Correlation and Principal Component Analysis on Main Agronomic Traits of New Waxy Corn Varieties

[Objective] This study was conducted to provide certain theoretical reference for the comprehensive evaluation and breeding of new fresh waxy corn vari- eties. [Method] With 5 good fresh waxy corn varieties as experimental materials, correlation analysis and principal component anatysis were performed on 13 agronomic traits, i.e., plant height, ear position, ear weight, ear diameter, axis diameter, ear length, bald tip length, ear row number, number of grains per row, 100-kernel weight, fresh ear yield, tassel length, and tassel branch number. [Result] The principal component analysis performed to the 13 agronomic traits showed that the first three principal components, i.e., the fresh ear yield factors, the tassel factors and the bald top factors, had an accumulative contribution rate over 87.2767%, and could basically represent the genetic information represented by the 13 traits. The first principal component is the main index for the selection and evaluation of good corn varieties which should have large ear, large ear diameter but small axis diameter, i.e., longer grains, larger number of grains per ear, higher, 100-grain weight and higher plant height. As to the second principal component, the plants of fresh corn varieties are best to have longer tassel and not too many branches, and under the premise of ensuring enough pollen for the female spike, the varieties with fewer tassel branches shoud be selected as far as possible. From the point of the third principal component, bald tip length affects the marketing quality of fresh corn, and during fariety evaluation and breeding, the bald top length should be control at the Iowest standard. [Conclusion] The fresh ear yield of corn is in close positive correlation with ear weight, 100-grain weight, ear diameter, number of grains per row and ear length, and plant height also affects fresh ear yield.

Correlation Analysis Between Yield Components and Yield Per Plant of Double-low Hybrid Rapeseed

[Objective] The experiment aimed to study the relation between yield components and yield per plant of double-low hybrid rapeseed and provide reference for breaking yield limitation of rapeseed and culturing new double-low hybrid rapeseed variety. [Method]The yield components and yield per plant of two cross combination of double-low hybrid rapeseed (B02, D04) and Shuza 6 were correlatively analyzed and compared, besides, the path analysis was also carried on to them. [Result] Among B02, D04 and Shuza No.6, effective pod number per plant and seeds per silique, seeds per pod and 1 000-grain weight were all negative correlation. In high yield hybrid, pod number per plant, seeds per pod had more impaction on yield per plant than 1 000-grain weight and the difference was at 0.01 significant level. In the control variety Shuza No.6, the impactions of pod number per plant and seeds per pod on yield per plant were bigger than that of 1 000-grain weight on yield per plant, however, the difference was not significant. [Conclusion] The improvement of effective pod number per plant was an important aim of breeding work of double low rapeseed breeding in Sichuan ecological region.

Research on the Correlation between Meteorological Factors and Flue-cured Tobacco Chemical Components in Different Altitudes of Lincang Region

In this study, the relationship between major meteorological factors and tobacco chemical components in different altitudes of Yongde County, Lincang Region of Yunnan Province was studied using correlation analysis and path analysis methods.The results showed that there was a difference on meteorological factors in different altitudes, causing different impacts on chemical components of flue-cured tobacco; contents of nicotine and total nitrogen decreased with the rising of altitude, conversely, total sugar and reducing sugar contents increased with the rising trend of altitude, and the differences on the contents of potassium （K） and chlorine（CI）, and the ratio of K to CI in different altitudes were not significant, at the altitude of 1 250-1 450 m, each chemical component indicator was better. The correlation between main meteorological factors and conventional chemical components of tobacco leaves was as follows： contents of reducing sugar and total sugar increased with the increase of rainfall and relative humidity, and decreased with the increase of sunshine duration; nicotine content increased with the increase of accumulated temperature and sunshine duration; total nitrogen （N） content increased with the increase of daily mean temperature and accumulated temperature; sugar-alkali ratio increased with the increase of rainfall. Path analysis showed that the direct effect of sunshine duration on reducing sugar and total sugar of tobacco leaves was faint, but the indirect effect was obvious; meanwhile, the direct and indirect effects of it on nicotine and total N were the greatest.

