scholarly journals Multivariate analysis of quantitative traits can effectively classify rapeseed germplasm

Genetika ◽  
2014 ◽  
Vol 46 (2) ◽  
pp. 545-559 ◽  
Author(s):  
Mirjana Jankulovska ◽  
Sonja Ivanovska ◽  
Ana Marjanovic-Jeromela ◽  
Snjezana Bolaric ◽  
Ljupcho Jankuloski ◽  
...  
Keyword(s):  
Regression Analysis ◽  
Vegetative Growth ◽  
Quantitative Traits ◽  
Principal Component ◽  
Breeding Program ◽  
Oil Yield ◽  
Growth Duration ◽  
Data Set ◽  
Combined Application ◽  
Selection Of

In this study, the use of different multivariate approaches to classify rapeseed genotypes based on quantitative traits has been presented. Tree regression analysis, PCA analysis and two-way cluster analysis were applied in order todescribe and understand the extent of genetic variability in spring rapeseed genotype by trait data. The traits which highly influenced seed and oil yield in rapeseed were successfully identified by the tree regression analysis. Principal predictor for both response variables was number of pods per plant (NP). NP and 1000 seed weight could help in the selection of high yielding genotypes. High values for both traits and oil content could lead to high oil yielding genotypes. These traits may serve as indirect selection criteria and can lead to improvement of seed and oil yield in rapeseed. Quantitative traits that explained most of the variability in the studied germplasm were classified using principal component analysis. In this data set, five PCs were identified, out of which the first three PCs explained 63% of the total variance. It helped in facilitating the choice of variables based on which the genotypes? clustering could be performed. The two-way cluster analysissimultaneously clustered genotypes and quantitative traits. The final number of clusters was determined using bootstrapping technique. This approach provided clear overview on the variability of the analyzed genotypes. The genotypes that have similar performance regarding the traits included in this study can be easily detected on the heatmap. Genotypes grouped in the clusters 1 and 8 had high values for seed and oil yield, and relatively short vegetative growth duration period and those in cluster 9, combined moderate to low values for vegetative growth duration and moderate to high seed and oil yield. These genotypes should be further exploited and implemented in the rapeseed breeding program. The combined application of these multivariate methods can assist in deciding how, and based on which traits to select the genotypes, especially in early generations, at the beginning of a breeding program.

Genetic variability, principal component and cluster analyses in black gram under Foot-hills conditions of Manipur

2018 ◽  
Author(s):  
M. Samuel Jeberson ◽  
K. S. Shashidhar ◽  
Amit Kumar Singh
Keyword(s):  
Genetic Variability ◽  
Path Analysis ◽  
Quantitative Traits ◽  
Principal Component ◽  
Yield Attributes ◽  
Eigen Values ◽  
Correlation And Path Analysis ◽  
And Cluster Analysis ◽  
100 Seed Weight ◽  
Selection Of

Analysis of genetic variability, heritability, correlation, path analysis, principal component and cluster analysis was carried for 25 blackgram genotypes grown in the foothills of Manipur. The results showed that phenotypic coefficients of variability recorded were higher than the genotypic coefficients of variability, irrespective of traits, demonstrating the effect of environment thereon. The present study revealed that the heritability (bs) estimates were maximum (>50%) for the traits such as days taken to attain the 50% flowering, number of clusters/plant, number of pods/plant and 100 seed weight. The correlation and path analysis proved the selection of the yield attributes in blackgram based on the characters, viz., number of pods/plant and number of cluster/plants. The first three principal components, having the Eigen values more than 1, contributed 84.52% towards variability among the 25 genotypes screened for quantitative traits. Based on the average linkage, 25 genotypes were grouped into five (5) clusters.

scholarly journals Geographical patterns of diversity for qualitative and quantitative traits in the pigeonpea germplasm collection

