scholarly journals Morphoanatomical study of Matricaria L. (Asteraceae) in Turkey

2019 ◽  
Vol 43 (2) ◽  
pp. 151-159
Author(s):  
Huseyin Inceer ◽  
Murat Bal
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Analysis Of Variance ◽  
Univariate Analysis ◽  
Principal Component ◽  
New Information ◽  
Turkish Flora ◽  
Pericarp Thickness ◽  
Diagnostic Traits

In the Turkish flora, the genus Matricaria is present with four taxa, namely M. aurea, M. chamomilla var. chamomilla, M. chamomilla var. recutita and M. matricarioides. This study presents an evaluation of selected diagnostic characters and anatomical traits of the achene (cypsela) of Matricaria in Turkey using univariate analysis (one-way analysis of variance) and multivariate analysis (cluster analysis, principal component analysis) to obtain new information. Three groups are found within the genus Matricaria based on morphoanatomical characteristics. The colour of disc florets, that of ribs on the achenes, the presence or absence of a slime envelope and pericarp thickness are useful for delimitation of Matricaria taxa, and a key to taxa based on these characters together with other diagnostic traits is provided.

scholarly journals Use of Highly Discriminating Morphological Characters and Isozymes in the Study of Spanish Chestnut Cultivars

2006 ◽  
Vol 131 (6) ◽  
pp. 770-779 ◽  
Author(s):  
Santiago Pereira-Lorenzo ◽  
María Belén Díaz-Hernández ◽  
Ana María Ramos-Cabrer
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Castanea Sativa ◽  
Principal Component ◽  
Market Value ◽  
Morphological Characters ◽  
Minor Importance ◽  
Breeding Programs ◽  
And Cluster Analysis

Morphological characters (six traits) and isozymes (four systems, five loci) were used to discriminate between Spanish chestnut cultivars (Castanea sativa Mill.) from the Iberian Peninsula. A total of 701 accessions (representing 168 local cultivars) were analyzed from collections made between 1989 and 2003 in the main chestnut growing areas: 31 were from Andalucía (12 cultivars), 293 from Asturias (65 cultivars), 25 from Castilla-León (nine cultivars), four from Extremadura (two cultivars) and 348 from Galicia (80 cultivars). Data were synthesized using multivariate analysis, principal component analysis, and cluster analysis. A total of 152 Spanish cultivars were verified: 58 cultivars of major importance and 94 of minor importance, of which 18 had high intracultivar variation. Thirty-seven cultivars were clustered into 14 synonymous groups. Six of these were from Galicia, one from Castilla-León (El Bierzo), four from Asturias, one from Asturias and Castilla-León (El Bierzo), and two from Asturias, Castilla-León (El Bierzo), and Galicia. The chestnut cultivars from Galicia and Asturias were undifferentiated in genetic terms, indicating that they are not genetically isolated. Overall, chestnut cultivars from southern Spain showed the least variation. Many (58%) of Spanish cultivars produced more than 100 nuts/kg; removing this low market-value character will be a high priority. The data obtained will be of use in chestnut breeding programs in Spain and elsewhere.

scholarly journals Assessment of Physicochemical Properties of Tequila Brands: Authentication and Quality

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Alejandra Carreon-Alvarez ◽  
Amaury Suárez-Gómez ◽  
Florentina Zurita ◽  
Sergio Gómez-Salazar ◽  
J. Felix Armando Soltero ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Refractive Index ◽  
Physicochemical Properties ◽  
Analysis Of Variance ◽  
Principal Component ◽  
Component Analysis ◽  
Total Variability ◽  
And Cluster Analysis ◽  
Physicochemical Data

Several physicochemical properties were measured in commercial tequila brands: conductivity, density, pH, sound velocity, viscosity, and refractive index. Physicochemical data were analyzed by Principal Component Analysis (PCA), cluster analysis, and the one-way analysis of variance to identify the quality and authenticity of tequila brands. According to the Principal Component Analysis, the existence of 3 main components was identified, explaining the 87.76% of the total variability of physicochemical measurements. In general, all tequila brands appeared together in the plane of the first two principal components. In the cluster analysis, four groups showing similar characteristics were identified. In particular, one of the clusters contains some tequila brands that are not identified by the Regulatory Council of Tequila and do not meet the quality requirements established in the Mexican Official Standard 006. These tequila brands are characterized by having higher conductivity and density and lower viscosity and refractive index, determined by one-way analysis of variance. Therefore, these economical measurements, PCA, and cluster analysis can be used to determinate the authenticity of a tequila brand.

