• Journal of Ginseng Research
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• v.35 no.1
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• pp.31-38
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• 2011

In order to distinguish the cultivation area of Panax ginseng, principal component analysis (PCA) using quantitative and qualitative data acquired from HPLC was carried out. A new HPLC method coupled with evaporative light scattering detection (HPLC-ELSD) was developed for the simultaneous quantification of ten major ginsenosides, namely $Rh_1$, $Rg_2$, $Rg_3$, $Rg_1$, Rf, Re, Rd, $Rb_2$, Rc, and $Rb_1$ in the root of P. ginseng C. A. Meyer. Simultaneous separations of these ten ginsenosides were achieved on a carbohydrate analytical column. The mobile phase consisted of acetonitrile-water-isopropanol, and acetonitrile-water-isopropanol using a gradient elution. Distinct differences in qualitative and quantitative characteristics for ginsenosides were found between the ginseng roots produced in two different Korean cultivation areas, Ganghwa and Punggi. The ginsenoside profiles obtained via HPLC analysis were subjected to PCA. PCA score plots using two principal components (PCs) showed good separation for the ginseng roots cultivated in Ganghwa and Punggi. PC1 influenced the separation, capturing 43.6% of the variance, while PC2 affected differentiation, explaining 18.0% of the variance. The highest contribution components were ginsenoside $Rg_3$ for PC1 and ginsenoside Rf for PC2. Particularly, the PCA score plot for the small ginseng roots of six-year old, each of which was light than 147 g fresh weight, showed more distinct discrimination. PC1 influenced the separation between different sample sets, capturing 51.8% of the variance, while PC2 affected differentiation, also explaining 28.0% of the variance. The highest contribution component was ginsenoside Rf for PC1 and ginsenoside $Rg_2$ for PC2. In conclusion, the HPLC-ELSD method using a carbohydrate column allowed for the simultaneous quantification of ten major ginsenosides, and PCA analysis of the ginsenoside peaks shown on the HPLC chromatogram would be a very acceptable strategy for discrimination of the cultivation area of ginseng roots.

• Journal of Korea Multimedia Society
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• v.20 no.2
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• pp.134-143
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• 2017

Machine fault diagnosis recovers all the studies that aim to detect automatically faults or damages on machines. Generally, it is very difficult to diagnose a machine fault by conventional methods based on mathematical models because of the complexity of the real world systems and the obvious existence of nonlinear factors. This study develops an automatic machine fault diagnosis system that uses pattern recognition techniques such as principal component analysis (PCA) and artificial neural networks (ANN). The sounds emitted by the operating machine, a drill in this case, are obtained and analyzed for the different operating conditions. The specific machine conditions considered in this research are the undamaged drill and the defected drill with wear. Principal component analysis is first used to reduce the dimensionality of the original sound data. The first principal components are then used as the inputs of a neural network based classifier to separate normal and defected drill sound data. The results show that the proposed PCA-ANN method can be used for the sounds based automated diagnosis system.

• Korean Journal of Plant Taxonomy
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• v.34 no.3
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• pp.205-220
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• 2004

Numerical analysis based on the 67 morphological characters from 28 populations of 6 species of Korean Rhus sensu lato (Anacardiaceae) was performed for the taxonomic delimitation. Based on the results of PCA with 47 quantitative characters, the sum of contributions for the total variance of three major principal components was 77,9% (PCl 35.2%, PC2 22.5% and PC3 20.2%). The sum of contributions for the total variance of three major principal components were 90,7% (PCl 37.7%, PC2 33.0% and PC3 20.0%) based on the results of PCA with 20 qualitative The characters. Two dimensional plotting from PCA results recognized six distinct species. UPGMA phenogram based on simple matching coefficient method recognized clear taxonomic delimitations among six taxa. On the cluster analysis, qualitative characters were more useful for grouping the species treated. Numerical analysis was very valuable to delimit the Korean taxa of Rhus s.l.

• The Journal of the Korea Contents Association
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• v.5 no.6
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• pp.41-48
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• 2005

This paper presents a face recognition method based on the combination of well-known statistical representations of Principal Component Analysis(PCA), and Linear Discriminant Analysis(LDA) with Radial Basis Function Networks. The original face image is first processed by PCA to reduce the dimension, and thereby avoid the singularity of the within-class scatter matrix in LDA calculation. The result of PCA process is applied to LDA classifier. In the second approach, the LDA process Produce a discriminational features of the face image, which is taken as the input of the Radial Basis Function Network(RBFN). The proposed approaches has been tested on the ORL face database. The experimental results have been demonstrated, and the recognition rate of more than 93.5% has been achieved.

