scholarly journals Multivariate Analysis of Phenotypic Diversity of Rice (Oryza sativa L.) Landraces from Lamjung and Tanahun Districts, Nepal

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Anup Dhakal ◽  
Amrit Pokhrel ◽  
Shishir Sharma ◽  
Ankur Poudel
Keyword(s):  
Principal Component Analysis ◽  
Phenotypic Diversity ◽  
Oryza Sativa L ◽  
Flag Leaf ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Value ◽  
Vital Role ◽  
Mean Values ◽  
Rice Landraces

The magnitude and nature of genetic divergence play a vital role in the selection of the desirable landraces for its utilization in the breeding program. A study was carried out with 30 rice landraces at the Institute of Agriculture and Animal Science, Lamjung Campus, during June–November 2018 to determine relation among individuals, estimate the relative contribution of various traits of rice using principal component analysis, and identify the potential parents for hybridization using Mahalanobis distance (D2). The principal component analysis revealed that five among the thirteen principal components were significant (eigenvalue >1) and contributed to 29.96%, 20.26%, 13.56%, 11.68%, and 9.22% of the total variance, respectively. PC1 included the traits that were related mostly to the yield, yield attributing, and grain characteristics. Landraces from Anadi group, Jetho Budo, Jarneli, and Rato Masino performed well in PC1 while landraces such as Mansara, Pakhe Sali, and Aanga performed well in PC2. The landraces were grouped into six clusters where 12 landraces were grouped into cluster I. Cluster analysis showed maximum and minimum intracluster distance in cluster VI (D2 = 35.77) and cluster I (D2 = 18.59), respectively. The maximum intercluster distance was obtained between clusters V and VI (D2 = 40.18) followed by clusters III and VI (D2 = 36.17) and clusters IV and VI (D2 = 35.74). Cluster III showed the highest mean value for grain width, flag leaf breadth, yield, and minimum mean value for plant height while mean values of total grain per panicle, filled grain percentage, and thousand-grain weight were maximum in cluster IV. Mean values of effective tiller and kernel width were found maximum in clusters V and VI, respectively. Landraces from clusters V and VI or clusters III and VI or clusters IV and VI can be used in the hybridization program to develop the superior hybrids by exploiting heterosis in segregating generation.

scholarly journals Improved Weighted Page Ranking Algorithm Based on Principal Component Analysis and Map Reduce Frame work for Web Access

2019 ◽  
Vol 8 (2) ◽  
pp. 32-39
Author(s):  
T. Mylsami ◽  
B. L. Shivakumar
Keyword(s):  
Data Mining ◽  
Principal Component Analysis ◽  
Web Mining ◽  
Principal Component ◽  
Component Analysis ◽  
Mean Value ◽  
Ranking Algorithm ◽  
Mean Values ◽  
Page Ranking ◽  
The Web

In general the World Wide Web become the most useful information resource used for information retrievals and knowledge discoveries. But the Information on Web to be expand in size and density. The retrieval of the required information on the web is efficiently and effectively to be challenge one. For the tremendous growth of the web has created challenges for the search engine technology. Web mining is an area in which applies data mining techniques to deal the requirements. The following are the popular Web Mining algorithms, such as PageRanking (PR), Weighted PageRanking (WPR) and Hyperlink-Induced Topic Search (HITS), are quite commonly used algorithm to sort out and rank the search results. In among the page ranking algorithm uses web structure mining and web content mining to estimate the relevancy of a web site and not to deal the scalability problem and also visits of inlinks and outlinks of the pages. In recent days to access fast and efficient page ranking algorithm for webpage retrieval remains as a challenging. This paper proposed a new improved WPR algorithm which uses a Principal Component Analysis technique called (PWPR) based on mean value of page ranks. The proposed PWPR algorithm takes into account the importance of both the number of visits of inlinks and outlinks of the pages and distributes rank scores based on the popularity of the pages. The weight values of the pages is computed from the inlinks and outlinks with their mean values. But in PWPR method new data and updates are constantly arriving, the results of data mining applications become stale and obsolete over time. To solve this problem is a MapReduce (MR) framework is promising approach to refreshing mining results for mining big data .The proposed MR algorithm reduces the time complexity of the PWPR algorithm by reducing the number of iterations to reach a convergence point.

scholarly journals Development of a Sensory Flavor Lexicon for Mushrooms and Subsequent Characterization of Fresh and Dried Mushrooms

Foods ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 980 ◽  
Author(s):  
SoonSil Chun ◽  
Edgar Chambers ◽  
Injun Han
Keyword(s):  
Principal Component Analysis ◽  
Food Industry ◽  
Principal Component ◽  
Component Analysis ◽  
Sensory Panel ◽  
Mean Values ◽  
Profile Method ◽  
Medicinal Properties ◽  
Selection Of

