• Bulletin of the Korean Chemical Society
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• v.30 no.5
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• pp.1121-1126
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• 2009

This paper describes a pattern recognition method of Magnoliae flos based on a gas chromatographic/mass spectrometric (GC/MS) analysis of the essential oil components. The botanical drug is mainly comprised of the four magnolia species (M. denudata, M. biondii, M. kobus, and M. liliflora) in Korea, although some other species are also being dealt with the drug. The GC/MS separation of the volatile components, which was extracted by the simultaneous distillation and extraction (SDE), was performed on a carbowax column (supelcowax 10; 30 m{\time}0.25 mm{\time}0.25{\mu}m$) using temperature programming. Variance in the retention times for all peaks of interests was within RSD 2% for repeated analyses (n = 9). Of the 74 essential oil components identified from the magnolia species, approximately 10 major components, which is $\alpha$-pinene, $\beta$-pinene, sabinene, myrcene, d-limonene, eucarlyptol (1,8-cineol), $\gamma$-terpinene, p-cymene, linalool, $\alpha$-terpineol, were commonly present in the four species. For statistical analysis, the original dataset was reduced to the 13 variables by Fisher criterion and factor analysis (FA). The essential oil patterns were processed by means of the multivariate statistical analysis including hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA). All samples were divided into four groups with three principal components by PCA and according to the plant origins by HCA. Thirty-three samples (23 training sets and 10 test samples to be assessed) were correctly classified into the four groups predicted by PCA. This method would provide a practical strategy for assessing the authenticity or quality of the well-known herbal drug, Magnoliae flos.

• Journal of Environmental Health Sciences
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• v.26 no.2
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• pp.34-40
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• 2000

The purpose of this study was to investigate the seasonal variation of PAHs and to estimate their source characteristics in Taegu area. To do this, four sampling sites were selected to represent an industrial, a traffic, a traffic & residential, and a residential area in Taegu. Total of 72 samples had been collected from January, 1999 to September, 1999 on glass micro fiber filters by high volume air sampler. The PAHs in the total suspended particulate were extracted by a soxhlet process with dichloromethane and analyzed by GC/MSD, GC/FID. A statistical analysis was performed for the PAHs data set using a principal component analysis to derive important factor inherent in the interactions among the variables. The specific conclusions of this research are: 1) There was a significant seasonal and local variation in the atmospheric concentration of PAHs. The seasonal variation is winter>spring>Fall>summer, and the local variation is industrial>traffic>graffic & residential>residential area. 2) To evaluate the correlation between a measured PAHs and other affecting factors such as air pollutant concentration and meterological data, statistical analysis was performed. PAHs and other affecting factors such as air pollutant concentration and meterological data, statistical analysis was performed. PAHs have negative correlation with temperature (r=-0.593, p<0.05), radiation(r=-0.535, p<0.05), and O3(r=-0.719, p<0.05), but have positive correlation with NO(r=0.615, p<0.05) 3)Finally, multivariate analysis was performed for the PAHs dat set to identify and to estimate the source contributions of PAHs. According to results of statistical analysis, it could be identifies as three factors such as vehicular/gasoline, vehicular/diesel, and combustion in Taegu area.

• Journal of the Korean Data and Information Science Society
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• v.28 no.3
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• pp.533-545
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• 2017

Quantile regression models provide a variety of useful statistical information by estimating the conditional quantile function of the response variable. However, the traditional linear quantile regression model can lead to the distorted and incorrect results when analysing real data having a nonlinear relationship between the explanatory variables and the response variables. Furthermore, as the complexity of the data increases, it is required to analyse multiple response variables simultaneously with more sophisticated interpretations. For such reasons, we propose a multivariate quantile regression tree model. In this paper, a new split variable selection algorithm is suggested for a multivariate regression tree model. This algorithm can select the split variable more accurately than the previous method without significant selection bias. We investigate the performance of our proposed method with both simulation and real data studies.

• The Korean Journal of Applied Statistics
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• v.29 no.1
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• pp.99-112
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• 2016

Asymmetric jump-diffusion models are effectively used to model the dynamic behavior of asset prices with abrupt asymmetric upward and downward changes. However, the estimation of their extension to the multivariate asymmetric jump-diffusion model has been hampered by the analytically intractable likelihood function. This article confronts the problem using a data augmentation method and proposes a new Bayesian method for a multivariate asymmetric Laplace jump-diffusion model. Unlike the previous models, the proposed model is rich enough to incorporate all possible correlated jumps as well as mention individual and common jumps. The proposed model and methodology are illustrated with a simulation study and applied to daily returns for the KOSPI, S&P500, and Nikkei225 indices data from January 2005 to September 2015.

• The Korean Journal of Applied Statistics
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• v.34 no.4
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• pp.659-669
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• 2021

Multivariate regression often appears in longitudinal or functional data analysis. Since multivariate regression involves multi-dimensional response variables, it is more strongly affected by the so-called curse of dimension that univariate regression. To overcome this issue, Yoo (2018) and Yoo (2019a) proposed three model-based response dimension reduction methodologies. According to various numerical studies in Yoo (2019a), the default method suggested in Yoo (2019a) is least sensitive to the simulated models, but it is not the best one. To release this issue, the paper proposes an selection algorithm by comparing the other two methods with the default one. This approach is called principal selected response reduction. Various simulation studies show that the proposed method provides more accurate estimation results than the default one by Yoo (2019a), and it confirms practical and empirical usefulness of the propose method over the default one by Yoo (2019a).