A multivariate analysis of alluvial terrace soils of the clarendon and cranebrook formations, Nepean River, NSW

Soil Research ◽  
1988 ◽  
Vol 26 (2) ◽  
pp. 243 ◽  
Author(s):  
G Atkinson
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Principal Component ◽  
Soil Samples ◽  
The Other ◽  
Soil Profiles ◽  
Alluvial Terrace ◽  
Southern Boundary ◽  
Alluvial Terraces

The techniques of cluster analysis and principal component analysis (PCA) were applied to soils data from two Pleistocene alluvial terraces on the Nepean River, N.S.W., the Clarendon and Cranebrook Formations, to address issues raised in the literature regarding their stratigraphic relationships. A total of 160 profiles were sampled at four fixed depths to 1 8 m. Profiles were located in four 1000 by 400 m sample areas, two on each terrace. Soil samples were analysed for colour, pH, and 2.8 M HCl extractable Fe2+, Mn2+, Na2+, K+, Ca2+ and Mg2+. Data were analysed by using whole profiles as the soil entities. One branch of the dendrogram resulting from the cluster analysis contained soil profiles exclusively from sample areas on the Cranebrook Formation, whilst the other branch contained profiles exclusively from sample areas on the Clarendon Formation. Soils typical of the Lowlands Formation, Londonderry Clay and minor subdivisions within the terraces could be distinguished on the dendrogram. Similar subdivisions could also be observed on a PCA scattergram. The Clarendon and Cranebrook Formations are complex units which contain minor terrace features. Each has a distinctly different suite of soils which is consistent with their continued designation as separate stratigraphic units. The Lowlands Formation can be separated from the Cranebrook Formation upstream of Castlereagh and the Clarendon Formation should have its southern boundary to the Londonderry Clay moved north towards Richmond and its stratigraphy redefined.

scholarly journals Urbanização extensiva e o processo de interiorização do estado de São Paulo: um enfoque contemporâneo

2013 ◽  
Vol 15 (2) ◽  
pp. 179
Author(s):  
Admir Antonio Betarelli Junior ◽  
Roberto Luís De Melo Monte-Mór ◽  
Rodrigo Ferreira Simões
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Urban Areas ◽  
Principal Component ◽  
Sao Paulo ◽  
Competitive Advantages ◽  
São Paulo ◽  
And Cluster Analysis ◽  
Contemporary Approach

O propósito deste trabalho é discutir a formação, produção e organização do espaço urbano no estado de São Paulo a partir do processo de interiorização da indústria paulista no final dos anos 1970. O lócus da análise é a indústria, uma vez que no enfoque contemporâneo o processo de industrialização sempre esteve articulado com a produção da espacialidade urbana. Conciliando o método diferencial-estrutural (shift-share), a Análise de Componentes Principais (ACP) e a análise de cluster, foi possível evidenciar que tal processo teve como resultado o fenômeno de urbanização extensiva. Os resultados “fotográficos” apontam que houve uma extensão virtual das condições gerais do tecido urbano-industrial de forma que centralidades polarizadoras e regiões circunvizinhas apresentam vantagens locacionais e competitivas, formando, assim, aglomerações urbanas no território paulista, principalmente, nas regiões beneficiadas pelo processo de interiorização da indústria. Palavras-chave: urbanização extensiva; análise multivariada; análise de cluster; método diferencial-estrutural; indústria; São Paulo. Abstract: The main aim of this paper is to discuss the formation, organization and production of urban areas in State of São Paulo (Brazil) in the variant of the process of industry’s internalization in the late ‘70s. As industrialization has always been linked to the production of urban spatiality in contemporary approach, the locus of analysis is the industry. Combining the method shift-share (Esteban-Marquillas), Principal Component Analysis (PCA) and cluster analysis, we noted evidence that this process has resulted in the phenomenon of extensive urbanization. The main findings of these applications (“photographic”) indicated that there was a virtual extension in general conditions of the urban-industrial fabric so that polarizing centralities and surrounding regions present locational and competitive advantages, forming, therefore, urban agglomerations in the territory of São Paulo, mainly in the regions benefiting with the process of industry’s internalization. Keywords: extensive urbanization; internalization of the industry; shift-share; multivariate analysis; São Paulo (Brazil).