Mushrooms are a nutritious versatile ingredient in many food products. They are low in calories and have various potential medicinal properties as well. Surprisingly, little research on their descriptive sensory properties has been conducted. The objectives of this study were to a) establish a descriptive sensory flavor lexicon for the evaluation of fresh, dried, and powdered mushrooms and 2) use that lexicon to compare a selection of different mushrooms of various species and in fresh dried and powdered forms. A lexicon for describing mushroom was developed using a consensus profile method. A highly trained, descriptive sensory panel identified, defined, and referenced 27 flavor attributes for commercially available mushroom samples prepared as “meat” and broth. Attributes could be grouped in categories such as musty (dusty/papery, earthy/humus, earthy/damp, earthy/potato, fermented, leather (new), leather (old), mold/cheesy, moldy/damp, mushroomy), and other attributes such as fishy, shell fish, woody, nutty, brown, green, cardboard, burnt/ashy, potato, umami, protein (vegetable), yeasty, bitter, salty, sweet aromatics, sour, and astringent. Samples were then tested in three replications and mean values were compared statistically. In addition, principal component analysis was used to understand the characteristics of mushrooms evaluated. Dried mushrooms showed bitter, burnt, musty/dusty, astringent, old leather, and fresh mushroom characteristics and fresh mushroom showed umami, sweet, earthy/potato, earthy/damp, yeasty, and fermented. Mushrooms were grouped and differentiated in similar ways regardless of whether they were tested as broth or “meat”. Mushroom growers, product developers, chefs and other culinary professionals, sensory scientists, researchers, the food industry, and ultimately consumers will benefit from this lexicon describing a wide variety of mushroom flavor properties.

scholarly journals Assessment of Morphometric Diversity for Yield and Yield Attributing Traits in Rice (Oryza sativa L.) for Tolerance to Heat Stress

2020 ◽  
pp. 29-49
Author(s):  
S. Sandeep ◽  
M. Sujatha ◽  
L. V. Subbarao ◽  
C. N. Neeraja
Keyword(s):  
Principal Component Analysis ◽  
Oryza Sativa ◽  
Heat Stress ◽  
Oryza Sativa L ◽  
Pollen Viability ◽  
Principal Component ◽  
Component Analysis ◽  
Spikelet Fertility ◽  
Breeding Programmes ◽  
Heat Tolerant

The present investigation entitled “Assessment of morphometric diversity for yield and yield attributing traits in rice (Oryza sativa L.) for tolerance to heat stress” was carried out with objective of assessing genetic divergence in 200 germplasm of rice for eleven characters at ICRISAT, Patencheru, Hyderabad. The genotypes were grouped into fifteen clusters in Tocher’s method, cluster analysis and principal component analysis, out of the 11 characters studied, number of grains per panicle, plant height, pollen viability and spikelet fertility contributed 96.73 per cent of the total divergence and these traits were found to be important potent factors for genetic differentiation in genotypes. Principal component analysis identified five principal components, which contributed for 78.66 percent % of cumulative variance. The overall results of the study revealed that crossing using the genotypes under cluster V and XI and cluster XI and XIII could be exploited by hybridization programme to yield good recombinants because they had maximum inter cluster distance and possessing high genetic diversity for the characters viz. panicle length, number of grains per panicle and single plant yield. The genotypes of cluster I, II, IV, VI, VII, VIII, XI, XII and XIII showed high spikelet fertility percentage. Hence the genotypes of these clusters can be used in breeding programmes for development of heat tolerant varieties. Euclidean2 method indicated that genotypes of cluster III and IX exhibited high spikelet fertility percentage which can be utilized in development of heat tolerant cultivars. The results of principal component analysis revealed that genotypes of cluster I, cluster IV, cluster V, cluster VIII, cluster IX, cluster XI, cluster XII and cluster XV exhibited highest spikelet fertility percentage. Hence, the genotypes of the clusters can be used in breeding programmes for the development of heat tolerant varieties. 