Correlation, Principal Component and Grey Relation Analysis of Sweetpotato Root Biological Traits

[Objective] This study was conducted to explore the internal relationship among root biological traits of sweetpotato, as well as the regularity in their formation and differentiation. [Method] The root traits of 10 sweetpotato cultivars were measured through hydroponic culture in a greenhouse and field survey, and then their correlations were analyzed by statistical methods. [Result] The root morphological traits of sweetpotato at seedling stage such as projected area, surface area, average diameter and volume processed the highest contribution rate （80.56%） 10 d after transplanting, and the contribution rate of root average diameter reached 27.79% 20 d after transplanting. Storage root fresh weight per plant shared extremely significant positive correlations with storage root fresh weight of penultimate node and storage root fresh weight of antepenultimate node, and a significant positive corre- lation with commercial storage root number, and a significant negative correlation with storage root number of penultimate node. Among them, the correlation coeffi- cient of storage root fresh weight per plant with storage root fresh weight of antepenultimate node was the highest （0.659 5）. Fifteen days after transplanting, storage root fresh weight per plant had significant negative correlations with root projected area, surface area and volume. There was a significant positive correlation between root dry weight and storage root fresh weight per plant 25 d after transplanting. Root dry weight, volume, length, average diameter of sweetpotato seedlings had higher relational degrees with storage root fresh weight per plant. Ten and twenty days after transplanting were important time for the growth and differentiation of sweetpotato roots. In addition, node length and planting depth had certain influence on sweetpotato yield, and direct relationship existed between the seedling root biological traits and storage root yield of sweetpotato. [Conclusion] The results provide theoretical support for standard cultivation and new variety breeding of sweetpotato.

Correlation analysis and multiple regression formulas of refractive errors and ocular components

The multiple regression formulas and correlation of ocular components with refractive errors are presented by Gaussian optics. The refractive error changing rate for the cornea and lens power, the axial length, anterior chamber depth(ACD) and vitreous chamber depth(VCD) are calculated, including nonlinear terms for more accurate rate functions than the linear theory. Our theory, consistent with the empirical data, shows that the Pearson correlation coefficients for spherical equivalent(SE) and ocular components are highest for SE with axial length, ACD and VCD and weakest for corneal power, lens power and lens thickness. Moreover, our regression formulas show the asymmetric feature of the correlation that the axial length, ACD and VCD are more strongly correlated(with higher negative regression constants) with refractive errors in eyes with hyperopia than in eyes with myopia, particularly for severe hyperopia.

Genetic Variation Analysis, Correlation Analysis and Principal Component Analysis on Agronomic Traits of Summer Sowing Soybean (Glycine max Merr.) in Huang-Huai-Hai Region

Genetic variation analysis, correlation analysis and principal component analysis were conducted to investigate the relationship between 12 yield-related agronomic traits of 87 summer sowing soybean eultivars in Huang-Huai-Hai region. According to the experimental results, effective branch number showed the maxi- mum variation coefficient and growth duration showed the minimum variation coefficient. The variation coefficient of bottom pod height, pod number per plant, grain number per plant, grain weight per plant and 100-grain weight of semi-determinate summer sowing soybean ranged between 18.38% -27.56. The variation coeffi- eient of plant height, bottom pod height, pod number per plant, grain number per plant and grain weight per plant of determinate summer sowing soybean ranged from 21.02% to 8.04%. In semi-determinate summer sowing soybean, yield showed extremely significantly positive correlation with grain number per pod, grain weight per plant and 100-grain weight, but extremely significantly negative correlation with effective branch number and significantly negative correlation with growth duration. In determinate summer sowing soybean, yidd showed extremely significantly positive correlation with stem diameter but significantly positive correlation with bottom pod height, while it showed no significant correlation with other agronomic traits. Principal component analysis of yield-rdated agronomic traits showed that cumulative contribution rates of the former four principal components to the variation of seml-determinate and determinate summer sowing soybean were 79.92% and 79.50%, respectively. Agronomic traits with the greatest variation should be selected first. Semi-determinate and determinate summer sowing soybean individu- als in Huang-Hnai-Hai region should be selected according to different podding habits. In addition, semi-determlnate soybean varieties with moderate plant height and growth duration, fewer effective branches, more grains per pod and greater 100-grain weight should be selected; determinate soybean varieties with thicker stem diameter, higher plant height and bottom pod height, more nodes on main stem, fewer grains per pod, more pods per plant and grains per plant should be selected.

Study on Segmented Correlation in EEG Based on Principal Component Analysis

In order to explore the correlation between the adjacent segments of a long term EEG, an improved principal component analysis(PCA) method based on mutual information algorithm is proposed. A one-dimension EEG time series is divided equally into many segments, so that each segment can be regarded as an independent variables and multi-segmented EEG can be expressed as a data matrix. Then, we substitute mutual information matrix for covariance matrix in PCA and conduct the relevance analysis of segmented EEG. The experimental results show that the contribution rate of first principal component(FPC) of segmented EEG is more larger than others, which can effectively reflect the difference of epileptic EEG and normal EEG with the change of segment number. In addition, the evolution of FPC conduce to identify the time-segment locations of abnormal dynamic processes of brain activities,these conclusions are helpful for the clinical analysis of EEG.