2005 ◽  
Vol 3 (3) ◽  
pp. 331-352 ◽  
Author(s):  
Hari D. Upadhyaya ◽  
R.P.S. Pundir ◽  
C.L.L. Gowda ◽  
K.N. Reddy ◽  
Sube Singh
Keyword(s):  
Quantitative Traits ◽  
Diversity Index ◽  
Germplasm Collection ◽  
Flower Colour ◽  
Principal Component ◽  
The Philippines ◽  
Growth Duration ◽  
Flowering Pattern ◽  
Stem Colour ◽  
Germplasm Accessions

We analysed the patterns of variation for 14 qualitative and 12 quantitative traits in 11,402 pigeonpea germplasm accessions from 54 countries, which were grouped into 11 regions. Semi-spreading growth habit, green stem colour, indeterminate flowering pattern and yellow flower colour were predominant among qualitative traits. Primary seed colour had maximum variability and orange colour followed by cream were the two most frequent seed colours in the collection. Variances for all the traits were heterogeneous among regions. The germplasm accessions from Oceania were conspicuous by short growth duration, short height, fewer branches, pods with fewer seeds, smaller seed size and lower seed yields. The accessions from Africa were of longer duration, taller, with multi-seeded pods and larger seeds. The germplasm diversity indicated by Shannon–Weaver diversity index (H′) pooled over all traits, was highest for Africa (0.464±0.039) and lowest for Oceania (0.337±0.037). The cluster analysis based on three principal component scores using 12 quantitative traits revealed formation of three clusters: cluster 1 includes accessions from Oceania; cluster 2 from India and adjacent countries; and cluster 3 from Indonesia, Thailand, the Philippines, Europe, Africa, America and the Caribbean countries. Pigeonpea-rich countries such as Myanmar, Uganda, and others like Bahamas, Burundi, Comoros, Haiti and Panama are not adequately represented in the collection, and need priority attention for germplasm exploration.

scholarly journals Principal Component Analysis for Assessment of Genetic Diversity in Rainfed Shallow Lowland Rice (Oryza sativa L)

2020 ◽  
pp. 256-261
Author(s):  
Prince Raj ◽  
Anand Kumar ◽  
. Satyendra ◽  
S. P. Singh ◽  
Mankesh Kumar ◽  
...  
Keyword(s):  
Total Variation ◽  
Seed Weight ◽  
Harvest Index ◽  
Quantitative Traits ◽  
Principal Component ◽  
Panicle Length ◽  
Total Carbohydrate ◽  
Total Variability ◽  
Biological Yield ◽  
Selection Of

The genetic diversity was estimated using seventy two genotypes of rice in a randomized block design with three replications at the rice research farm of Bihar Agricultural University, Sabour (Bhagalpur) during Kharif, 2019-20 to determine the contribution of fifteen quantitative traits to the total variability in rice using Principal component analysis. In the present investigation PCA was performed for fifteen quantitative traits of rice. All the 3PCs exhibited more than 1.0 Eigen value and showed about 95.00% variability. Therefore, these PCs were given due important for the further explanation. The PC1 showed 77.28 per cent variation of total variation followed by second to third components which accounted 15.65 and 2.05 per cent of total variation presented among the genotypes, respectively. PC1 contributed 77.28% of the total variation and correlated with total carbohydrate, generation of H2O2, days to 50% flowering, biological yield, number of fertile grains per panicle, panicle length and flag leaf area while PC2 explained an additional 15.65% of the total variation and dominated by total carbohydrate, days to 50% flowering, harvest index, biological yield, total number of spikelet’s and plant height. PC III accounted 2.05 per cent of the total variability and correlated with the traits like days to 50% flowering, biological yield, total number of spikelet’s, 1000-seed weight, plant height, harvest index, generation of H2O2 and panicle length had maximum positive contribution Since, a total of 95.00% of the total variation was contributed by PC1 and PC2, therefore, these two principal components can be allowed for simultaneous selection of yield contributing traits in desi chickpea. Genotype usually found in more PC, were CR3933-13-2-1-4-1-2-1, TTB1011-14-171-2-2-1-2-1, TTB1032-45-937-2-3-3-1-1, (Santepheap3/IR49830-7/RajendraMahsuri)-1-3-1, (BR11/IR8041OB)-2-1-1, (RajendraMhasuri/CN1039)-4-2-1, TTB1011-14-243-1-2-2-2-1, TTB1032-45-937-2-3-3-1-1, CR4138-3-1-1, CR4139-9-2-1, CR4139-9-2- and CR4128-9-1-1. Genotypes fall in a common principal component were observed to be the most important factor for seed yield. These genotypes may further be utilized in breeding programmes for improving seed yield and these genotypes can be considered an ideotype breeding material for selection of traits viz. more total number of seed per plant and 100-seed weight further utilization in precise breeding programme.

