• Bulletin of the Korean Chemical Society
• /
• v.28 no.4
• /
• pp.557-560
• /
• 2007

The metabolic discrimination of safflowers from various geographical origins was performed using 1H nuclear magnetic resonance (NMR) spectroscopy followed by principal components analysis. With a combination of these techniques, safflower samples from different origins could be discriminated using the first two principal components (PC) of the 1H NMR spectra of the 50% methanol fractions. PC1 and PC2 accounted cumulatively for 91.3% of the variation in all variables. The major peaks in the 1H NMR spectra that contributed to the discrimination were assigned to fatty acid (terminal CH3), lactic acid, acetic acid, choline derivatives, glycine, and safflower yellow derivatives. In this study, we suggest that various types of safflower can be discriminated using PCA and 1H NMR spectra.

• Journal of Plant Biotechnology
• /
• v.33 no.4
• /
• pp.283-288
• /
• 2006

Discrimination of 5 rice cultivars (Sangjubyeo , Dongjinbyeo Simbaekbyeo , Hwamanbyeo , and Simbaek-hetero ) using metabolic profiling was carried out. Whole cell extracts from each cultivar were subjected to $^1H$ NMR spectroscopy. When spectral data were analyzed by principal component analysis, 5 cultivars were clustered into 3 groups: SJ, DJ + SB, and HM + SH. Thecultivars showed great difference in carbohydrate region of $^1H$ NMR spectra, suggesting that qualitative and quantitative differences in carbohydrate compounds play a major role in discrimination of the cultivars. In addition, it was readily possible to determine relative quantification of major carbohydrates including sucrose, glucose, maltose from spectral data of the cultivars. SJ showed 2 to 4 times higher content of maltose than the other rice cultivars. Overall results indicate that metabolic discrimination of rice cultivars using $^1H$ NMR spectroscopy combined by multivariate statistical analysis can be used for rapid discrimination of numerous rice cultivars and simple quantitative analysis system of major carbohydrate compounds in rice grains.

• Archives of Obesity and Metabolism
• /
• v.2 no.1
• /
• pp.1-5
• /
• 2023

The importance of weight discrimination for people with obesity has been highlighted by research which has found that more than 40% of those living with obesity have experienced weight discrimination. Evidence suggests that weight bias among obese individuals puts their health at risk more than health issues caused by obesity itself. Although bias, stigma, and discrimination towards individuals living with obesity are factors that make it difficult for them to lose weight, weight bias and stigma among healthcare professionals are common, causing individuals living with obesity to avoid treatment and potentially exacerbating obesity-related health issues. The concept that one's own efforts matter contributes to stigma, discrimination, and bias. This issue will be more frequent among primary care providers treating individuals living with obesity; thus, it is important to acknowledge the issues of bias, stigma, and discrimination towards individuals living with obesity and to seek out solutions. In this review, I will discuss the concept of weight bias, stigma, and discrimination, the problems they cause, and seek solutions to weight prejudice, stigma, and discrimination.

• Journal of muscle and joint health
• /
• v.21 no.1
• /
• pp.46-54
• /
• 2014

Purpose: This study was designed to explore the stage distribution of subjects according to stages of change for exercise and to identify factors that could discriminate subjects in various stages. Methods: The sample consisted of 182 subjects who had metabolic syndrome. The instruments used in this study were the stage placement instrument for exercise, the metabolic syndrome knowledge and metabolic syndrome health belief scale, and the metabolic syndrome exercise self efficacy scale. Data were analyzed using chi-square, ANOVA, and discriminant analysis by using the SPSS 19.0 program. Results: For exercise stages, exercise efficacy, occupation, and exercise benefit showed high standardized canonical discriminant function coefficients. Subjects in precontemplation/contemplation stage for exercise were more likely to have occupations, had less exercise efficacy and exercise benefit than those in other stage. Conclusion: This study implies that the level of exercise efficacy and exercise benefit of subjects in precontemplation/contemplation stage need to be enhanced in developing exercise program incorporating the stage of change for metabolic syndrome patients.

• Journal of Plant Biotechnology
• /
• v.43 no.3
• /
• pp.383-390
• /
• 2016

To determine whether FT-IR spectral analysis based on multivariate analysis could be used to discriminate Chinese cabbage breeding line at metabolic level, whole cell extracts of nine different breeding lines (three paternal, three maternal and three $F_1$ lines) were subjected to Fourier transform infrared spectroscopy (FT-IR). FT-IR spectral data of Chinese cabbage plants were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA), and hierarchical clustering analysis (HCA). The hierarchical dendrograms based on PLS-DA from two of three cross combinations showed that paternal, maternal, and their progeny $F_1$ lines samples were perfectly separated into three branches in breeding line dependent manner. However, a cross combination failed to fully discriminate them into three branches. Thus, hierarchical dendrograms based on PLS-DA of FT-IR spectral data of Chinese cabbage breeding lines could be used to represent the most probable chemotaxonomical relationship among maternal, paternal, and $F_1$ plants. Furthermore, these metabolic discrimination systems could be applied for rapid selection and classification of useful Chinese cabbage cultivars.

