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 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.

EVALUATION AND MODELLING OF GROUND WATER QUALITY DATA OF ALLAHABAD CITY BY ENVIRONMETRIC METHODS

2016 ◽  
Vol 2 (4) ◽  
pp. 211
Author(s):  
Girdhari Lal Chaurasia ◽  
Mahesh Kumar Gupta ◽  
Praveen Kumar Tandon
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sampling Location ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Positive Loading

Water is an essential resource for all the organisms, plants and animals including the human beings. It is the backbone for agricultural and industrial sectors and all the small business units. Increase in human population and economic activities have tremendously increased the demand for large-scale suppliers of fresh water for various competing end users.The quality evaluation of water is represented in terms of physical, chemical and Biological parameters. A particular problem in the case of water quality monitoring is the complexity associated with analyzing the large number of measured variables. The data sets contain rich information about the behavior of the water resources. Multivariate statistical approaches allow deriving hidden information from the data sets about the possible influences of the environment on water quality. Classification, modeling and interpretation of monitored data are the most important steps in the assessment of water quality. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system. In the present study water samples were analyzed for various physicochemical analyses by different methods following the standards of APHA, BIS and WHO and were subjected to further statistical analysis viz. the cluster analysis to understand the similarity and differences among the various sampling stations.  Three clusters were found. Cluster 1 was marked with 3 sampling locations 1, 3 & 5; Cluster-2 was marked with sampling location-2 and cluster-3 was marked with sampling location-4. Principal component analysis/factor analysis is a pattern reorganization technique which is used to assess the correlation between the observations in terms of different factors which are not observable. Observations correlated either positively or negatively, are likely to be affected by the same factors while the observations which are not correlated are influenced by different factors. In our study three factors explained 99.827% of variances. F1 marked  51.619% of total variances, high positive strong loading with TSS, TS, Temp, TDS, phosphate and moderate with electrical conductivity with loading values of 0.986, 0.970, 0.792, 0.744, 0.695,  0.701, respectively. Factor 2 marked 27.236% of the total variance with moderate positive loading with total alkalinity & temp. with loading values 0.723 & 0.606 respectively. It also explained the moderate negative loading with conductivity, TDS, and chloride with loading values -0.698, -0.690, -0.582. Factor F 3 marked 20.972 % of the variances with positive loading with PH, chloride, and phosphate with strong loading of pH 0.872 and moderate positive loading with chloride and phosphate with loading values 0.721, and 0.569 respectively. 

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 Analysis of the physical-chemical and sensorial properties of Maria type cookies

2013 ◽  
Vol 6 (2) ◽  
pp. 269-280 ◽  
Author(s):  
Daniela Pereira ◽  
Paula M. R. Correia ◽  
Raquel P. F. Guiné
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Sensory Evaluation ◽  
Pearson Correlation ◽  
Principal Component ◽  
Component Analysis ◽  
Volume Density ◽  
Physical Parameters ◽  
Multivariate Statistical ◽  
Main Component

Abstract Given the importance of the cookies of type Maria worldwide, and considering the absence of any scientific study setting out their main features, it becomes important to identify the differentiating characteristics of several commercialized brands, in particular related to the chemical, physical and sensory characteristics. In this way, the aim of this work was to study and compare eight different brands of cookies of type Maria. The elemental chemical analysis (moisture, ash, protein, fat, fibre and carbohydrates contents), determination of physical parameters (volume, density, texture and colour) and sensory evaluation of studied cookies were performed. Multivariate statistical methods (Pearson correlation, principal component analysis and cluster analysis) were applied to estimating relationships in analysed data. The results for the elemental analysis showed that the samples were very similar in terms of some components, like for example ashes, while quite different in terms of other components, such as moisture and fat contents. With respect to texture and colour the samples showed, in general, some important differences. In terms of sensory evaluation, the sample C was the one that in most sensory tests gathered the preference of the panellists. The cluster analysis showed that the sample A was much different from the other samples. The results of principal component analysis showed that the main component explains 32.6 % of the total variance, and is strongly related to variables associated to colour.

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 Multivariate statistical characterization of groundwater quality in Fesdis, East of Algeria

2018 ◽  
Vol 37 (1) ◽  
pp. 65-74 ◽  
Author(s):  
Safia Khelif ◽  
Abderrahmane Boudoukha
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Anthropogenic Activities ◽  
Principal Component ◽  
Hierarchical Cluster ◽  
Component Analysis ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Statistical Characterization ◽  
And Cluster Analysis

AbstractThis study is a contribution to the knowledge of hydrochemical properties of the groundwater in Fesdis Plain, Algeria, using multivariate statistical techniques including principal component analysis (PCA) and cluster analysis. 28 samples were taken during February and July 2015 (14 samples for each month). The principal component analysis (PCA) applied to the data sets has resulted in four significant factors which explain 75.19%, of the total variance. PCA method has enabled to highlight two big phenomena in acquisition of the mineralization of waters. The main phenomenon of production of ions in water is the contact water-rock. The second phenomenon reflects the signatures of the anthropogenic activities. The hierarchical cluster analysis (CA) in R mode grouped the 10 variables into four clusters and in Q mode, 14 sampling points are grouped into three clusters of similar water quality characteristics.

scholarly journals Chemical and Microbiological Characterization for PDO Labelling of Typical East Piedmont (Italy) Salami

