• YAKHAK HOEJI
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• v.60 no.1
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• pp.29-35
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• 2016

Traditional Korean medicines may be managed more scientifically, through the development of logical criterion to verify their cultivation region. It contributes to advance the industry of traditional herbal medicines. Volatile compounds were obtained from 14 samples of domestic Taeksa and 30 samples of Chinese Taeksa by steam distillation. The metabolites were identified by NIST mass spectral library in the obtained gas chromatography/mass spectrometer (GC/MS) data of 35 training samples. The multivariate statistical analysis, such as Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), were performed based on the qualitative and quantitative data. Finally trans-(2,3-diphenylcyclopropyl)methyl phenyl sulfoxide (47.265 min), 1,2,3,4-tetrahydro-1-phenyl-naphthalene (47.781 min), spiro[4-oxatricyclo[5.3.0.0.(2,6)]decan-3-one-5,2'-cyclohexane] (54.62 min), 6-[7-nitrobenzofurazan-4-yl]amino-morphinan-4,5-epoxy (54.86 min), p-hydroxynorephedrine (55.14 min) were determined as marker metabolites to verify candidates for the origin of Taeksa. The statistical model was well established to determine the origin of Taeksa. The cultivation areas of test samples, each 3 domestic and 6 Chinese Taeksa were predicted by the established OPLS-DA model and it was confirmed that all 9 samples were precisely classified.

• 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.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.63 no.4
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• pp.360-377
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• 2018

The levels of 12 isoflavones were measured in soybean (Glycine max (L.) Merrill) sprouts of 68 genetic varieties from three countries (China, Japan, and Korea). The isoflavone profile differences were analyzed using data mining methods. A principal component analysis (PCA) revealed that the CSRV021 variety was separated from the others by the first two principal components. This variety appears to be most suited for functional food production due to its high isoflavone levels. Partial least squares discriminant analysis (PLS-DA) and orthogonal projections to latent structures discriminant analysis (OPLS-DA) showed that there are meaningful isoflavone compositional differences in samples that have different countries of origin. Hierarchical clustering analysis (HCA) of these phytochemicals resulted in clusters derived from closely related biochemical pathways. These results indicate the usefulness of metabolite profiling combined with chemometrics as a tool for assessing the quality of foods and identifying metabolic links in biological systems.

• Analytical Science and Technology
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• v.29 no.1
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• pp.1-9
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• 2016

It is believed that traditional Korean medicines can be managed more scientifically through the development of logical criteria to verify their region of cultivation, and that this could contribute to the advancement of the traditional herbal medicine industry. This study attempted to determine such criteria for Sansuyu. The volatile compounds were obtained from 20 samples of domestic Corni fructus (Sansuyu) and 45 samples of Chinese Sansuyu by steam distillation. The metabolites were identified in the NIST Mass Spectral Library via the obtained gas chromatography/mass spectrometer (GC/MS) data of 53 training samples. Data binning at 0.2 min intervals was performed to normalize the number of variables used in the statistical analysis. Multivariate statistical analyses, such as principle 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 package. Significant variables with a variable importance in the projection (VIP) score higher than 1.0 were obtained from OPLS-DA, and variables that resulted in a p-value of less than 0.05 through one-way ANOVA were selected to verify the marker compounds. Finally, among the 11 variables extracted, 1-ethylbutyl-hydroperoxide (9.089 min), nonadecane (20.170 min), butylated hydroxytoluene (25.319 min), 5β,7βH,10α-eudesm-11-en-1α-ol (25.921 min), 7,9-bis(2-methyl-2-propanyl)-1-oxaspiro[4.5]deca-6,9-diene-2,8-dione (34.257 min), and 2-decyldodecyl-benzene (54.717 min) were selected as markers to indicate the origin of Sansuyu. The statistical model developed was suitable for the determination of the geographical origin of Sansuyu. The cultivation areas of four Korean and eight Chinese Sansuyu samples were predicted via the established OPLS-DA model, and it was confirmed that 11 of the 12 samples were accurately classified.

