• Korean Journal of Medicinal Crop Science
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• v.24 no.2
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• pp.93-100
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• 2016

Background: A set of logical criteria that can accurately identify and verify the cultivation region of raw materials is a critical tool for the scientific management of traditional herbal medicine. Methods and Results: Volatile compounds were obtained from 19 and 32 samples of Angelica gigas Nakai cultivated in Korea and China, respectively, by using steam distillation extraction. The metabolites were identified using GC/MS by querying against the NIST reference library. Data binning was performed to normalize the number of variables used in statistical analysis. Multivariate statistical analyses, such as Principal Component Analysis (PCA), Partial Least Squares-Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA) were performed using the SIMCA-P software. Significant variables with a Variable Importance in the Projection (VIP) score higher than 1.0 as obtained through OPLS-DA and those that resulted in p-values less than 0.05 through one-way ANOVA were selected to verify the marker compounds. Among the 19 variables extracted, styrene, ${\alpha}$-pinene, and ${\beta}$-terpinene were selected as markers to indicate the origin of A. gigas. Conclusions: The statistical model developed was suitable for determination of the geographical origin of A. gigas. The cultivation regions of six Korean and eight Chinese A. gigas. samples were predicted using the established OPLS-DA model and it was confirmed that 13 of the 14 samples were accurately classified.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.299-302
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of$.$10 persons show that the proposed method yields high recognition rates.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.279-282
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• 2006

분광혼합은 위성영상에서 공간해상도의 한계로 인해 다른 분광 속성을 가진 물질들이 하나의 픽셀 내에 존재하게 될 때 발생하게 된다. 이러한 문제를 해결하고자 분광분리 알고리즘을 통해 픽셀의 순수한 영역만을 선정하여 정확도 높은 탐지가 가능하도록 하는 분광혼합분석(Spectral Mixture Analysis, 이하 SMA)을 고해상도 영상에 적용하였다. 본 연구는 산림의 훼손이 심각한 강원도 정선군 임계지역의 QuickBird 다중분광 위성영상을 이용하였다. 주성분분석(Principal Component Analysis, 이하 PCA)으로 생성된 결과 영상의 1, 2, 3번 밴드를 추출한 후에 밴드간의 Scatter plots 내에서 끝지점에 위치하는 Endmember를 3개(나지, 산림, 초지) 선정하였다. 선정된 Endmember를 토대로 작성된 fraction 영상을 이용하여 강원도 임계지역의 산림훼손으로 초지와 나지로 변화된 지역을 탐지하여 보았다.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.822-826
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• 2001

The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of 10 persons show that the proposed method yields high recognition rates.

• Journal of the Korean Chemical Society
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• v.63 no.2
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• pp.78-84
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• 2019

High-resolution $^1H$ NMR spectroscopic technique has been widely used as one of the most powerful analytical tools in food chemistry as well as to define molecular structure. The $^1H$ NMR spectra-based metabolomics has focused on classification and chemometric analysis of complex mixtures. The principal component analysis (PCA), an unsupervised clustering method and used to reduce the dimensionality of multivariate data, facilitates direct peak quantitation and pattern recognition. Using a combination of these techniques, the various green teas and black teas brewed were investigated via metabolite profiling. These teas were characterized based on the leaf size and country of cultivation, respectively.

• Journal of the Korean Society of Food Science and Nutrition
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• v.40 no.4
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• pp.613-617
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• 2011

This study was conducted to compare the quality of the brown rice (BR) and milled rice (MR) during storage. To assess quality, BR and MR were analysed by their fat acidity and flavor pattern using a SMart Nose$^{(R)}$. BR was stored for 30 days at $30^{\circ}C$, and analysed after 5, 15, 20, and 30 days of storage. MR produced in 2005, 2009, and 2010 were also tested. The fat acidity of both rice groups was increased with extended storage and the fat acidity of BR was more rapidly increased than that of MR in general. The flavor patterns from the SMart Nose$^{(R)}$ results were analyzed by the principal component analysis (PCA). The major groups of atomic mass unit (amu) for good discrimination contribution were from 41 to 85 amus. The PCA1 and PCA2 of BR were 95.64% and 2.78%, respectively when the samples were categorized by storage period. The PCA1 and PCA2 of MR were 81.18% and 13.85%, respectively when the samples were compared by production year. Both rice groups could be practically differentiated into flavor patterns by volatile properties for storage period. With regard to the correlation between fat acidity and flavor pattern, we could find that increasing storage period increased fat acidity value and changed flavor pattern from SMart Nose$^{(R)}$. Accordingly, SMart Nose$^{(R)}$ could be successfully used for easy screening and quality evaluation of stored rice.