scholarly journals Multivariate Analysis and its Application for Screening Mungbean [Vigna radiata (L.) Wilczek] Landraces

2022 ◽  
Author(s):  
Berk Benlioglu ◽  
Ugur Ozkan
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Vigna Radiata ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
Breeding Programs ◽  
High Level

Background: Mungbean [Vigna radiata (L.) Wilczek] is known as one of the important crop of the Vigna group. In order to determine morphological traits of mungbean, multivariate analysis will provide important advantages in the selection phase of future breeding programs. Multivariate statistical analysis was used to determine and classify these traits. Multivariate analysis, that includes principal component analysis (PCA) and cluster analysis (CA), is considered the best tool for selecting promising genotypes in the future breeding programs. Methods: Eighteen landraces and two species were used to classify morphological traits in this study. Nine different morphological traits were observed during the research period. These are; days to 50% flowering (DFT), plant height (PH), branches per plant (BPP), clusters per plant (CPP), number of pods per cluster (PPC), seed yield per plot (SYPP), biomass yield per plot (BYPP), harvest index (HI), 1000 seed weight (SW). Result: Principal component analysis (PCA) revealed a high level of variation among the genotypes. Therefore, high variability was observed in DFT (36-59 day), PH (39-76 cm), BPP (3-7), CPP (4-21), SYPP (231-824 g), BYPP (3300-10300 g), HI (6.77-11.25%) and 1000 SW (19.95-50.50 g). According to cluster analysis, landraces with the least genetic diversity distance between them in terms of morphological traits examined were determined as 2 and 3.

scholarly journals Multivariate Analysis for Yield and Its Component Traits in Experimental Maize Hybrids

2017 ◽  
Vol 9 (3) ◽  
pp. 219
Author(s):  
Ramesh Kumar ◽  
G. K. Chikkappa ◽  
S. B. Singh ◽  
Ganapati Mukri ◽  
J. Kaul ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Crop Yields ◽  
Principal Component ◽  
Component Analysis ◽  
Plant Breeding Program ◽  
Component Traits ◽  
Group B ◽  
Ending Process

Crop yields of major cereal including maize are not increasing at the targeted growth rates to feed the rising demands stemming from increase in the human population. To increase maize grain yield, there should be continuous improvement of cultures which are actively utilized by the plant breeders. Variability in germplasm is always the key to improvement and to assess the extent of variation is never ending process in a plant breeding program. Out of several methods available for assessing the variability, multivariate analysis is one of the most important and widely used methods. In the present study, 27 hybrids (including three checks) were evaluated for yield and yield contributing traits at three different locations during rabi 2013-14. Analysis of variance revealed significant variations among hybrids for all the traits. Based on Principal Component Analysis, 76.81% of the total variance in the data was accounted for by first four principal components (PC). Cluster analysis based on PC grouped the 27 hybrids into two major groups named as A and B. The group A further contained three sub-groups named as A1, A2, and A3 with two hybrids falling in each group. Similarly group B contained four subgroups classified as B1 to B4 with 2, 7, 5 and 7 hybrids falling in each subgroup respectively. The hybrids falling in two major groups contained more diversity than those falling in subgroups within a group. Selection of hybrids from the different groups would facilitate exploiting significant heterosis. Therefore, multivariate analysis including Principal component analysis followed by cluster analysis could be a reliable approach for assessing the extent of variability on in the germplasm and making its use in a suitable direction.

scholarly journals Evaluation of vigor and tolerance of sweet corn seeds under hypoxia