2015 ◽  
Vol 2015 ◽  
pp. 1-22 ◽  
Author(s):  
V. Gianotti ◽  
S. Panseri ◽  
E. Robotti ◽  
M. Benzi ◽  
E. Mazzucco ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Biogenic Amines ◽  
Principal Component ◽  
Component Analysis ◽  
Statistical Tools ◽  
Multivariate Statistical ◽  
North West ◽  
And Cluster Analysis ◽  
Aroma Components

This study is focused on the characterisation of typical salami produced in Alessandria province (North West of Italy). Seventeen small or medium salami producers from this area were involved in the study and provided the samples investigated. The aim is double and consists in obtaining a screening of the characteristics of different products and following their evolution along ripening. The study involved five types of typical salami that were characterised for aroma components and nutritional features. This approach could provide a basis for a possible PDO or PGI label request. Principal Component Analysis and cluster analysis were used as multivariate statistical tools for data treatment. The overall results obtained point out that the products investigated do not deviate from analogous European products and show the possibility of characterising by specific parameters three main groups of samples:Salamini di Mandrogne,Muletta, andNobile Giarolo; moreover some considerations can also be drawn with respect to the nutritional characterization considering the biogenic amines profile.

Genetic Diversity and Association Studies in Greengram [Vigna Radiata (L.) Wilczek]

2019 ◽  
Author(s):  
S Mohan ◽  
A Sheeba ◽  
T Kalaimagal
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Path Analysis ◽  
Seed Yield ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Positive Direct ◽  
Number Of Seeds ◽  
Number Of Branches

The present study was conducted to evaluate 44 greengram genotypes using correlation, path analysis, principal component analysis and cluster analysis based on ten morphological traits. Basic descriptive statistics showed considerable variance for all the traits. Association analysis indicated that, number of pods per plant, number of pod clusters per plant, number of seeds per pod and number of branches per plant showed significant positive association with seed yield per plant. Path analysis specified that the highest positive direct effect on single plant yield was exerted by days to 50 % flowering, number of pods per plant and number of seeds per pod. Principal component analysis (PCA) revealed 79.12 per cent of the variability by the first five components. PC1 was associated mainly with seed yield per plant, number of pod clusters per plant, number of pods per plant and number of branches per plant. The Wards method of hierarchical cluster analysis grouped the accessions into six major clusters. The clustering of greengram genotypes based on different morphological traits would be useful to identify the promising genotypes for effective utilization in future breeding programmes..

scholarly journals Clustering and Principal Component Analysis of Nerica Mutant Rice Lines Growing Under Rainfed Condition

2019 ◽  
Vol 7 (3) ◽  
pp. 327-334
Author(s):  
Md. Nuruzzaman ◽  
Md. Shohel Rana ◽  
Aleya Ferdausi ◽  
Md. Monjurul Huda ◽  
Lutful Hassan ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Morphological Traits ◽  
Principal Component ◽  
Component Analysis ◽  
Rice Varieties ◽  
Cluster Distance ◽  
Rice Improvement ◽  
Clear Idea ◽  
Scree Test

A field experiment was conducted at subtropical region in Bangladesh to assess the contribution of morphological traits to variability in some NERICA mutant rice lines. The experiment was conducted following RCBD with three replications. Thirty-one NERICA rice genotypes (twenty-eight mutant lines along with three parents) of advanced generations were used. Data were collected on twelve morphological traits. The results of the principal component analysis showed that the first four components account for 80% of total variation giving a clear idea of the structure underlying the variables analyzed. This result was also supported by scree test. Cluster analysis using Ward's method classified the thirty-one genotypes into five distinct groups. The maximum inter-cluster distance was observed between clusters indicating the possibility of high heterosis if individuals from these clusters are cross-bred. The results of PCA were closely in line with those of the cluster analysis. These results can now be used by breeders to develop drought tolerant high yielding rice varieties and new breeding protocols for rice improvement. Int. J. Appl. Sci. Biotechnol. Vol 7(3): 327-334  

scholarly journals Economic Causes of Extremism Intellectual (Thoughts) Views of Graduate Students Using Multivariate Statistical Techniques

2016 ◽  
Vol 9 (7) ◽  
pp. 160
Author(s):  
Hasan Abdullah Al-Dajah
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Graduate Students ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
The Impact

The present study investigated the impact of the economic reasons on the intellectual (thoughts) extremism, and the statement of the most important indicators in the economic factor that lead to extremism from the views of graduate students. The study problem based on the following question: What are economic factors leading to the extremism of the intellectual(Thoughts)? Correlation coefficient, Principal component analysis (PCA), varimax (F) rotated factor analysis, and dendrogram cluster analysis (DCA) were assessed for the economic impacts that leads to extremism(Thoughts). Multivariate statistical analysis of the dataset and correlation analysis suggested that the strong positive correlations are commonly associated in the poverty and lack of interest in remote areas for major cities Center. Multivariate statistical analysis such as principal component analysis, varimax rotated factor analysis, and dendrogram cluster analysis allowed the identification of three main factors controlling that lead to extremism from the views of graduate students. The extracted factors are as follows: low living expenses, poverty and substantial deprivation, and unequal opportunities and unemployment associations related to prevalence of corruption phase.

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