scholarly journals Genetic Characterization of Indian mustard (Brassica juncea L.) Germplasm for Quantitative Traits through Principal Component Analysis

2020 ◽  
Vol 9 (5) ◽  
pp. 1192-1196
Author(s):  
Neelam Shekhawat ◽  
Kartar Singh
Keyword(s):  
Principal Component Analysis ◽  
Brassica Juncea ◽  
Quantitative Traits ◽  
Genetic Characterization ◽  
Indian Mustard ◽  
Principal Component ◽  
Component Analysis

scholarly journals Evaluation and diversity analysis in Indian mustard [Brassica juncea (L.) Czern & Coss.] germplasm accessions on the basis of principal component analysis

2017 ◽  
Vol 9 (4) ◽  
pp. 2485-2490
Author(s):  
Ram Avtar ◽  
Manmohan Manmohan ◽  
Minakshi Jattan ◽  
Babita Rani ◽  
Nisha Kumari ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Total Variation ◽  
Brassica Juncea ◽  
Seed Yield ◽  
Oil Content ◽  
Indian Mustard ◽  
Principal Component ◽  
Component Analysis ◽  
Principal Factor ◽  
Germplasm Accessions

Principal component analysis was carried out with 20 morphological traits (including quantitative as well as qualitative) among 96 germplasm lines of Indian mustard [Brassica juncea (L.) Czern & Coss.]. Principal factor analysis led to the identification of eight principal components (PCs) which explained about 70.41% variability. The first principal component (PC1) explained 16.21% of the total variation. The remaining PC’s explained progressively lesser and lesser of the total variation. Varimax Rotation enabled loading of similar type of variables on a common principal factor (PF) permitting to designate them as yield factor, maturity factor and oil factor etc. Based on PF scores and cluster mean values the germplasm accessions viz., RC2, RC32 and RC51 (cluster I), RC95 and RC96 (cluster X) were found superior for seed yield/plant and yield related factors like primary and secondary branches/plant; while the accessions RC34, RC185 and RC195 (cluster III) and RC53 (cluster VIII) were found superior for oil content. These accessions may further be utilized in breeding programmes for evolving mustard varieties having high seed yield and oil content. Hierarchical cluster analysis resulted into ten clusters containing two to 26 accessions. The results of cluster and principal factor analyses were in confirmation of each other.

Characterization of fretting wear experiments on spline couplings by principal component analysis

2016 ◽  
Vol 231 (7) ◽  
pp. 860-868 ◽  
Author(s):  
Waqar Qureshi ◽  
Francesca Cura ◽  
Andrea Mura
Keyword(s):  
Experimental Data ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Fretting Wear ◽  
Aircraft Industry ◽  
Surface Movement ◽  
Noisy Measurements ◽  
Quasi Static Process

Fretting wear is a quasi-static process in which repeated relative surface movement of components results in wear and fatigue. Fretting wear is quite significant in the case of spline couplings which are frequently used in the aircraft industry to transfer torque and power. Fretting wear depends on materials, pressure distribution, torque, rotational speeds, lubrication, surface finish, misalignment between spline shafts, etc. The presence of so many factors makes it difficult to conduct experiments for better models of fretting wear and it is the case whenever a mathematical model is sought from experimental data which is prone to noisy measurements, outliers and redundant variables. This work develops a principal component analysis based method, using a criterion which is insensitive to outliers, to realize a better design and interpret experiments on fretting wear. The proposed method can be extended to other cases too.

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