• Food Science of Animal Resources
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• v.44 no.4
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• pp.934-950
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• 2024

This study addresses the prevalent issue of meat species authentication and adulteration through a chemometrics-based approach, crucial for upholding public health and ensuring a fair marketplace. Volatile compounds were extracted and analyzed using headspace-solid-phase-microextraction-gas chromatography-mass spectrometry. Adulterated meat samples were effectively identified through principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Through variable importance in projection scores and a Random Forest test, 11 key compounds, including nonanal, octanal, hexadecanal, benzaldehyde, 1-octanol, hexanoic acid, heptanoic acid, octanoic acid, and 2-acetylpyrrole for beef, and hexanal and 1-octen-3-ol for pork, were robustly identified as biomarkers. These compounds exhibited a discernible trend in adulterated samples based on adulteration ratios, evident in a heatmap. Notably, lipid degradation compounds strongly influenced meat discrimination. PCA and PLS-DA yielded significant sample separation, with the first two components capturing 80% and 72.1% of total variance, respectively. This technique could be a reliable method for detecting meat adulteration in cooked meat.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1339-1347
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• 2020

This study is a preliminary study to design a character automation system that considers the facial characteristics of mammals. The experimental data of this study was conducted on dogs (dog breeds) and humans, which were designed to be used in many contents. First, data was extracted from 100 types of dogs and 100 human data. Second, the criteria for measuring the ratio of important parts of the dog and human face were suggested. In addition, a comparative analysis of the face of a dog and a human face is conducted. Lastly, by analyzing the main component(PCA), the most characteristic elements in the faces of dogs and humans were analyzed. As a result, it was confirmed that the length of the face, the size of the eyes, the length of the glabellar, and the length of the glabellar and other parts are important. Through this study, the features of the dog's face that are different from humans are expected to contribute to the animal character automation.

• Particle and aerosol research
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• v.7 no.1
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• pp.31-44
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• 2011

The hourly volatile organic compounds(VOCs) concentrations between 2005 and 2008 at Bulgwang photochemical assessment monitoring station were investigated to establish a method for quality assurance and quality control(QA/QC) procedure. Systematic error, erratic error, and random error, which was manifested by outlier and highly fluctuated data, were checked and removed. About 17.3% of the raw data were excluded according to the proposed QA/QC procedure. After QA/QC, relative standard deviation for representing 15 species concentrations decreased from 94.7-548.0% to 63.4-125.8%, implying the QA/QC procedure is proper. For further evaluation about the adequacy of QA/QC procedure, principal components analysis(PCA) was carried out. When the data after QA/QC procedure was used for PCA, the extracted principal components were different from the result from the raw data and could logically explain the major emission sources(gasoline vapor, vehicle exhaust, and solvent usage). The QA/QC procedure based on the concept of errors is inferred to proper to be applied on VOCs. However, an additional QA/QC step considering the relationship between species in the atmosphere needs to be further considered.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.09a
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• pp.373-376
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• 2004

To investigate the vertical petrological and hydrological characteristics of fractured rock, geophysical and chemical logging were executed at 5 boreholes installed in the study area. The geophysical and hydrochemical logging data were analysed by using principal components analysis (PCA). Three main variables from PCA explained 86.4% of total variance of geophysical log data. The PCA results showed that PCl is closely related to groundwater properties and PC2 and PC3 are influenced by rock and fracture properties. Hydrochemical analysis indicated the presence of highly fractrued zone at the depth of 60m.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.3
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• pp.54-60
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• 2010

The local appearance-based method is one of the face recognition methods that divides face image into small areas and extracts features from each area of face image using statistical analysis. It collects classification results of each area and decides identity of a face image using a voting scheme by integrating classification results of each area of a face image. The conventional local appearance-based method divides face images into small pieces and uses all the pieces in recognition process. In this paper, we propose a local appearance-based method that makes use of only the relatively important facial components. The proposed method detects the facial components such as eyes, nose and mouth that differs much from person to person. In doing so, the proposed method detects exact locations of facial components using support vector machines (SVM). Based on the detected facial components, a number of small images that contain the facial parts are constructed. Then it extracts features from each facial component image using principal components analysis (PCA). We compared the performance of the proposed method to those of the conventional methods. The results show that the proposed method outperforms the conventional local appearance-based method while preserving the advantages of the conventional local appearance-based method.

• Journal of Biosystems Engineering
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• v.29 no.3
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• pp.261-266
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• 2004

This study was Performed to classify the acoustic emission(AE) signal due to surface cracking and moisture movement in the flat-sawn boards of oak(Quercus Variablilis) during drying using the principal component analysis(PCA) and artificial neural network(ANN). To reduce the multicollinearity among AE parameters such as peak amplitude, ring-down count event duration, ring-down count divided by event duration, energy, rise time, and peak amplitude divided by rise time and to extract the significant AE parameters, correlation analysis was performed. Over 96 of the variance of AE parameters could be accounted for by the first and second principal components. An ANN analysis was successfully used to classify the Af signals into two patterns. The ANN classifier based on PCA appeared to be a promising tool to classify the AE signals from wood drying.