Correlation, Regression and Path Analyses of Seed Yield Components in <i>Crambe abyssinica</i>, a Promising Industrial Oil Crop

In the present study correlation, regression and path analyses were carried out to decide correlations among the agro- nomic traits and their contributions to seed yield per plant in Crambe abyssinica. Partial correlation analysis indicated that plant height (X1) was significantly correlated with branching height and the number of first branches (P 2) was significantly correlated with pod number of primary inflorescence (P 3) was significantly correlated with number of secondary branches (P -grain weight (P 4) was significantly correlated with seed yield per plant (P 7) was significantly correlated with seed yield per plant (P -grain weight (P -grain weight (X8) was significantly correlated with seed yield per plant (P -grain weight contributed significantly to seed yield per plant at P 7) and 1000-grain weight (X8) to seed yield per plant (Y) is Y = 0.006 X7 + 1.222 X8 - 7.191. The path coefficient of pod number per plant to seed yield per plant was 0.967 and that of 1000-grain weight was 0.194. The determination coefficient of pod number per plant and 1000-grain weight to seed yield per plant was 0.983 and the determination coefficient of other agronomic traits was 0.130. Coefficient of variance indicated that the length of primary inflorescence showed the greatest variation, followed by seed yield per plant, pod number per plant, number of secondary branches, branching height, pod number of primary inflorescence, number of first branches, seed yield per plot, 1000-grain weight and plant height. It was suggested that seed yield per plant in Crambe might be improved by increasing the pod number per plant through selection or cultivation, but the negative correlation between pod number per plant and 1000-grain weight also needs to be considered.

Correlation Analysis of Processing Technology,Physical Parameters and Chemical Components during Plain Stir-baking of Trichosanthis Radix

[Objectives]To explore the correlation of processing technology,physical parameters and chemical components during plain stir-baking of Trichosanthis Radix.[Methods]Based on mixture uniform experiment design,the Trichosanthis Radix was prepared by plain stir-bake method.Delphi method was used to evaluate and select the highest-scoring processed product for measuring physical parameters.UV spectrophotometry was used to determine the contents of starch and polysaccharide.The correlation and linear regression model of processing technology,physical parameters and chemical components were established with the aid of SPSS 26.0[Results]After processing by plain stir-bake method,the relative density and chromaticity showed a decreasing trend in the processed products of Trichosanthis Radix,the oxidation value,hydroscopic rate and swelling decreased firstly and then increased,and pH increased firstly and then decreased.The content of total starch decreased,the content of polysaccharide increased,and there was a negative correlation between them.There was a significant positive correlation between temperature and oxidation value,swelling and hydroscopic rate,hydroscopic rate and polysaccharide,and there was a significant negative correlation between relative density and hydroscopic rate or polysaccharide,total starch and hydroscopic rate or swelling.The linear relation model between processing technology and physical parameters and chemical components was r2>0.9.[Conclusions]After processing by plain stir-bake method,the physical parameters of Trichosanthis Radix changed,and there may be mutual conversion between total starch and polysaccharides.To a certain extent,physical parameters can be used to evaluate the quality of processed products of Trichosanthis Radix.This study is expected to provide a reference for research on quality evaluation of processed products of traditional Chinese medicine.

Correlation Analysis and Principal Component Analysis of Eight Agronomic Traits of Autumn Broad Beans (Vicia faba L.)

[Objectives] This study was conducted to give play to the yield-increasing potential of new broad bean varieties. [Methods] The correlation analysis and principal component analysis of eight main agronomic traits of 15 autumn-sown broad bean varieties in 2017-2018 were carried out. [Results] There were extremely complicated correlations between the agronomic traits. Among the various agronomic traits, the number of pods per plant showed a significant negative correlation from the number of seeds per pod(P<0.05), and significant negative correlations with pod length and 100-grain weight(P<0.01). The number of seeds per pod was significantly positively correlated with pod length(P<0.01). The 100-grain weight of broad beans was significantly positively correlated with the pod length(P<0.01), and was significantly negatively correlated with the number of pods per plant(P<0.01). Three principal components were established by principal component analysis, and the contribution rate of the first principal component was the highest, which was 40.18%. The contribution rates of the second principal component and the third principal component to the total variance were 24.08% and 13.55%, respectively. The score equations of the principal components were derived. [Conclusions] This study provides a theoretical reference for the application and promotion of new varieties of autumn broad beans.