• Journal of Plant Biotechnology
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• v.38 no.1
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• pp.9-14
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• 2011

To determine whether pattern recognition based on metabolite fingerprinting for whole cell extracts can be used to discriminate cultivars metabolically, leaves of nine commercial Orostachys plants were subjected to Fourier transform infrared spectroscopy (FT-IR). FT-IR spectral data from leaves were analyzed by principal component analysis (PCA) and Partial least square discriminant analysis (PLS-DA). The dendrogram based on hierarchical clustering analysis of these PLS-DA data separated the nine Orostachys species into five major groups. The first group consisted of O. iwarenge 'Yimge', 'Jeju', 'Jeongsun' and O. margaritifolius 'Jinju' whereas in the second group, 'Sacheon' was clustered with 'Busan,' both of which belong to O. malacophylla species. However, 'Samchuk', belong to O. malacophylla was not clustered with the other O. malacophylla species. In addition, O. minuta and O. japonica were separated to the other Orostachys plants. Thus we suggested that the hierarchical dendrogram based on PLS-DA of FT-IR spectral data from leaves represented the most probable chemotaxonomical relationship between commercial Orostachys plants. Furthermore these metabolic discrimination systems could be applied for reestablishment of precise taxonomic classification of commercial Orostachys plants.

• Journal of the Korean Society of Food Science and Nutrition
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• v.46 no.8
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• pp.971-978
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• 2017

Danggui leaf (Angelica acutiloba Kitagawa) has numerous dietary fiber and healthy functional properties such as anti-inflammatory activity. However, it is usually discarded after its roots are harvested. For wide application of danggui leaf, squeezed danggui leaf juice was used for making pan bread. Treatments included imported wheat flour (IMWF) and domestic wheat flour (DOWF) as a control, 1% soluble solid contents of squeezed danggui leaf juice instead of wheat flour (1% SDLJ), 2% SDLJ, 3% SDLJ, and 4% SDLJ. In the fermentation expansion, dough volume was not significantly different between DOWF and 1% SDLJ, whereas it decreased according to increased amount of squeezed danggui leaf juice. Regarding physical properties, springiness and cohesiveness decreased according to increased amount of squeezed danggui leaf juice and longer storage period. Gumminess, brittleness, and hardness increased according to increased amount of squeezed danggui leaf juice, although these were not statistically different among IMWF, DOWF, and 1% SDLJ after making pan bread. Principal component analysis (PCA) was performed to assess the correlation between storage period and physical properties. In the PCA, variance proportion of principal component (PC) 1 was 0.87 while that of PC 2 was 0.10. Further, 1% SDLJ showed similar results as DOWF and INWF after making pan bread and after 3 days of storage. In conclusion, use of 1% SDLJ is desirable for making squeezed danggui leaf juice.

• Journal of Biosystems Engineering
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• v.30 no.2 s.109
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• pp.121-126
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• 2005

In this study, a commercial electronic nose system was used to detect contamination of Salmonella bacteria. Odors from growth media contaminated with Salmonella typhimurium, Salmonella enteritidis, or Escherichia coli were collected and analyzed to evaluate a possibility of rapid detection of pathogen. Odor chromatograph showed that S. typhimurium, S. enteritidis, and E. coli had 7,6, and 9 main peaks, respectively. Retention time and intensity of the peaks were distinct for different bacteria species. Principal component analysis (PCA) were also performed to clarify odor differences. Analysis results showed that the odors for uncontaminated growth medium were differently grouped from the odors of contaminated one. The odor from the bacteria growth identified with two principal components, PC 1 and PC2. In PCA figures, odor groups were moved from left to right of PC 1 with elapse of the bacteria growth time. The electronic nose system could detect odors of S. typhimurium, S. enteritidis, E. coli when their concentration were $1.85\times10^6\;cfu/g,\;2.25\times10^6\;cfu/g,\;and\;1.8\times10^5 cfu/g$, respectively.

• Korean Journal of Remote Sensing
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• v.31 no.5
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• pp.361-370
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• 2015

Various algorithms such as Chronochrome(CC), Principle Component Analysis(PCA), and spectral unmixing have been studied for hyperspectral change detection. Change detection by spectral unmixing offers useful information on the nature of the change compared to the other change detection methods which provide only the locations of changes in the scene. However, hyperspectral change detection by spectral unmixing is still in an early stage. This research proposed a new approach to extract endmembers, which have identical properties in temporally different images, by Iterative Error Analysis (IEA) and Spectral Angle Mapper(SAM). The change map obtained from the difference of abundance efficiently showed the changed pixels. Simulated images generated from Compact Airborne Spectrographic Imager (CASI) and Hyperion were used for change detection, and the experimental results showed that the proposed method performed better than CC, PCA, and spectral unmixing using N-FINDR. The proposed method has the advantage of automatically extracting endmembers without prior information, and it could be applicable for the real images composed of many materials.