• Journal of Applied Biological Chemistry
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• v.65 no.4
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• pp.439-446
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• 2022

Soybean is a crop with high-quality of protein and oil, and it is one of the most widely used genetically modified (GM) crops in the world today. In South Korea, Kwangan is the most utilized variety as a parental line for GM soybean development. In this study, untargeted LC-MS metabolomic approaches were used to compare metabolite profiles of Kwangan and three other commercial varieties cultivated in Gunwi and Jeonju in 2020 year. Metabolomic studies revealed that the 4 soybean varieties were distinct based on the partial least squares-discriminant analysis (PLS-DA) score plots; 18 metabolites contributed to variety distinction, including phenylalanine, isoflavones, and fatty acids. All varieties were clearly differentiated by location on the PLS-DA score plot, indicating that the growing environment is also attributable to metabolite variability. In particular, isoflavones and linolenic acid levels in Kwangan were significantly lower and higher, respectively compared to those of the three varieties. It was discussed that it might need to include more diverse conventional varieties as comparators in regard to metabolic characteristics of Kwangan for the assessment of substantial equivalence biogenetically engineered soybeans in a Kwangan-variety background.

• 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.

• Food Science of Animal Resources
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• v.34 no.5
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• pp.700-708
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• 2014

The volatile composition of veal has yet to be reported and is one of the important factors determining meat character and quality. To identify the most important aroma compounds in veal from Holstein bull calves fed one of three diets, samples were subjected to solid-phase microextraction (SPME) combined with gas chromatography-quadrupole mass spectrometry (GC-MS). Most of the important odorants were aldehydes and alcohols. For group A (veal calves fed entirely on milk for 90 d before slaughter), the most abundant compound class was the aldehydes (52.231%), while that was alcohols (26.260%) in group C (veal calves fed starter diet for at least 60 d before slaughter). In both classes the absolute percentages of the volatile compounds in veal were different indicating that the veal diet significantly (p<0.05) affected headspace volatile composition in veal as determined by principal component analysis (PCA). Twenty three volatile compounds showed significance by using a partial least-squared discriminate analysis (PLS-DA) (VIP>1). The establishment of the global volatile signature of veal may be a useful tool to define the beef diet that improves the organoleptic characteristics of the meat and consequently impacts both its taste and economic value.

• Korean Journal of Agricultural Science
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• v.39 no.1
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• pp.117-123
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• 2012

Pathogenic fungi and bacteria such as Pectobacterium atrosepticum, Clavibacter michiganensis subsp. sepedonicus, Verticillium albo-atrum, and Rhizoctonia solani were the major microorganism which causes diseases in seed potato during postharvest process. Current detection method for disease-infected seed potato relies on human inspection, which is subjective, inaccurate and labor-intensive method. In this study, a reflectance spectroscopy was used to classify sound and disease-infected seed potatoes with the spectral range from 400 to 1100 nm. Partial least square discriminant analysis (PLS-DA) with various preprocessing methods was used to investigate the feasibility of classification between sound and disease-infected seed potatoes. The classification accuracy was above 97 % for discriminating disease seed potatoes from sound ones. The results show that Vis/NIR reflectance method has good potential for non-destructive sorting for disease-infected seed potatoes.

• Journal of Ginseng Research
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• v.37 no.3
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• pp.371-378
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• 2013

Serum and liver metabolites in rats fed red ginseng (RG) were analyzed by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry. The mass data were analyzed by partial least squares-discriminant analysis (PLS-DA) to discriminate between control and RG groups and identify metabolites contributing to this discrimination. The RG group was clearly separated from the control group on PLS-DA scores plot for serum samples, but not liver samples. The major metabolites contributing to the discrimination included lipid metabolites (lysophosphatidylcholine, acyl-carnitine, and sphingosine), isoleucine, nicotinamide, and corticosterone in the serum; the blood levels of all but isoleucine were reduced by RG administration. Not all metabolites were positively correlated with the health benefits of RG. However, the blood levels of lysophosphatidylcholine, which stimulate various diseases, and long-chain acylcarnitines and corticosterone, which activate the stress response, were reduced by RG, suggesting long-term RG might relieve stress and prevent physiological and biological problems.

• Mycobiology
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• v.42 no.4
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• pp.353-360
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• 2014

Makgeolli is a traditional Korean alcoholic beverage. The flavor of makgeolli is primarily determined by metabolic products such as free sugars, amino acids, organic acids, and aromatic compounds, which are produced during the fermentation of raw materials by molds and yeasts present in nuruk, a Korean fermentation starter. In this study, makgeolli was brewed using the wild yeast strain Saccharomyces cerevisiae Y98-5, and temporal changes in the metabolites during fermentation were analyzed by ultra-high-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry. The resultant data were analyzed by partial least squares-discriminant analysis (PLS-DA). Various metabolites, including amino acids, organic acids, sugar alcohols, small peptides, and nucleosides, were obviously altered by increasing the fermentation period. Changes in these metabolites allowed us to distinguish among makgeolli samples with different fermentation periods (1, 2, 3, 6, 7, and 8 days) on a PLS-DA score plot. In the makgeolli brewed in this study, the amounts of tyrosine ($463.13{\mu}g/mL$) and leucine ($362.77{\mu}g/mL$) were high. Therefore, our results indicate that monitoring the changes in metabolites during makgeolli fermentation might be important for brewing makgeolli with good nutritional quality.