scholarly journals Principal component analysis in interpretation of the results of HPLC-ELC, HPLC-DAD and essential elemental contents obtained for medicinal plant extracts

2013 ◽  
Vol 11 (4) ◽  
pp. 519-526
Author(s):  
Pawel Konieczynski
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Spectroscopic Techniques ◽  
Plant Materials ◽  
Polygonum Aviculare ◽  
Combined Application ◽  
Medicinal Plant Extracts ◽  
Betula Verrucosa ◽  
Selection Of

AbstractPrincipal component analysis (PCA) was applied to compare its usefulness with cluster analysis (CA), and factorial k-means analysis (fkm), for evaluating the results obtained using HPLC-DAD, HPLC-ELC and spectroscopic techniques (AAS and UV/VIS spectrometry for determining content of N, P, Fe and Cu) in aqueous extracts of seven medicinal plants. These represented the following plant species that are rich in flavonoids: Betula verrucosa Ehrh., Equisetum arvense L., Polygonum aviculare L., Viola tricolor L., Crataegus oxyacantha L., Sambucus nigra L. and Helichrysum arenarium (L.) Moench. The databases analyzed comprised four sets: 1) results obtained by the use of HPLC-DAD detection, 2) results obtained by the use of electrochemical detection (HPLC-ELC), 3) results for determining elements — total and water-extractable species, and 4) all data combined. Application of statistical methods allowed the samples to be classified into four groups: 1) Crataegus, Sambucus, 2) Equisetum, Polygonum and Viola, 3) Betula, and 4) Helichrysum, which were differentiated by characteristic patterns. PCA supported by CA, was the most suitable method, because it simultaneously allowed for reduction of multidimensionality of the databases, grouped the samples into four clusters, and made possible selection of the factors responsible for differentiation of the plant materials studied.

A MANUFACTURING FAILURE ROOT CAUSE ANALYSIS IN IMBALANCE DATA SET USING PCA WEIGHTED ASSOCIATION RULE MINING

2015 ◽  
Vol 77 (18) ◽  
Author(s):  
Phaik-Ling Ong ◽  
Yun-Huoy Choo ◽  
Azah Kamilah Muda
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Manufacturing Systems ◽  
Principal Component ◽  
Root Cause Analysis ◽  
Rule Mining ◽  
Data Set ◽  
Cause Analysis ◽  
Root Cause ◽  
Selection Of

Root cause analysis is key issue for manufacturing processes. It has been a very challenging problem due to the increasing level of complexity and huge number of operational aspects in manufacturing systems. Association rule mining (ARM) which aids in root cause analysis was introduced to extract interesting correlations, frequent patterns, associations or casual structures among items in the transactional database. Although ARM was proven outstanding in many application domains, not many researches were focusing on solving rare items problem in imbalance dataset. The existence of imbalanced dataset in manufacturing environment make the classical ARM fails to extract interesting pattern in an efficient way. Weighted association rule mining (WARM) overcomes the rare items problem by assigning weights to items. The goal of using weighted support is to make use of the weight in the mining process and priorities the selection of the selection of targeted itemsets according to their significance, rather than frequency alone. However, the development of a suitable weight assignment scheme has been an important issue. In this research, we proposed principal component analysis (PCA) to automate the weight in WARM. The result shows that PCA-WARM is capable in capturing pattern from the data of industrial process. These patterns are proven able to explain industrial failure.