Community Structure of Endophytic Fungi in Ginseng and Its Correlation Analysis with Active Components

[Objectives]The paper was to explore the correlation between active components of ginseng and community structure of endophytic fungi in ginseng under different cultivation methods.[Methods]Using ginsengs under two main planting patterns in Huanren County,Liaoning Province as the samples,the community structure of endophytic fungi was studied by molecular means,and the chemical components were identified and the content of chemical markers was measured via LC-MS.Moreover,the correlation between medicinal components of ginseng and diversity of endophytic fungal flora was analyzed.[Results]The diversity and active components of endophytic fungi of ginseng were different.The diversity analysis showed that the medicinal components of ginseng were positively correlated with some fungal groups in ginseng.[Conclusions]Artificial intervention in the planting process would affect the endophytic fungi in medicinal materials,and further affect the chemical components of ginseng medicinal materials.

Determination of Fourier Components of Spatial Correlation Function of Dielectric Susceptibility of Random Medium

In this work, we present a new method of directly determining Fourier components of the spatial correlation function of the dielectric susceptibility of random medium. The method is based on the analysis of the ratio of the spectrum of the light scattered by the spatial correlation components of the dielectric susceptibility of tissue to the spectrum of light scattered by the randomly distributed scatterers which are independent on the value of the spectrum of the incident light and the direction of the observation. The results may find wide applications in areas such as in biomedical diagnosis.

Spatial Heterogeneity of Soil Mineral Oxide Components in Depression Between Karst Hills,Southwest China

In karst regions,the spatial heterogeneity of soil mineral oxides and environmental variables is still not clear.We investigated the spatial heterogeneity of SiO2,Al2O3,Fe2O3,CaO,MgO,P2O5,K2O,and MnO contents in the soils of slope land,plantation forest,secondary forest,and primary forest,as well as their relationships with environmental variables in a karst region of Southwest China.Geostatistics,principal component analysis(PCA),and canonical correlation analysis(CCA)were applied to analyze the field data.The results show that SiO2was the predominant mineral in the soils(45.02%–67.33%),followed by Al2O3and Fe2O3.Most soil mineral oxide components had a strong spatial dependence,except for CaO,MgO,and P2O5in the plantation forest,MgO and P2O5in the secondary forest,and CaO in the slope land.Dimensionality reduction in PCA was not appropriate due to the strong spatial heterogeneity in the ecosystems.Soil mineral oxide components,the main factors in all ecosystems,had greater influences on vegetation than those of conventional soil properties.There were close relationships between soil mineral oxide components and vegetation,topography,and conventional soil properties.Mineral oxide components affected species diversity,organic matter and nitrogen levels.

A Methodology for Identification of Weather Sensitive Component of Electrical Load Using Empirical Mode Decomposition Technique

The expansion planning and operation of all three sectors, generation, transmission and distribution, of power system essentially require load forecasting. Weather conditions have significant impacts on forecasted load, especially short-term and mid-term. A momentous portion of the electrical energy is consumed, especially in cold or hot countries, to mitigate the impact of weather on the daily life of human society. Usually, weather dependent component of load is identified by fitting appropriate non-linear curve to the scatter plot of weather-load model. This technique some times shows lower correlation with weather variables. This paper proposes a new methodology to identify the weather sensitive component of electrical load using empirical mode decomposition (EMD) technique. The proposed methodology is applied to the daily peak load of Dhaka zone of Bangladesh Power System (BPS) of the year 2012. A detailed numerical process to evaluate the weather sensitive portion of the load is also presented. The proposed methodology is validated through statistical error evaluation process. Finally the salient features of the results are discussed.

Influence of Multi-component Failure on Mechanical Product Design Change

Design change is one of the most common activities in design and development of mechanical products, and the change will affected by the design and maintenance of the products. It is not uncommon that multiple components might run out of order simultaneously during the use of a product, which would have significant influences on the product performance. Such kind of multi-component failure must be considered early in the product design stage, therefore it is necessary to study multiple design changes incurred by multi-component failure. Existing studies on the design change only focus on single change and ignore multiple simultaneous changes. To address this problem, a change influence network model is establishes, and the propagation path of multiple changes on the change influence network is described according to the change propagation characteristics of mechanical products. By analyzing the degree of dependence between the nodes and the absorption situations of nodes to change propagation, the possible paths of change propagation on the change influence network are determined, and based on which, calculation of the change influence is carried out, which provides decision-making support for product designers. Finally, the feasibility of the proposed methodology is demonstrated by taking a household juicer for case study.

