• Proceedings of the Korean Society of Crop Science Conference
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• 2017.06a
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• pp.263-263
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• 2017

Rice (Oryza sativa L.) is the world's third largest food crop after wheat and corn. Geographic authentication of rice has recently emerged as an important issue for enhancing human health via food safety and quality assurance. Here, we aimed to discriminate rice from six Asian countries through geographic authentication using combinations of elemental/isotopic composition analysis and chemometric techniques. Principal components analysis could distinguish samples cultivated from most countries, except for those cultivated in the Philippines and Japan. Furthermore, orthogonal projection to latent structure-discriminant analysis provided clear discrimination between rice cultivated in Korea and other countries. The major common variables responsible for differentiation in these models were ${\delta}^{34}S$, Mn, and Mg. Our findings contribute to understanding the variations in elemental and isotopic compositions in rice depending on geographic origins, and offer valuable insight into the control of fraudulent labeling regarding the geographic origins of rice traded among Asian countries.

• Journal of Ginseng Research
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• v.42 no.4
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• pp.485-495
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• 2018

Background: The geographical origin of Panax ginseng Meyer, a valuable medicinal plant, is important to both ginseng producers and consumers in the context of economic profit and human health benefits. We, therefore, aimed to discriminate between the cultivation regions of ginseng using the stable isotope ratios of C, N, O, and S, which are abundant bioelements in living organisms. Methods: Six Korean ginseng cultivars (3-yr-old roots) were collected from five different regions in Korea. The C, N, O, and S stable isotope ratios in ginseng roots were measured by isotope ratio mass spectrometry, and then these isotope ratio profiles were statistically analyzed using chemometrics. Results: The various isotope ratios found in P. ginseng roots were significantly influenced by region, cultivar, and the interactions between these two factors ($p{\leq}0.001$). The variation in ${\delta}^{15}N$ and ${\delta}^{13}C$ in ginseng roots was significant for discriminating between different ginseng cultivation regions, and ${\delta}^{18}O$ and ${\delta}^{34}S$ were also affected by both altitude and proximity to coastal areas. Chemometric model results tested in this study provided discrimination between the majority of different cultivation regions. Based on the external validation, this chemometric model also showed good model performance ($R^2=0.853$ and $Q^2=0.738$). Conclusion: Our case study elucidates the variation of C, N, O, and S stable isotope ratios in ginseng root depending on cultivation region. Hence, the analysis of stable isotope ratios is a suitable tool for discrimination between the regional origins of ginseng samples from Korea, with potential application to other countries.

• Archives of Pharmacal Research
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• v.15 no.4
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• pp.376-378
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• 1992

A chemical tool was developed for the analysis of complex mixtures such as crude drugs by the method of pattern recognition. Pattern recognition was accomplished by a multiple reference peak identification method and three kinds of outlier statistics. This tool was tested on the analysis of synthetic mixtures.

• Clean Technology
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• v.18 no.3
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• pp.265-271
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• 2012

The use of renewable energies has been required to diminish the dependency on fossil fuels. As one of clean energy sources biomass has been extensively studied because various biomass resources necessitated rapid characterization of their chemical and physical properties in an on-line or real-time basis. For such an analysis near-infrared (NIR) spectroscopy has been successfully applied because of its non-invasive and informative characteristics. In this work, the applicability of nonlinear chemometric techniques based on biomass near infrared (NIR) data is evaluated for the rapid prediction of ash/char contents in different types of biomass. The prediction results of various prediction models and the effect of using preprocessing methods for NIR data are compared using six types of biomass NIR data. The results showed that nonlinear prediction models yielded better prediction performance than linear ones. It also turned out that by adopting the use of proper preprocessing methods the performance of prediction of biomass properties improved.

• Korean Journal of Agricultural Science
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• v.47 no.3
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• pp.633-644
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• 2020

Rice paper is an element of Vietnamese cuisine that can be used to wrap vegetables and meat. Rice and starch are the main ingredients of rice paper and their mixing ratio is important for quality control. In a commercial factory, assessment of food safety and quantitative supply is a challenging issue. A rapid and non-destructive monitoring system is therefore necessary in commercial production systems to ensure the food safety of rice and starch flour for the rice paper wrap. In this study, fluorescence hyperspectral imaging technology was applied to classify grain flours. Using the 3D hyper cube of fluorescence hyperspectral imaging (fHSI, 420 - 730 nm), spectral and spatial data and chemometric methods were applied to detect and classify flours. Eight flours (rice: 4, starch: 4) were prepared and hyperspectral images were acquired in a 5 (L) × 5 (W) × 1.5 (H) cm container. Linear discriminant analysis (LDA), partial least square discriminant analysis (PLSDA), support vector machine (SVM), classification and regression tree (CART), and random forest (RF) with a few preprocessing methods (multivariate scatter correction [MSC], 1^st and 2^nd derivative and moving average) were applied to classify grain flours and the accuracy was compared using a confusion matrix (accuracy and kappa coefficient). LDA with moving average showed the highest accuracy at A = 0.9362 (K = 0.9270). 1D convolutional neural network (CNN) demonstrated a classification result of A = 0.94 and showed improved classification results between mimyeon flour (MF)1 and MF2 of 0.72 and 0.87, respectively. In this study, the potential of non-destructive detection and classification of grain flours using fHSI technology and machine learning methods was demonstrated.

