murni KxK, JxJ, MxM, dan TxT. Hal tersebut terjadi pula pada populasi jantan. Populasi betina menunjukkan JxK dan MxK terpisah berdasarkan karakter A2 (dahi-pangkal sirip punggung) dan A3 (pangkal sirip punggung-pangkal sirip perut). Indeks kesamaan tertinggi dalam 12 populasi diperoleh pada populasi Jambi dan Majalengka berturut-turut sebesar 94,00% dan 92,00%; sedangkan indeks kesamaan terendah diperoleh pada populasi TJ sebesar 72,00%. Ikan gurami ukuran konsumsi terdapat empat kelompok besar berdasarkan bentuk badannya. Dua kelompok pada galur murni menunjukkan populasi galur murni Kalimantan, Majalengka, dan Tasikmalaya kekerabatannya dekat, akan tetapi dengan Jambi memiliki kekerabatan yang jauh. Dua kelompok lainnya pada populasi persilangan, yaitu: persilangan JxM dan MxK dan kelompok lainnya adalah persilangan KxJ, KxM, JxK, TxJ, MxJ, dan KxT. Populasi galur murni dan persilangan memiliki jarak genetik yang jauh, sehingga populasi galur murni dan persilangan itu berbeda.The first step in breeding program towards generating fast-growing strain of giant gourami is the collection and characterization of giant gourami populations have been used as a genetic source. Giant gourami had been collected from South Kalimantan, Jambi, Majalengka, and Tasikmalaya. The aim of this experiment was to determine the morphological diversity among these collected populations using truss morphometric method. Principal component analysis followed by cluster analysis were used to identify the pattern of morphological variability among populations and varieties. The results showed that dendrogram populations of giant gourami regardless of gender showed a grouping of some of the population into two groups: the first group was J×M and M×K crosses, while the second population consisted of: Jambi Kalimantan (J×K), Kalimantan Jambi (K×J), Tasikmalaya Jambi (T×J), Kalimantan Majalengka (K×M), Majalengka Jambi (M×J), Kalimantan Tasikmalaya (K×T), purebred Kalimantan (K×K), Jambi (J×J), Majalengka (M×M), and Tasikmalaya (T×T). This was true for the male population. Female population showed J×K and M×K apart, the difference lies in the character of the forehead-base of the dorsal fin (A2) and the base of the dorsal fin-fin base stomach (A3). The highest similarity index was found Jambi (94.00%) Majalengka (92.00%) populations, while the lowest similarity index was T×J (72.00%). At market size of the consumption of giant gourami there are four major groups, based on the shape of the body. Two groups on pure strains showed a population of pure lines Kalimantan, Majalengka, and Tasikmalaya close kinship, but Jambi had a distant kinship. Two other groups in the population crosses, namely: cross J×M and M×K and the other group was a cross K×J, M×K, J×K, T×J, M×J, and K×T. The population of pure lines and crosses had a genetic distance away, so that the population of pure lines and crosses were different.

Classification of Observations through Combination of the Dimension Reduction and the Cluster Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 30
Author(s):  
Hyeuk Kim
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Unsupervised Learning ◽  
Principal Component ◽  
Component Analysis ◽  
Baseball Players ◽  
Partitioning Around Medoids ◽  
Different Characteristics

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.

Genetic Diversity Assessment in Dolichos Bean (Lablab purpureus L.) Based on Principal Component Analysis and Single Linkage Cluster Analysis

2021 ◽  
Author(s):  
S.R. Singh ◽  
S. Rajan ◽  
Dinesh Kumar ◽  
V.K. Soni
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Single Linkage ◽  
Pod Yield ◽  
Diversity Assessment ◽  
Single Linkage Cluster ◽  
Linkage Cluster

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.

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