• Journal of Plant Biotechnology
• /
• v.39 no.3
• /
• pp.121-126
• /
• 2012

To determine whether FT-IR spectroscopy combined with multivariate analysis for whole cell extracts can be used to discriminate major leguminous plant at metabolic level, seed extracts of six leguminous plants were subjected to Fourier transform infrared spectroscopy (FT-IR). FT-IR spectral data from seed extracts were analyzed by principal component analysis (PCA), partial least square discriminant analysis (PLS-DA) and hierarchical clustering analysis (HCA). The PCA could not fully discriminate six leguminous plants, however PLS-DA could successfully discriminate six leguminous plants. The hierarchical dendrogram based on PLS-DA separated the six leguminous plants into four branches. The first branch was consisted of all three Vigna species including Vigna radiata var. radiate, Vigna angularis var. angularis and Vigna unguiculata subsp. Unguiculata. Whereas Pisum sativum var. sativum, Glycine max L and Phaseolus vulgaris var. vulgaris were clustered into a separate branch respectively. The overall results showed that metabolic discrimination system were in accordance with known phylogenic taxonomy. Thus we suggested that the hierarchical dendrogram based on PLS-DA of FT-IR spectral data from seed extracts represented the most probable chemotaxonomical relationship between six leguminous plants.

• Journal of the Korean Magnetic Resonance Society
• /
• v.16 no.2
• /
• pp.91-102
• /
• 2012

Metabolomics is the systematic identification and quantification of all metabolites in an organism or biological sample. NMR has been used as a major application tool in plant metabolomics such as quality control, chemotaxonomy, and analysis of genetically modified plants. Herbal medicines are the important therapeutics and are used to manage common diseases such as cold, inflammation, pain, heart diseases, liver cirrhosis, diabetes and central nerve system diseases. Herb plants include various kinds of species such as geranium, mint, and thyme and so on and contain different kinds of metabolites. We performed NMR-based metabolomics study on the seven different species of herb plants using $^1H$ NMR experiments and OPLS-DA to understand the correlation between the classification of herb plants and their metabolite contents. This study showed clear metabolic discrimination among various herb plants. This metabolmics study found several diagnostic NMR signals which are able to be used as bio-markers for identification of the specific herb plants among various species. Clear metabolic discrimination of herb plants suggests three chemotaxonomic groups of herb species.

• Proceedings of the Korean Society of Crop Science Conference
• /
• 2017.06a
• /
• pp.175-175
• /
• 2017

To understand molecular mechanisms underlying adaptation of plant cells to saline stress and stress memory, we developed Arabidopsis callus suspension-cultured cells adapted to high salt. Adapted cells to high salt exhibited enhanced tolerance compared to control cells. Moreover, the salt tolerance of adapted cells was stably maintained even after the stress is relieved, indicating that the acquired salt tolerance of adapted cells was memorized. In order to characterize metabolic responses of plant cells during adaptation to high salt stress as well as stress memory, we compared metabolic profiles of salt-adapted and stress-memorized cells with control cells by using NMR spectroscopy. A principle component analysis showed clear metabolic discrimination among control, salt-adapted and stress-memorized cells. Compared with control cells, metabolites related to shikimate metabolism such as tyrosine, and flavonol glycosides, which are related to protective mechanism of plant against stresses were largely up-regulated in adapted cell lines. Moreover, coniferin, a precursor of lignin, was more abundant in salt-adapted cells than control cells. Cell morphology analysis using transmission electron microscopy indicated that cell wall thickness of salt-adapted cells was significantly induced compared to control cells. Consistently, salt adapted cells contained more lignin in their cell walls compared to control cells. The results provide new insight into mechanisms of plant adaptation to saline stress as well as stress memory in metabolic level.

• Horticultural Science & Technology
• /
• v.34 no.2
• /
• pp.324-330
• /
• 2016

To determine whether FT-IR spectral analysis based on multivariate analysis for whole cell extracts can be used to discriminate between artichoke (Cynara cardunculus var. scolymus L.) plants at the metabolic level, leaves of ten artichoke plants were subjected to Fourier transform infrared(FT-IR) spectroscopy. 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 $1,100-950cm^{-1}$, respectively. These spectral regions reflect the quantitative and qualitative variations of amide I, II from amino acids and proteins ($1,700-1,500cm^{-1}$), phosphodiester groups from nucleic acid and phospholipid ($1,500-1,300cm^{-1}$) and carbohydrate compounds ($1,100-950cm^{-1}$). PCA revealed separate clusters that corresponded to their species relationship. Thus, PCA could be used to distinguish between artichoke species with different metabolite contents. PLS-DA showed similar species classification of artichoke. Furthermore these metabolic discrimination systems could be used for the rapid selection and classification of useful artichoke cultivars.

• Journal of Ginseng Research
• /
• v.38 no.1
• /
• pp.59-65
• /
• 2014

Discriminating between two herbal medicines (Panax ginseng and Panax quinquefolius), with similar chemical and physical properties but different therapeutic effects, is a very serious and difficult problem. Differentiation between two processed ginseng genera is even more difficult because the characteristics of their appearance are very similar. An ultraperformance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF MS)-based metabolomic technique was applied for the metabolite profiling of 40 processed P. ginseng and processed P. quinquefolius. Currently known biomarkers such as ginsenoside Rf and F11 have been used for the analysis using the UPLC-photodiode array detector. However, this method was not able to fully discriminate between the two processed ginseng genera. Thus, an optimized UPLC-QTOF-based metabolic profiling method was adapted for the analysis and evaluation of two processed ginseng genera. As a result, all known biomarkers were identified by the proposed metabolomics, and additional potential biomarkers were extracted from the huge amounts of global analysis data. Therefore, it is expected that such metabolomics techniques would be widely applied to the ginseng research field.