• Analytical Science and Technology
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• v.33 no.2
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• pp.98-107
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• 2020

Methamphetamine (MA) is currently the most abused illicit drug in Korea. MA is produced by chemical synthesis, and the final target drug that is produced contains small amounts of the precursor chemicals, intermediates, and by-products. To identify and quantify these trace compounds in MA seizures, a practical and feasible approach for conducting chromatographic fingerprinting with a suite of traditional chemometric methods and recently introduced machine learning approaches was examined. This was achieved using gas chromatography (GC) coupled with a flame ionization detector (FID) and mass spectrometry (MS). Following appropriate examination of all the peaks in 71 samples, 166 impurities were selected as the characteristic components. Unsupervised (principal component analysis (PCA), hierarchical cluster analysis (HCA), and K-means clustering) and supervised (partial least squares-discriminant analysis (PLS-DA), orthogonal partial least squares-discriminant analysis (OPLS-DA), support vector machines (SVM), and deep neural network (DNN) with Keras) chemometric techniques were employed for classifying the 71 MA seizures. The results of the PCA, HCA, K-means clustering, PLS-DA, OPLS-DA, SVM, and DNN methods for quality evaluation were in good agreement. However, the tested MA seizures possessed distinct features, such as chirality, cutting agents, and boiling points. The study indicated that the established qualitative and semi-quantitative methods will be practical and useful analytical tools for characterizing trace compounds in illicit MA seizures. Moreover, they will provide a statistical basis for identifying the synthesis route, sources of supply, trafficking routes, and connections between seizures, which will support drug law enforcement agencies in their effort to eliminate organized MA crime.

• Journal of the Korean Wood Science and Technology
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• v.48 no.5
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• pp.591-607
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• 2020

This study was aimed to investigate the changes of chemical composition of the volatile organic compounds (VOCs) emitted from red pine needles in the process of needle abscission or senescence. The VOCs in intact, senescent, and litter red pine needle samples were analyzed by headspace-solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS). And then, multivariate statistical interpretation of the processed data sets was conducted to investigate similarities and dissimilarities of the needle samples. Principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to investigate the dataset structure and discrimination between samples, respectively. From the data preview, the levels of major components of VOCs from needles were not significantly different between needle samples. By PCA investigation, the data reduction according to classification based on the chlorophyll a / chlorophyll b (Ca/Cb) ratio were found to be ideal for differentiating intact, senescent, and litter needles. The following OPLS-DA taking Ca/Cb ratio as y-variables showed that needle samples were well grouped on score plot and had the significant discriminant compounds, respectively. Several compounds had significantly correlated with Ca/Cb ratio in a bivariate correlation analysis. Notably, the litter needles had a higher content of oxidized compounds than the intact needles. In summary, we found that chemical compositions of VOCs between intact, senescent, and litter needles are different each other and several compounds reflect characteristic of needle.

• Korean Journal of Agricultural Science
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• v.45 no.3
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• pp.447-454
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• 2018

Zearalenone (ZEN), a mycotoxin produced by Fusarium in food and feed, causes serious damage to the health of humans and livestock. Therefore, we compared the metabolomic profiles in the liver, serum, and urine of piglets fed a ZEN-contaminated diet using proton nuclear magnetic resonance ($^1H-NMR$) spectroscopy. The spectra from the three different samples, treated with ZEN concentrations of 0.8 mg/kg for 4 weeks, were aligned and identified using MATLAB. The aligned data were subjected to discriminating analysis using multivariate statistical analysis and a web server for metabolite set enrichment analysis. The ZEN-exposed groups were almost separated in the three different samples. Metabolic analysis showed that 28, 29, and 20 metabolites were profiled in the liver, serum, and urine, respectively. The discriminating analysis showed that the alanine, arginine, choline, and glucose concentrations were increased in the liver. Phenylalanine and tyrosine metabolites showed high concentrations in serum, whereas valine showed a low concentration. In addition, the formate levels were increased in the ZEN-treated urine. For the integrated analysis, glucose, lactate, taurine, glycine, alanine, glutamate, glutamine, and creatine from orthogonal partial least squares discriminant analysis (OPLS-DA) were potential compounds for the discriminating analysis. In conclusion, our findings suggest that potential biomarker compounds can provide a better understanding on how ZEN contaminated feed in swine affects the liver, serum, and urine.

