• Journal of the Korea Safety Management & Science
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• v.12 no.2
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• pp.231-236
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• 2010

The investigation was initiated with data from 27 abandoned mines along with 12 locations in Kyongbuk abandoned mines. The analyses for soil pollution by heavy metal pollutants were conducted by using correlation analysis, cluster analysis, and principal component analysis. The correlation analysis indicated that Ni and pH were highly correlated compared to those of other heavy metal ions. The principal component analyses showed that the heavy metal ions might be classified into two catagories, such as antropogenic and lithogenic components. The cluster analysis was also clearly divided by two groups. The respective two groups might be Pb-Zn-Cd-Cu and As-Hg-Ni.

• YAKHAK HOEJI
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• v.48 no.1
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• pp.6-12
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• 2004

Near-infrared spectra of methanol-water mixtures and acetonitrile-water mixtures were acquired to find interactions between solvents widely used for reverse-phase liquid chromatography. Mixtures were prepared to give a series of increasing mole fractions of methanol or acetonitrile in water. Data matrices of acquired spectra were analyzed to determine the proper number of principal components of each mixture system using Malinowski's factor indicator function. Initial guess of score matrix and loading matrix were calculated by nonlinear iterative partial least squares (NIPALS) algorithm for faster computation. Iterative target transform factor analysis (ITTFA) was applied to convert the initial estimation of score matrix to true concentration profile and loading matrix to pure spectra of pure components of the mixtures. In case of methanol-water the number of principal components was found to be 4 and those initial guess of factors were converted to the pure spectra of water methanol and two kinds of complexes. In case of acetonitrile-water the number of pure components of the mixtures was found to be 3 and the pure spectrum of acetonitrile-water complex was found. The nonlinear characteristics of concentration profiles of complexes in the solvent mixtures may give a good criteria in understanding their elution characteristics in reverse-phase liquid chromatogrsphy.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.367-379
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• 2012

In baseball rankings, prediction has been a subject of interest for baseball fans. To predict these rankings, (based on 2011 data from Korea Professional Baseball records) the arithmetic mean method, the weighted average method, principal component analysis, and principal component regression analysis is presented. By standardizing the arithmetic average, the correlation coefficient using the weighted average method, using principal components analysis to predict rankings, the final model was selected as a principal component regression model. By practicing regression analysis with a reduced variable by principal component analysis, we propose a rank predictability model of a pitcher part, a batter part and a pitcher batter part. We can estimate a 2011 rank of pro-baseball by a predicted regression model. By principal component regression analysis, the pitcher part, the other part, the pitcher and the batter part of the ranking prediction model is proposed. The regression model predicts the rankings for 2012.

• Journal of Animal Science and Technology
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• v.44 no.6
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• pp.643-652
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• 2002

Data were consisted of the ten body measurements (withers height, rump height, body length, chest depth, chest width, rump width, rump length, thurls width, hipbone width and chest girth) of 642 steers (Korean cattle), which was entered in the National Beef Quality Contest hosted by the Korea Animal Improvement Association from 1997 to 2001. A principal components analysis was used to classify the body types of the steers, and estimate the correlations between carcass traits and principal components for the body measurements of the first, second, third and fourth period, respectively. The first principal component of body measurements at the first, second, third and fourth period accounted for 76.0%, 83.0%, 72.7% and 57.4% of the total variance, respectively. The sum of first, second and third principal component at each period accounted for 86.69%, 90.49%, 84.62% and 77.26% of the total variance, respectively. At each period, all the first principal component of the body measurements were positive and it generally showed large framed body shape. The size of body was influenced mostly by chest depth(0.328${\sim}$0.339) and rump length(0.325${\sim}$0.341). The second, third and fourth principal component at the each period were various. There were positive correlations between principal components index of each period and carcass traits such as carcass weight(0.539${\sim}$0.755), average daily gain(0.256${\sim}$0.564), backfat thickness(0.227${\sim}$0.280), and eye muscle area(0.187${\sim}$0.344). The correlation with yield grade index(－0.246${\sim}$－0.110), however, was negative. The correlation with marbling score(0.066${\sim}$0.099) was low or statistically insignificant. According to principal component indexes of the second, third, and fourth components, the correlations with the carcass traits were various. There were no large differences between the correlations of the single body measurement trait with the carcass traits and the correlations of the first principal component indexes with the carcass traits.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.3
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• pp.79-84
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• 2021

A feature extraction method capable of reflecting features well while mainaining the properties of data is required in order to process high-dimensional data. The principal component analysis method that converts high-level data into low-dimensional data and express high-dimensional data with fewer variables than the original data is a representative method for feature extraction of data. In this study, we propose a principal component analysis method based on adaptive correlation when selecting principal component variables in principal component analysis for data feature extraction when the data is high-dimensional. The proposed method analyzes the principal components of the data by adaptively reflecting the correlation based on the correlation between the input data. I want to exclude them from the candidate list. It is intended to analyze the principal component hierarchy by the eigen-vector coefficient value, to prevent the selection of the principal component with a low hierarchy, and to minimize the occurrence of data duplication inducing data bias through correlation analysis. Through this, we propose a method of selecting a well-presented principal component variable that represents the characteristics of actual data by reducing the influence of data bias when selecting the principal component variable.