2019 ◽  
Vol 41 (2) ◽  
pp. 180-186
Author(s):  
Jéssica de Lucena Marinho ◽  
Denis Santiago da Costa ◽  
Deived Uilian de Carvalho ◽  
Maria Aparecida da Cruz ◽  
Claudemir Zucareli
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Multivariate Analysis ◽  
Sweet Corn ◽  
Principal Component ◽  
Multivariate Methods ◽  
Distilled Water ◽  
Germination Test ◽  
Corn Seed ◽  
Test Discrimination

Abstract: Evaluation of the physiological potential of seeds by fast and efficient methods is an important step in the process of production and commercialization of sweet corn seeds. The aim of this study was to discriminate sweet corn seed lots by applying multivariate methods regarding the usual vigor tests to verify the sensitivity of these seeds to flood conditions and to verify if the submersion test has potential for classifying lots of this species regarding vigor. Five seed lots of sweet corn were tested for moisture content and physiological potential. Cluster analysis and principal component analysis were performed on the data to discriminate the seed lots regarding initial vigor. Subsequently, two of these lots were selected for the water submersion test, performed with four replicates of 150 seeds, which were submerged in 100 mL of distilled water for 0, 24, 48, 72, and 96 hours at 25 ºC, and they then underwent the germination test. Discrimination of sweet corn seed lots is possible through multivariate analysis. Sweet corn seeds are sensitive to submersion in water; however, the test did not allow seed lots to be differentiated for vigor.

Comparison of Visual Responses of Female Observers to Clothing over Time

1981 ◽  
Vol 53 (1) ◽  
pp. 299-309 ◽  
Author(s):  
Marilyn Revell De Long ◽  
Carol Salusso-Deonier ◽  
Kinley Larntz
Keyword(s):  
Principal Component Analysis ◽  
Multivariate Analysis ◽  
Analysis Of Variance ◽  
Principal Component ◽  
Semantic Differential ◽  
Consistent Difference ◽  
Visual Responses ◽  
Multivariate Analysis Of Variance ◽  
Group Response ◽  
Over Time

Two groups of females ( n = 35, n = 36) completed a semantic differential instrument in response to 10 slides of fashionable dress. The response sessions were separated by 12 wk. to determine the effect of change in fashion on group response to identical stimuli. Responses were analyzed for similarity in use of word pairs via principal component analysis. The first component explained the largest proportion of variance and was interpreted as evaluative while subsequent components were informational. Profiles of word-pairs identified through a multivariate analysis of variance indicated a consistent difference between responses of groups which was keyed to word-pairs reflecting recent changes in fashion.

scholarly journals Flower morphometry of members of the genus Dactylorhiza Necker ex Nevski (Orchidaceae) from the Altai Mountains of Kazakhstan

2021 ◽  
Vol 22 (8) ◽  
Author(s):  
Aidar Sumbembayev ◽  
SAULE ABUGALIEVA ◽  
ALEVTINA DANILOVA ◽  
EKATERINA MATVEYEVA ◽  
DARIUSZ SZLACHETKO
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Analysis Of Variance ◽  
Principal Component ◽  
Population Level ◽  
Component Analysis ◽  
Population Diversity ◽  
Altai Mountains ◽  
Distinctive Features ◽  
High Degree

Abstract. Sumbembayev AA, Abugalieva SI, Danilova AN, Matveyeva EV, Szlachetko DL. 2021. Flower morphometry of members of the genus Dactylorhiza Necker ex Nevski (Orchidaceae) from the Altai mountains of Kazakhstan. Biodiversitas 22: 3545-3555. Several species of Dactylorhiza (Orchidaceae) from the Altai mountains of Kazakhstan have been investigated regarding their morphological flower variability. Significant metric characters were identified allowing to differentiate between the four species: D. incarnata, D. fuchsii, D. maculata and D. salina. The morphometric structure of flowers was analyzed by comparing 17 metric parameters in representatives of 11 populations. We identified the most variable and stable traits as well as distinctive features for each species. A high degree of flower morphometric diversity was revealed from principal component analysis for species and populations. Cluster analysis demonstrated the structure of the population diversity. Structural schemes have been compiled from data of the photographic processing of flower morphometry, the analysis of variance ANOVA, and the degree of variation at the population level. Useful characters are provided for further taxonomic work on members of the genus Dactylorhiza in Kazakhstan.

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.

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