• Biomolecules & Therapeutics
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• v.21 no.6
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• pp.470-475
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• 2013

Ultraviolet (UV) radiation is a major environmental factor that leads to acute and chronic reactions in the human skin. UV exposure induces wrinkle formation, DNA damage, and generation of reactive oxygen species (ROS). Most mechanistic studies of skin physiology and pharmacology related with UV-irradiated skin have focused on proteins and their related gene expression or single-targeted small molecules. The present study identified and analyzed the alteration of skin metabolites following UVB irradiation and topical retinyl palmitate (RP, 5%) treatment in hairless mice using direct analysis in real time (DART) time-of-flight mass spectrometry (TOF-MS) with multivariate analysis. Under the negative ion mode, the DART ion source successfully ionized various fatty acids including palmitoleic and linolenic acid. From DART-TOF-MS fingerprints measured in positive mode, the prominent dehydrated ion peak (m/z: 369, M+H-$H_2O$) of cholesterol was characterized in all three groups. In positive mode, the discrimination among three groups was much clearer than that in negative mode by using multivariate analysis of orthogonal partial-least squares-discriminant analysis (OPLS-DA). DART-TOF-MS can ionize various small organic molecules in living tissues and is an efficient alternative analytical tool for acquiring full chemical fingerprints from living tissues without requiring sample preparation. DART-MS measurement of skin tissue with multivariate analysis proved to be a powerful method to discriminate between experimental groups and to find biomarkers for various experiment models in skin dermatological research.

• Korean Journal of Agricultural Science
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• v.48 no.4
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• pp.875-890
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• 2021

In order to confirm the safety of a genetically modified organism (GMO), we assess its potential toxicity on non-target insects and spiders. In this study, the effects of GM soybean, a type of vitamin-A-enhanced transgenic soybean with tolerance to the herbicide glufosinate, were assessed under a field condition. The study compared this vitamin-A-enhanced transgenic soybean and a non-GM soybean (Gwangan) in a living modified organism (LMO) isolated field of Kyungpook National University (Gunwi) and the National Institute Agricultural Sciences (Jeonju) in the Republic of Korea in 2019 - 2020. In total, 207,760 individual insects and arachnids, representing 81 families and 13 orders, were collected during the study. From the two types of soybean fields, corresponding totals of 105,765 and 101,995 individuals from the vitamin-A-enhanced transgenic soybean and Gwangan samples areas were collected. An analysis of variance indicated no significant differences (p < 0.05). A multivariate analysis showed that the dominance and richness outcomes of plant-dwelling insects were similar. The data on insect species population densities were subjected to a principal component analysis (PCA) and an orthogonal partial least squares-discriminant analysis (OPLS-DA), which did not distinguish between the two varieties, i.e., the vitamin-A-enhanced transgenic soybean and the non-GM soybean in any cultivated field. However, the results of the PCA analysis could be divided overall into four groups based on the yearly survey areas. Therefore, there was no evidence for the different impact of vitamin A-enhanced transgenic soybean on the above-ground insects and spiders compared to non-GM soybean.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.1
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• pp.79-90
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• 2020

Objective: In the present study, an liquid chromatography/mass spectrometry (LC/MS) metabolomics approach was performed to investigate potential biomarkers of milk production in high- and low-milk-yield dairy cows and to establish correlations among rumen fluid metabolites. Methods: Sixteen lactating dairy cows with similar parity and days in milk were divided into high-yield (HY) and low-yield (LY) groups based on milk yield. On day 21, rumen fluid metabolites were quantified applying LC/MS. Results: The principal component analysis and orthogonal correction partial least squares discriminant analysis showed significantly separated clusters of the ruminal metabolite profiles of HY and LY groups. Compared with HY group, a total of 24 ruminal metabolites were significantly greater in LY group, such as 3-hydroxyanthranilic acid, carboxylic acids, carboxylic acid derivatives (L-isoleucine, L-valine, L-tyrosine, etc.), diazines (uracil, thymine, cytosine), and palmitic acid, while the concentrations of 30 metabolites were dramatically decreased in LY group compared to HY group, included gentisic acid, caprylic acid, and myristic acid. The metabolite enrichment analysis indicated that protein digestion and absorption, ABC transporters and unsaturated fatty acid biosynthesis were significantly different between the two groups. Correlation analysis between the ruminal microbiome and metabolites revealed that certain typical metabolites were exceedingly associated with definite ruminal bacteria; Firmicutes, Actinobacteria, and Synergistetes phyla were highly correlated with most metabolites. Conclusion: These findings revealed that the ruminal metabolite profiles were significantly different between HY and LY groups, and these results may provide novel insights to evaluate biomarkers for a better feed digestion and may reveal the potential mechanism underlying the difference in milk yield in dairy cows.