Principal Component-Discrimination Model and Its Application

Having researched for many years, seismologists in China presented about 80 earthquake prediction factors which reflected omen information of earthquake. How to concentrate the information that the 80 earthquake prediction factors have and how to choose the main factors to predict earthquakes precisely have become one of the topics in seismology. The model of principal component-discrimination consists of principal component analysis, correlation analysis, weighted method of principal factor coefficients and Mahalanobis distance discrimination analysis. This model combines the method of maximization earthquake prediction factor information with the weighted method of principal factor coefficients and correlation analysis to choose earthquake prediction variables, applying Mahalanobis distance discrimination to establishing earthquake prediction discrimination model. This model was applied to analyzing the earthquake data of Northern China area and obtained good prediction results.

Application of Principal Component Regression with Dummy Variable in Statistical Downscaling to Forecast Rainfall

Statistical downscaling (SD) analyzes relationship between local-scale response and global-scale predictors. The SD model can be used to forecast rainfall (local-scale) using global-scale precipitation from global circulation model output (GCM). The objectives of this research were to determine the time lag of GCM data and build SD model using PCR method with time lag of the GCM precipitation data. The observations of rainfall data in Indramayu were taken from 1979 to 2007 showing similar patterns with GCM data on 1st grid to 64th grid after time shift (time lag). The time lag was determined using the cross-correlation function. However, GCM data of 64 grids showed multicollinearity problem. This problem was solved by principal component regression (PCR), but the PCR model resulted heterogeneous errors. PCR model was modified to overcome the errors with adding dummy variables to the model. Dummy variables were determined based on partial least squares regression (PLSR). The PCR model with dummy variables improved the rainfall prediction. The SD model with lag-GCM predictors was also better than SD model without lag-GCM.

Generalized two-dimensional correlation near-infrared spectroscopy and principal component analysis of the structures of methanol and ethanol

Liquid state methanol and ethanol under different temperatures have been investigated by FT-NIR(Fourier transform nearinfrared) spectroscopy,generalized two-dimensional(2D) correlation spectroscopy,and PCA(principal component analysis) . First,the FT-NIR spectra were measured over a temperature range of 30-64(or 30-71) °C,and then the 2D correlation spectra were computed.Combining near-infrared spectroscopy,generalized 2D correlation spectroscopy,and references,we analyzed the molecular structures(especially the hydrogen bond) of methanol and ethanol,and performed the NIR band assignments. The PCA method was employed to verify the results of the 2D analysis.This study will be helpful to the understanding of these reagents.

Criterion for Blind Signals Separation Based on Correlation Function

Blind separation of source signals usually relies either on the condition of statistically independence or involving their higher-order cumulants. The model of two channels signal separation is considered. A criterion based on correlation functions is proposed. It is proved that the signals can be separated, using only the condition of noncorrelation. An algorithm is derived, which only involves the solution to quadric nonlinear equations.

Genetic Diversity and Correlation Studies for Cotton Leaf Curl Disease (CLCuD), Fiber &Yield Related Attributes in Exotic Lines of <i>Gossypium arboreum</i>L.

Genetic diversity provides the foundation for crop improvement. Genetic variation and associations among Cotton Leaf Curl Disease (CLCuD), fiber and yield related traits were investigated in exotic lines of Gossypium arboreum L. in an experimental field at the Central Cotton Research Institute (CCRI), Multan, Pakistan during the crop season 2011-12. One hundred and nineteen (119) accessions imported from USA through the Pakistan and US “Cotton Productivity Enhancement Program” (CPEP), were evaluated in this study. Various statistical approaches including descriptive statistics, correlation and principal component analysis was performed to evaluate and identify desirable genotypes. Results revealed that seed cotton yield was significantly and positively correlated with boll weight and number of bolls plant-1. Similarly, plant height was also significantly correlated with sympodial branches, lint percentage and micronaire value. Lint percentage showed a highly significant and positive correlation with plant height, sympodial branches and micronaire value. With respect to fiber traits, negative and significant relationships were observed between the micronaire value (MIC) and fiber strength. CLCuD showed no relationship with any of the studied traits, as all the G. arboreum L. lines evaluated were scored resistant to CLCuD. Principal component analysis (PCA) showed that the first four out of 11 components contributed about 65.88% of the total variation having an eigen value greater than 1. Based on PCA, the genotypes GS-4, GS-9, GS-8, GS-55 and GS-50 could be utilized successfully in a future breeding program based on having the highest positive loading factor for staple length (0.135) in PC1 and seed cotton yield (0.625), number of bolls plant-1, boll weight, first sympodial nod, staple length and fiber strength in PC2 respectively.