• Bulletin of the Korean Chemical Society
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• v.26 no.12
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• pp.2027-2032
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• 2005

This paper demonstrates details of thermal behavior of Langmuir-Blodgett (LB) film of poly(tert-butyl methacrylate) (PtBMA) by using the principal component analysis based two-dimensional correlation spectroscopy (PCA2D) through eigenvalue manipulating transformation (EMT). By uniformly lowering the power of a set of eigenvalues associated with the original data, the smaller eigenvalues becomes more prominent and the subtle contribution from minor components is now highlighted much more strongly than the original data. Thus, the subtle difference of thermal behavior of LB film of PtBMA from minor components, which is not readily detectable in the conventional 2D correlation analysis, is much more noticeable than the original data. PCA2D correlation spectra with EMT operation for the temperature-dependent IR spectra of LB film of PtBMA reveal the hidden property of phase transition processes during heating.

• Communications for Statistical Applications and Methods
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• v.23 no.3
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• pp.231-239
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• 2016

A graphical diagnostic method based on multiple case deletions in a regression context is introduced by using the sampling distribution of the difference between two least squares estimators with and without multiple cases. Principal components analysis plays a key role in deriving this diagnostic method. Multiple case deletions of test statistic are also considered when a new observation is fitted to a given regression model. The result is useful for detecting influential observations in econometric data analysis, for example in checking whether the consumption pattern at a later time is the same as the one found before or not, as well as for investigating the influence of cases in the usual regression model. An illustrative example is given.

• The Korean Journal of Quaternary Research
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• v.3 no.1
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• pp.55-68
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• 1989

This paper presents examples of the computer-aided zonation and correlation of pollen data from the Late-glacial to Holocene stratigraphic sequences at four sites in central Iowa, U.S.A. Spearman's rank correlation coefficient matrix and first four components of Principal components analysis plotted in a stratigraphic order are combined to provide an excellent zonation of the pollen data at each site. Correlation of the four pollen sequences are conducted by Principal components analysis of the data sets combined in one. The first and second principal components successfully provide correlation lines that match fairly closely the zone boundaries of each pollen sequence. The third and fourth components, in contrast, are greatly different from site to site, representing the unique pollen assemblages at each site.

• Journal of Microbiology and Biotechnology
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• v.17 no.10
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• pp.1682-1687
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• 2007

The differences in the nonvolatile metabolites of pine-mushrooms (Tricholoma matsutake Sing.) according to different parts and heating times were analyzed by applying principal component analysis (PCA) to $^1H$ nuclear magnetic resonance (NMR) spectroscopy data. The $^1H$ NMR spectra and PCA enabled the differences of nonvolatile metabolites among mushroom samples to be clearly observed. The two parts of mushrooms could be easily discriminated based on PC 1, and could be separated according to different heattreated times based on PC 3. The major peaks in the $^1H$ NMR spectra that contributed to differences among mushroom samples were assigned to trehalose, succinic acid, choline, leucine/isoleucine, and alanine. The content of trehalose was higher in the pileus than in the stipe of all mushroom samples, whereas succinic acid, choline, and leucine/isoleucine were the main components in the stipe. Heating resulted in significant losses of alanine and leucine/isoleucine, whereas succinic acid, choline, and trehalose were the most abundant components in mushrooms heat-treated for 3 min and 5 min, respectively.

• Animal Bioscience
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• v.34 no.1
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• pp.12-19
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• 2021

Objective: The objectives of the current study were to study the main environmental factors affecting birth weight (BW) and weaning weight (WW), estimate variance components, genetic parameters and genetic trend and to evaluate the variability and relationships among breeding value of BW and WW using principal components analysis (PCA). Methods: A total of 16,370 records were collected from 8,271 buffalo calves. Genetic parameters and breeding values were estimated using a bivariate animal model which includes direct, maternal and permanent maternal effects. These estimates were standardized and used in PCA. Results: The direct heritability estimates were 0.06 and 0.41 for BW and WW, respectively whereas direct maternal heritability values were 0.03 and 0.14, respectively. Proportions of variance due to permanent environmental effects of dam were 0.455 and 0.280 for BW and WW respectively. The genetic correlation between BW and WWs was weak approaching zero, but the maternal correlation was 0.26. The first two principal components (PC1 and PC2) were estimated utilizing the standardized breeding values according to Kaiser method. The total variance explained by the first two PCs was 71.17% in which 45.91% and 25.25% were explained by PC1 and PC2, respectively. The direct breeding values of BW were related to PC2 but those of WW and maternal breeding values of BW and WWs were associated with PC1. Conclusion: The results of genetic parameters and PCA indicate that BW and WWs were not genetically correlated and improving growth traits of Egyptian buffaloes could be achieved using WW without any adverse effect by BW.