• The Korean Journal of Applied Statistics
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• v.14 no.1
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• pp.137-148
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• 2001

Compositional data found in many practical applications consist of non-negative vectors of proportions with the constraint which the sum of the elements of each vector is unity. It is well-known that the statistical analysis of compositional data suffers from the unit-sum constraint. Moreover, the non-linear pattern frequently displayed by the data does not facilitate the application of the linear multivariate techniques such as principal component analysis. In this paper we develop new type of principal component analysis for compositional data using Box-Cox contrast transformation. Numerical illustrations are provided for comparative purpose.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.42 no.3
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• pp.284-289
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• 2009

We determined the influence of Sargassum beds on the water quality in Gamak Bay, Korea. Water temperature and salinity range from 3.3 to $23.4^{\circ}C$ and from 29.6 to 33.7 psu, respectively. Dissolved oxygen was 10.45 mg L-1 in the Sargassum bed and 9.23 mg L-1 in the control. Chlorophyll-a was $3.90{\mu}g\;L^{-1}$ in the Sargassum bed and 2.21${\mu}g \;L^{-1}$ in the control. Chemical oxygen demand were 1.14${\mu}g\;L^{-1}$ in the Sargassum bed and 1.43${\mu}g\;L^{-1}$ in the control. Total nitrogen were 0.038${\mu}g\;L^{-1}$ in the Sargassum bed and 0.067${\mu}g \;L^{-1}$ in the control. Total phosphorus were 0.043${\mu}g \;L^{-1}$ in the Sargassum bed and 0.072${\mu}g \;L^{-1}$ in the control. Multivariate statistical analysis was used to analyze data. Water temperature was highly positively correlated with DO (p<0.01). T-N was highly positively correlated with T-P (p<0.01).

• Journal of Ginseng Research
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• v.40 no.4
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• pp.344-350
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• 2016

Background: Mountain-cultivated ginseng (MCG) and cultivated ginseng (CG) both belong to Panax ginseng and have similar ingredients. However, their pharmacological activities are different due to their significantly different growth environments. Methods: An ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS/MS)-based approach was developed to distinguish MCG and CG. Multivariate statistical methods, such as principal component analysis and supervised orthogonal partial-least-squares discrimination analysis were used to select the influential components. Results: Under optimized UPLC-QTOF-MS/MS conditions, 40 ginsenosides in both MCG and CG were unambiguously identified and tentatively assigned. The results showed that the characteristic components of CG and MCG included ginsenoside Ra3/isomer, gypenoside XVII, quinquenoside R1, ginsenoside Ra7, notoginsenoside Fe, ginsenoside Ra2, ginsenoside Rs6/Rs7, malonyl ginsenoside Rc, malonyl ginsenoside Rb1, malonyl ginsenoside Rb2, palmitoleic acid, and ethyl linoleate. The malony ginsenosides are abundant in CG, but higher levels of the minor ginsenosides were detected in MCG. Conclusion: This is the first time that the differences between CG and MCG have been observed systematically at the chemical level. Our results suggested that using the identified characteristic components as chemical markers to identify different ginseng products is effective and viable.

• Journal of Plant Biotechnology
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• v.33 no.3
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• pp.161-169
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• 2006

Plant metabolomics is a plant biology field for identifying all of the metabolites found in a certain plant cell, tissue, organ, or whole plant in a given time and conditions and for studying changes in metabolic profiling as time goes or conditions change. Metabolomics is one of the most recently developed omics for holistic approach to biology and is a kind of systems biology. For holistic approach, metabolomics frequently uses chemometrics or multivariate statistical analysis of metabolic profillings. In plant biology, metabolomics is useful to determine functions of genes often in combination with DHA microarrays by analyzing tagged mutants of the model plants Arabidopsis and rice. This review paper attempted to introduce basic concepts of metabolomics and practical uses of multivariate statistical analysis of metabolic profiling obtained by $^1$H HMR and Fourier transform infrared spectrometry.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.4253-4264
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• 2022

Since the implementation of the phosphate project in Bled El-Hadba (BEH) deposit, western region of Tébessa, no detailed study has been conducted to assess the natural radioactivity distribution and the associated radiological risk parameter for this open-pit mine. For the sake of determining a credible premining reference database for the region of interest, 21 samples were collected from different geological layers of the above-mentioned deposit. Gamma Spectrometry was applied for measuring radioactivity using a high resolution HPGe semiconductor detector. The obtained activity results have shown a significant broad variation in the radioactive contents for the different phosphate samples. The total average concentrations (in Bq·kg^-1) for ²²⁶Ra, ²³⁸U, ²³⁵U, ²³²Th and ⁴⁰K computed for the different type of phosphate layers were found to be 570 ± 169, 788 ± 280, 52 ± 18, 66 ± 6 and 81 ± 18 respectively. The mean activity concentrations of the measured radionuclides were compared to other regional and worldwide deposits. The ratios between the detected radioisotopes have been calculated for spatial distribution of natural radionuclides in the study area. Based on the aforementioned activity concentrations, the corresponding radiation hazard parameters were assessed. Correlations between the obtained parameters were drawn and a multivariate statistical analysis (Pearson Correlation, Cluster and Factor analysis) was carried out in order to identify the existing relationships.