Customer-Related Social Stressors

2015 ◽  
Vol 14 (4) ◽  
pp. 165-181 ◽  
Author(s):  
Sarah Dudenhöffer ◽  
Christian Dormann
Keyword(s):  
Service Providers ◽  
Principal Component ◽  
Well Being ◽  
Emotional Dissonance ◽  
Social Stressors ◽  
Data Set ◽  
Confirmatory Factor Analyses ◽  
Confirmatory Factor ◽  
Service Jobs ◽  
Single Data

Abstract. The purpose of this study was to replicate the dimensions of the customer-related social stressors (CSS) concept across service jobs, to investigate their consequences for service providers’ well-being, and to examine emotional dissonance as mediator. Data of 20 studies comprising of different service jobs (N = 4,199) were integrated into a single data set and meta-analyzed. Confirmatory factor analyses and explorative principal component analysis confirmed four CSS scales: disproportionate expectations, verbal aggression, ambiguous expectations, disliked customers. These CSS scales were associated with burnout and job satisfaction. Most of the effects were partially mediated by emotional dissonance. Further analyses revealed that differences among jobs exist with regard to the factor solution. However, associations between CSS and outcomes are mainly invariant across service jobs.

Spectroscopy-Based Mapping with Scanning Microwave Impedance Microscopy

2018 ◽  
Author(s):  
Peter De Wolf ◽  
Zhuangqun Huang ◽  
Bede Pittenger
Keyword(s):  
Single Point ◽  
Electrical Characterization ◽  
Principal Component ◽  
High Sensitivity ◽  
Data Cube ◽  
Nanometer Scale ◽  
Learning Approaches ◽  
Data Set ◽  
3D Data ◽  
Higher Dimensional

Abstract Methods are available to measure conductivity, charge, surface potential, carrier density, piezo-electric and other electrical properties with nanometer scale resolution. One of these methods, scanning microwave impedance microscopy (sMIM), has gained interest due to its capability to measure the full impedance (capacitance and resistive part) with high sensitivity and high spatial resolution. This paper introduces a novel data-cube approach that combines sMIM imaging and sMIM point spectroscopy, producing an integrated and complete 3D data set. This approach replaces the subjective approach of guessing locations of interest (for single point spectroscopy) with a big data approach resulting in higher dimensional data that can be sliced along any axis or plane and is conducive to principal component analysis or other machine learning approaches to data reduction. The data-cube approach is also applicable to other AFM-based electrical characterization modes.

Analisa Pemilihan Moda Transportasi Umum Rute Tanjung Karang – Bandara Radin Inten II Dengan Stated Preference dan Uji Crame’s V

2018 ◽  
Vol 2 (2) ◽  
pp. 137
Author(s):  
Muhammad Abi Berkah Nadi
Keyword(s):  
Regression Analysis ◽  
Utility Function ◽  
Stated Preference ◽  
Technical Analysis ◽  
Chi Square ◽  
Time Headway ◽  
Independent Variable ◽  
The Relationship ◽  
Selection Of

Radin Inten II Airport is a national flight in Lampung Province. In this study using the technical analysis stated preference which is the approach by conveying the choice statement in the form of hypotheses to be assessed by the respondent. By using these techniques the researcher can fully control the hypothesized factors. To determine utility function for model forecasting in fulfilling request of traveler is used regression analysis with SPSS program. The analysis results obtained that the passengers of the dominant airport in the selection of modes of cost attributes than on other attributes. From the result of regression analysis, the influence of independent variable to the highest dependent variable is when the five attributes are used together with the R square value of 8.8%. The relationship between cost, time, headway, time acces and service with the selection of modes, the provision that states whether or not there is a decision. The significance of α = 0.05 with chi-square. And the result of Crame's V test average of 0.298 is around the middle, then the relationship is moderate enough.

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