• Korean Journal of Optics and Photonics
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• v.31 no.3
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• pp.125-133
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• 2020

An innovative method for separating overlapping latent fingerprints, using laser-induced plasma spectroscopy (LIPS) combined with multivariate analysis, is reported in the current study. LIPS provides the capabilities of real-time analysis and high-speed scanning, as well as data regarding the chemical components of overlapping fingerprints. These spectra provide valuable chemical information for the forensic classification and reconstruction of overlapping latent fingerprints, by applying appropriate multivariate analysis. This study utilizes principal-component analysis (PCA) and partial-least-squares (PLS) techniques for the basis classification of four types of fingerprints from the LIPS spectra. The proposed method is successfully demonstrated through a classification example of four distinct latent fingerprints, using discrimination such as soft independent modeling of class analogy (SIMCA) and partial-least-squares discriminant analysis (PLS-DA). This demonstration develops an accuracy of more than 85% and is proven to be sufficiently robust. In addition, by laser-scanning analysis at a spatial interval of 125 ㎛, the overlapping fingerprints were separated as two-dimensional forms.

• Journal of the Korean Society of International Agriculture
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• v.31 no.4
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• pp.378-383
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• 2019

To determine whether FT-IR spectral analysis based on multivariate analysis for whole cell extracts can be used to discriminate papaya at metabolic level. FT-IR spectral data from leaves were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). FT-IR spectra confirmed typical spectral differences between the frequency regions of 1,700-1,500, 1,500-1,300 and 1,100-950 cm^-1, respectively. These spectral regions were reflecting the quantitative and qualitative variations of amide I, II from amino acids and proteins (1,700-1,500 cm^-1), phosphodiester groups from nucleic acid and phospholipid (1,500-1,300 cm^-1) and carbohydrate compounds (1,100-950 cm^-1). The result of PCA analysis showed that papaya leaves could be separated into clusters depending on different growth temperature. In this case, showed discrimination confirmed according to metabolite content of growth condition from papaya. And PLS-DA analysis also showed more clear discrimination pattern than PCA result. Furthermore, these metabolic discrimination systems could be applied for rapid selection and classification of useful papaya cultivars.

• Journal of Microbiology and Biotechnology
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• v.28 no.12
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• pp.1971-1981
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• 2018

In this study, we investigated the altered enzymatic activities and metabolite profiles of koji fermented using varying permutations of Aspergillus oryzae and/or Bacillus amyloliquefaciens. Notably, the protease and ${\beta}$-glucosidase activities were manifold increased in co-inoculated (CO) koji samples (co-inoculation of A. oryzae and B. amyloliquefaciens). Furthermore, gas chromatography-mass spectrometry (GC-MS)-based metabolite profiling indicates that levels of amino acids, organic acids, sugars, sugar alcohols, fatty acids, nucleosides, and vitamins were distinctly higher in CO, SA (sequential inoculation of A. oryzae, followed by B. amyloliquefaciens), and SB (sequential inoculation of B. amyloliquefaciens, followed by A. oryzae). The multivariate principal component analysis (PCA) plot based on GC-MS datasets indicated a clustered pattern for MA and MB (koji samples inoculated either with A. oryzae or B. amyloliquefaciens) across PC2 (20.0%). In contrast, the CO, SA, and SB metabolite profiles displayed segregated patterns across PLS1 (22.2%) and PLS2 (21.1%) in the partial least-square discriminant analysis (PLS-DA) model. Intriguingly, the observed disparity in the levels of primary metabolites was engendered largely by higher relative levels of sugars and sugar alcohols in MA, SA, and CO koji samples, which was commensurate with the relative amylase activities in respective samples. Collectively, the present study emphasizes the utility of integrated biochemical and metabolomic approaches for achieving the optimal permutation of fermentative inocula for industrial koji preparation.

• Journal of the Korean Society for Nondestructive Testing
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• v.34 no.1
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• pp.31-37
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• 2014

The aim of this study was the non-destructive evaluation of bloody eggs using VIS/NIR spectroscopy. The bloody egg samples used to develop the sorting mode were produced by injecting chicken blood into the edges of egg yolks. Blood amounts of 0.1, 0.7, 0.04, and 0.01 mL were used for the bloody egg samples. The wavelength range for the VIS/NIR spectroscopy was 471 to 1154 nm, and the spectral resolution was 1.5nm. For the measurement system, the position of the light source was set to $30^{\circ}$, and the distance between the light source and samples was set to 100 mm. The minimum exposure time of the light source was set to 30 ms to ensure the fast sorting of bloody eggs and prevent heating damage of the egg samples. Partial least squares-discriminant analysis (PLS-DA) was used for the spectral data obtained from VIS/NIR spectroscopy. The classification accuracies of the sorting models developed with blood samples of 0.1, 0.07, 0.04, and 0.01 mL were 97.9%, 98.9%, 94.8%, and 86.45%, respectively. In this study, a novel nondestructive sorting technique was developed to detect bloody brown eggs using spectral data obtained from VIS/NIR spectroscopy.