scholarly journals Agronomic and molecular evaluation of rice lines in a breeding program

2020 ◽  
Author(s):  
Luis Marqués ◽  
Susana Barceló ◽  
José María Osca
Keyword(s):  
Principal Component Analysis ◽  
Field Tests ◽  
Principal Component ◽  
Component Analysis ◽  
Indirect Selection ◽  
Rice Varieties ◽  
Complex Process ◽  
Total Variability ◽  
Yield Values ◽  
Molecular Marker Analysis

Abstract Obtaining new and improved varieties of rice requires long and complex plant breeding programs. The early detection of desirable characteristics is a complex process, especially when seeking to improve yield, as the interaction between the environment and plants may hinder selection in early generations considerably. Techniques that facilitate the selection of plants with desirable characteristics in early generations are highly valuable to plant breeders. An indirect selection method in early generations of rice was examined by principal component analysis of performance supported by field tests with a honeycomb design. This study used double haploid lines of rice obtained by crossing two rice varieties, namely ‘Benisants’ and ‘Gigante Vercelli’. This method was compared to indirect selection using genomic tools such as high-throughput molecular marker analysis. The main factors that can be used in indirect selection have been selected by principal component analysis. The model resulting from the phenological evaluation and principal component analysis with six selected variables explained 98.73% of the total variability of yield. The variable that contributes the most to the model is the Harvest Index. The best selected lines provided 32% and 43% higher yield values than the parentals and match the results from indirect selection with molecular markers.

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.

scholarly journals Principal Component Analysis on Soil Fertility Parameters of Vegetable Growing Panchayats/ Locations in Ernakulam District of Kerala

2020 ◽  
pp. 58-65
Author(s):  
A. Muhsina ◽  
Brigit Joseph ◽  
Vijayaraghava Kumar
Keyword(s):  
Principal Component Analysis ◽  
Soil Fertility ◽  
Soil Ph ◽  
Principal Component ◽  
Component Analysis ◽  
Organic Carbon Content ◽  
Score Plot ◽  
Five Factors ◽  
Total Variability ◽  
High Level

The present paper used Principal Component Analysis (PCA) on 13 soil fertility parameters including soil pH and electrical conductivity of 17 vegetable growing panchyat/locations in Ernakulam district of Kerala based on 583 soil samples. Soil pH of panchayats varied from 4.2- 5.8 with a coefficient of variation 3.16-12.23 per cent and it was inferred that most of the panchayats in the district had very strongly acidic (pH: 4.2-5) and strongly acidic soils (pH: 5-5.5). High level of organic carbon content was noticed in most of the panchayats except in four panchayats. The results of PCA revealed that five PC’s together explained a total variability of 80 per cent and the remaining PCs accounted for 20 per cent of the variability in the data which has been discarded from further analysis. First principal component accounted for 25 per cent variance followed by PC 2(21%), PC 3(14%), PC 4(10%) and PC 5(10%). Factor analysis generated five factors and they explained 85 per cent of variability. Score plot drawn as part of PCA showed that Chengamanadu, Manjapra and Thirumaradi panchayats had high content of soil available S and B. EC was also found to be higher in these panchayats. Amount of OC, Fe and Mn were more in Kalady, Keerampara and Mudakkuzha of Ernakulam district whereas Thuravur, Piravom and Pothanikkad had highly acidic and Mg rich soils. Amount of Zn was more in Vengoor panchayat. Available K, Ca, P and Cu were found to be higher in Kakkad, Nedumbassery, Vengola and Kadungalloor. Based on the fertility status of each panchayats, they could be classified into different groups.

scholarly journals Genetic Diversity Studies in Drought Tolerant Rice (Oryza sativa L.) Genotypes

2021 ◽  
Author(s):  
A. Sheeba ◽  
S. Mohan
Keyword(s):  
Genetic Diversity ◽  
Principal Component Analysis ◽  
Association Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Large Set ◽  
Panicle Length ◽  
Rice Varieties ◽  
Drought Tolerant ◽  
Rice Genotypes

Background: Assessing the genetic diversity and relationship among breeding materials isan invaluable aid for any crop improvement programme. Principal component analysis (PCA) is a multivariate statistical technique attempt to simplify and analyze the inter relationship among a large set of variables in term of a relatively a small set of variables or components without losing any essential information of original data set. Methods: The present investigation was carried out to study the genetic diversity and relationship among the sixty five rice genotypes including popular rice varieties of Tamil Nadu, drought tolerant rice varieties, aerobic rice genotypes and land races. These genotypes were raised at Rice Research Station, Tiruvallur, during kharif, 2015 in randomized block design with three replications under aerobic condition. Data on eight yield and yield attributing traits were recorded and subjected to principal component analysis and association analysis. Result: In principal component analysis, PC1accounted for 22.91% and PC2 accounted for 19.53% of the total variation. The traits panicle length, no. of grains per panicle, plant height, days to 50% flowering, no of productive tillers per plant from the first two principal components accounted for major contribution to the total variability. Cluster analysis grouped the genotypes into six discrete clusters. The association analysis revealed that the traits viz., no. of productive tillers/plant, panicle length and hundred seed weight had positive association with higher direct effect on plot yield which could be used as selection criteria for developing high yielding rice varieties. The results of the present study have revealed the high level of genetic variation existing in the genotypes studied and explains the traits contributing for this diversity.