CARDIAC ARRHYTHMIA DIAGNOSIS BY HRV SIGNAL PROCESSING USING PRINCIPAL COMPONENT ANALYSIS

2012 ◽  
Vol 12 (05) ◽  
pp. 1240032 ◽  
Author(s):  
S. VINITHA SREE ◽  
DHANJOO N. GHISTA ◽  
KWAN-HOONG NG
Keyword(s):  
Heart Rate ◽  
Principal Component Analysis ◽  
Nervous System ◽  
Standard Deviation ◽  
Probabilistic Neural Network ◽  
Principal Component ◽  
Component Analysis ◽  
Cardiac Cells ◽  
Ecg Signal ◽  
Mean Values

An electrocardiogram (ECG) signal represents the sum total of millions of cardiac cells' depolarization potentials. It helps to identify the cardiac health of the subject by inspecting its P-QRS-T wave. The heart rate variability (HRV) data, extracted from the ECG signal, reflects the balance between sympathetic and parasympathetic components of the autonomic nervous system. Hence, HRV signal contains information on the imbalance between these two nervous system components that results in cardiac arrhythmias. Thus in this paper, we have analyzed HRV signal abnormalities to determine and classify arrhythmias. The HRV signals are non-stationary and non-linear in nature. In this work, we have used continuous wavelet transform (CWT) coupled with principal component analysis (PCA) to extract the important features from the heart rate signals. These features are fed to the probabilistic neural network (PNN) classifier, for automated classification. Our proposed system demonstrates an average accuracy of 80% and sensitivity and specificity of 82% and 85.6%, respectively, for arrhythmia detection and classification. Our system can be operated on larger data sets. Our CWT–PCA analysis resulted in eigenvalues which constituted the HRV signal analysis parameters. We have shown and plotted the distribution of the parameters' mean values and the standard deviation for arrhythmia classification. We found some overlap in the distribution of these eigenvalue parameters for the different arrhythmia classes, which mitigates the effective use of these parameters to separate out the various arrhythmia classes. Therefore, we have formulated a HRV Integrated Index (HRVID) of these eigenvalues, and determined and plotted the mean values and standard deviation of HRVID for the various arrhythmia classifications. From this information, it can be seen that the HRVID is able to distinguish among the various arrhythmia classes. Hence, we have made a case for the employment of this HRVID as an index to effectively diagnose arrhythmia disorders.

scholarly journals Assessment of genetic variability for micronutrient content and agro-morphological traits in rice (Oryza sativa L.)

2020 ◽  
Vol 80 (02) ◽  
Author(s):  
P. Madhubabu ◽  
R. Surendra ◽  
K. Suman ◽  
M. Chiranjeevi ◽  
R. Abdul Fiyaz ◽  
...  
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Oryza Sativa L ◽  
Development Program ◽  
Principal Component ◽  
Component Analysis ◽  
High Yield ◽  
Polished Rice ◽  
Iron And Zinc

Assessment of rice genetic diversity is critical step for trait specific varietal development program. In the present study, a collection of 281 Indian germplasm accessions were evaluated for genetic diversity study using 30 agro-morphological characters and grain iron and zinc contents in brown and polished rice. To identify the pattern of relatedness and associations, cluster analysis and principal component analysis coupled with correlation were used. Cluster analysis grouped 281 accessions into six main groups. Cluster 4 is the largest and had accessions with higher yield, zinc and iron content. Six components of principal component analysis indicated 76.4% of the total variation. The Principal Component (PC)1 showed 19.05%, while, PC2, PC3, PC4, PC5 and PC6 exhibited 14.23%, 13.61%, 11.58%, 7.59%, and 6.71% variability, respectively. Among the germplasm, three accessions IC145407, IC145357 and IC248034 have shown significant iron and zinc content in polished rice along with desirable grain yield. The information presented here will be useful in the development of rice varieties with high yield and micronutrient content.

scholarly journals GENETIC DIVERSITY AND PARENTS SELECTION FROM EXOTIC RICE (Oryza sativa L.) GERMPLASM

2012 ◽  
Vol 25 (1) ◽  
pp. 11-16
Author(s):  
A. A. Mamun ◽  
N. A. Ivy ◽  
M. G. Rasul ◽  
M. M. Hossain
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Oryza Sativa ◽  
Oryza Sativa L ◽  
Principal Component ◽  
Component Analysis ◽  
Rice Genotypes ◽  
Total Divergence ◽  
Five Axes

Genetic divergence among fifty exotic rice genotypes along with two check varieties were estimated using D2 and principal component analysis. The study was undertaken to select suitable donor parents for use in improved breeding program of Bangabandhu Sheikh Mujibur Rahman Agricultural University in 2009. Principal component analysis (PCA) revealed that the first five axes accounted for 58.10% of the total variation. As per cluster analysis, the genotypes were grouped into seven clusters consisting 11, 16, 7, 11, 1, 2 and 4 genotypes which revealed that there exist considerable diversity among the genotypes. Considering the magnitude of genetic distance, contribution of different characters towards the total divergence and magnitude of cluster means for different characters, the genotypes RG-BU-08-057, 61, 65, 67, 69, 71, 85, 86, 88, 94, 96, 98 and 99 might be selected as a suitable parent for future hybridization program.DOI: http://dx.doi.org/10.3329/bjpbg.v25i1.17007

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