scholarly journals Comprehensive Evaluation of 17 Qualities of 84 Types of Rice Based on Principal Component Analysis

Foods ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2883
Author(s):  
Shijie Shi ◽  
Enting Wang ◽  
Chengxuan Li ◽  
Hui Zhou ◽  
Mingli Cai ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Protein Content ◽  
Comprehensive Evaluation ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Rice Varieties ◽  
Rice Quality ◽  
Coefficients Of Variation ◽  
Cluster Analysis Method

Rice quality is a complex indicator, and people are paying more and more attention to the quality of rice. Therefore, we used seven rice varieties for twelve nitrogen fertilizer treatments and obtained eighty-four rice types with seventeen qualities. It was found that 17 quality traits had different coefficients of variation. Among them, the coefficient of variation of chalkiness and protein content was the largest, 44.60% and 17.89% respectively. The cluster analysis method was used to define four categories of different rice qualities. The principal component analysis method was used to comprehensively evaluate 17 qualities of 84 rice. It was found that rice quality was better under low nitrogen conditions, Huanghuazhan and Lvyinzhan were easier to obtain better comprehensive rice quality during cultivation. Future rice research should focus on reducing protein content and increasing peak viscosity.

scholarly journals Investigation of Cotton Germplasm for Genetic Divergence Regarding Yield Related Trait Using Principal Component Analysis

2020 ◽  
Author(s):  
Qudrat Ullah ◽  
Muhammad Zulfiqar Ahmad ◽  
Kalim Ullah ◽  
Obaidullah Sayal ◽  
Arshad Jamil ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Genetic Divergence ◽  
Principal Component ◽  
Component Analysis ◽  
Main Cluster ◽  
Total Variability ◽  
Yield Related Trait ◽  
And Cluster Analysis ◽  
Related Trait

Abstract Background: Cotton is a vital fiber and cash crop in Pakistan. Genetic diversity of a germplasm play an important role for cotton breeding. One hundred and two germplasm of upland cotton were investigated for genetic divergence regarding yield related attributes using principal component analysis. The research was carried out in RCB design with 2 replications. Experiment data was recorded on various qualitative and quantitative parameters and were subjected to principal components analysis (PCA) and cluster analysis.Results: PCA result showed that only four components were considered on account of their eigenvalue greater than 1 which contributed 65% to the total variability. Score plot showed that the suncrop-6, tipu-9, TJ-max, Deebal, CRIS-543, TH-20, Tahafuz-7, Eagle, BS-80, IUB-69, BH-221, NIAB-1048, and NIAB BT-2 showed the vertex of polygon and resulted as most divergent germplasm. Similarly cluster analysis also categorized the yield related traits into 5 main cluster. Cluster-1 contain 20 germplasm, cluster-II contain 16, and cluster-III, cluster-IV, and cluster-V comprise 13, 16, and 37 germplasm, respectively.Conclusion: Based on results, it was recommended that these genetically diverse germplasm might be used as parents that could be utilized in upcoming breeding programs.

scholarly journals Principal component analysis of JNPT lines of rice for the important traits responsible for yield and quality

2021 ◽  
Vol 81 (01) ◽  
pp. 127-131
Author(s):  
Shivangi Rahangdale ◽  
Yogendra Singh ◽  
P. K. Upadhyay ◽  
G. K. Koutu
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Spikelet Fertility ◽  
Quality Traits ◽  
Plant Type ◽  
Yield And Quality ◽  
Eigen Value ◽  
Total Variability ◽  
Panicle Weight

In present study, 67 JNPT (Jawahar New Plant Type) lines were evaluated for 28 morphological and quality traits planted in RCBD with three replications. Principal Component Analysis (PCA) revealed that out of 28, only eight PCs exhibited more than 1.0 eigen value and showed about 81.84% total variability. For selecting the high yielding genotypes in rice, the characters viz., spikelet density, spikelet fertility, number of tillers plant–1and panicle weight plant–1 may be considered. On the basis of high PC score ten most prominent lines namely JNPT-1059-9, JNPT-1059- 10, JNPT1062-1, JNPT-1062-2, JNPT-1064-9, JNPT-1065-1, JNPT-1065-2, JNPT1065-3, JNPT-1066-52 and JNPT-1068- 65 were identified for yield and quality traits.

scholarly journals Application of principal component analysis in assessment of relation between the parameters of technological quality of wheat grains treated with inert dusts against rice weevil (Sitophilus oryzae L.)

2011 ◽  
Vol 26 (4) ◽  
pp. 385-390 ◽  
Author(s):  
Marija Bodroza-Solarov ◽  
Petar Kljajic ◽  
Goran Andric ◽  
Bojana Filipcev ◽  
Olivera Simurina ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Sitophilus Oryzae ◽  
Principal Component ◽  
Component Analysis ◽  
Quality Parameters ◽  
Two Dimensions ◽  
Data Set ◽  
Inert Dusts ◽  
Total Variability ◽  
Technological Quality

Quality parameters of several wheat grain lots (low vitreous and high vitreous grains, non-infested and infested with rice weevils, (Sitophilus oryzae L.) treated with inert dusts (natural zeolite, two diatomaceous earths originating from Serbia and a commercial product Protect-It?) were investigated. Principal component analysis (PCA) was used to investigate the classification of treated grain lots and to assess how attributes of technological quality contribute to this classification. This research showed that vitreousness (0.95) and test weight (0.93) contributed most to the first principal component whereas extensigraph area (-0.76) contributed to the second component. The determined accountability of the total variability by the first component was around 55%, while with the second it was 18%, which means that those two dimensions together account for around 70% of total variability of the observed set of variables. Principal component analysis (PCA) of data set was able to distinguish among the various treatments of wheat lots. It was revealed that inert dust treatments produce different effects depending on the degree of endosperm vitreousness.

scholarly journals Principal component analysis for heavy metals in hydrographic sub-basins of the rivers Capivari and Mortes/MG

2021 ◽  
Vol 12 (4) ◽  
pp. 93-104
Author(s):  
Diego Vipa Amâncio ◽  
Gilberto Coelho ◽  
Rosângela Francisca de Paula Vitor Marques ◽  
Laíla Luana Campos ◽  
Renato Antônio da Silva
Keyword(s):  
Heavy Metals ◽  
Principal Component Analysis ◽  
Principal Component ◽  
Industrial Effluents ◽  
Component Analysis ◽  
Water Sources ◽  
Average Percentage ◽  
Total Variability ◽  
Components Analysis ◽  
Iron Manganese

Population growth and industrialization are correlated with the contamination of water resources by the release of untreated effluents into water sources. The objective of this work was to characterize heavy metals in sub-basins of the rivers Capivari and Mortes and the variability using principal component analysis (PCA). Three points were sampled at GD1 (P - I at Ingai – Minduri River, P - II at Capivari River and P - III at Ingai – Luminarias River) and three points at GD2 (P - IV at Mortes River, P - V at Peixe River and P - VI at Ribeirao dos Tabuoes). The monitoring period was from April 2015 to February 2016. Analysis of Aluminum, Bromine, Copper, Hexavalent Chromium, Iron, Manganese, Nickel and Zinc were evaluated. We compared the results with the Maximum Allowed Value in agreement with class 2, according to DN COPAM CERH 01/08. We also observed variables above the allowed value due to the discharge of domestic and industrial effluents, interference from precipitation and the contact between livestock and water sources. The principal components analysis (PCA) revealed that on average, the principal component 1 corresponds to 62.2% of the total variability of the data considering GD 1, and, in GD 2, PC 1 is responsible for a higher average percentage of the total variability of the data, corresponding to 73.4%, hence being more representative.

scholarly journals Distinction between Early Keratoconus with Normal Vision and Normal Cornea based on Pattern Recognition Analysis

2017 ◽  
Vol 6 (2) ◽  
pp. 58-66
Author(s):  
Alfredo Vega-Estrada ◽  
Jorge L Alio ◽  
Pablo Sanz ◽  
María J Prieto ◽  
Antonio Cardona ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Corneal Topography ◽  
Normal Vision ◽  
Total Variability ◽  
Abnormal Pattern ◽  
Pattern Recognition Analysis

ABSTRACT Aim To find the profile that differentiates most normal corneas from early keratoconus with normal vision. Materials and methods Multicentric, comparative study including a total of 995 eyes and divided into two groups: 625 eyes suffering from early keratoconus but with normal vision [spectacle corrected distance visual acuity (CDVA) of 0.9 decimal or better] and 370 normal control eyes with same normal vision level. To ascertain the main differences that would allow the identification of the keratoconic eyes from normals, a pattern recognition analysis was performed combining two statistical methods: Principal component analysis (PCA) and discriminant analysis. Visual and refractive parameters, corneal topography, aberrometry, and PCA were evaluated in both groups. Results The application of the PCA with Varimax rotation offered a total of five factors which explains the 85.51% of the total variability. Discriminant analysis indicated that factors 1 and 3 were at the greatest discriminating capacity. From a total of 318 cases, the newly identified abnormal pattern profile allowed the recognition of 275, which presents a sensitivity and specificity of 71.6 and 97.3% respectively. Conclusion In eyes with normal CDVA, those factors related to the nonorthogonal shape irregularity of the cornea and the refractive power are the ones that showed more discriminating capabilities between normal and early keratoconic eyes. Clinical significance Principal component analysis allows to correctly discriminate between normal and mild keratoconus patients; additionally, this method is not restricted to a particular corneal topography technology and is available to any normally equipped ophthalmology office. How to cite this article Alio JL, Vega-Estrada A, Sanz P, Prieto MJ, Cardona A, Maldonado M, Gutierrez R, Barraquer RI, Sádaba LM. Distinction between Early Keratoconus with Normal Vision and Normal Cornea Based on Pattern Recognition Analysis. Int J Kerat Ect Cor Dis 2017;6(2):58-66.

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