• Korean Journal of Food Science and Technology
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• v.38 no.2
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• pp.165-168
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• 2006

Fourier transform-near infrared spectroscopy (FT-NIRS) was evaluated for determination of total dietary fiber (TDF) content of brown rice. Enzymatic-gravimetric method was suitable to obtain reference values for calibration of NIR at 1,000-2,500 nm range. Standard error of laboratory procedure ranged 0.17 to 0.72%. Partial least square (PLS) regression was used to develop the calibration equations. Regression was performed automatically using NIRCal chemometric software. Accuracy of prediction model for TDF content was certified for regression coefficient (r), standard error of estimation (SEE) and standard error of prediction (SEP), showing 0.9780, 0.0636, and 0.0642, respectively. This prediction model can be used for determination of TDF in brown rice and would be useful for real-time analysis in food industry.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1041-1041
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• 2001

Removal of variability in spectra data before the application of chemometric modeling will generally result in simpler (and presumably more robust) models. Particularly for sparsely sampled data, such as typically encountered in diode array instruments, the use of Savitzky-Golay (S-G) derivatives offers an effective method to remove effects of shifting baselines and sloping or curving apparent baselines often observed with scattering samples. The application of these convolution functions is equivalent to fitting a selected polynomial to a number of points in the spectrum, usually 5 to 25 points. The value of the polynomial evaluated at its mid-point, or its derivative, is taken as the (smoothed) spectrum or its derivative at the mid-point of the wavelength window. The process is continued for successive windows along the spectrum. The original paper, published in 1964 [1] presented these convolution functions as integers to be used as multipliers for the spectral values at equal intervals in the window, with a normalization integer to divide the sum of the products, to determine the result for each point. Steinier et al. [2] published corrections to errors in the original presentation [1], and a vector formulation for obtaining the coefficients. The actual selection of the degree of polynomial and number of points in the window determines whether closely situated bands and shoulders are resolved in the derivatives. Furthermore, the actual noise reduction in the derivatives may be estimated from the square root of the sums of the coefficients, divided by the NORM value. A simple technique to evaluate the actual convolution factors employed in the calculation by the software will be presented. It has been found that some software packages do not properly account for the sampling interval of the spectral data (Equation Ⅶ in [1]). While this is not a problem in the construction and implementation of chemometric models, it may be noticed in comparing models at differing spectral resolutions. Also, the effects on parameters of PLS models of choosing various polynomials and numbers of points in the window will be presented.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1127-1127
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• 2001

The transfer of predictive models using various chemometric techniques has been reported for FTNIR and scanning-grating based NIR instruments with respect relatively dry samples (<10% water). Some of the currently used transfer techniques include slope and bias correction (SBC), direct standardization (DS), piecewise direct standardization (PDS), orthogonal signal correction (OSC), finite impulse transform (FIR) and wavelet transform (WT) and application of neural networks. In a previous study (Greensill et at., 2001) on calibration transfer for wet samples (intact melons) across silicon diode array instrumentation, we reported on the performance of various techniques (SBC, DS, PDS, double window PDS (DWPDS), OSC, FIR, WT, a simple photometric response correction and wavelength interpolative method and a model updating method) in terms of RMSEP and Fearns criterion for comparison of RMSEP. In the current study, we compare these melon transfer results to a similar study employing pairs of spectrometers for non-invasive prediction of soluble solid content of peaches.

• Journal of the Korean Magnetic Resonance Society
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• v.18 no.1
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• pp.10-14
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• 2014

Hsp90 is a good drug target molecule that is involved in regulating various signaling pathway in normal cell and the role of Hsp90 is highly emphasized especially in cancer cells. Thus, much efforts for discovery and development of Hsp90 inhibitor have been continued and a few Hsp90 inhibitors targeting the N-terminal ATP binding site are being tested in the clinical trials. There are no metabolic signature molecules that can be used to evaluate the effect of Hsp90 inhibition. We previously found a potential C-domain binder named PPC1 that is a synthetic small molecule. Here we report the metabolomics study to find signature metabolites upon treatment of PPC1 compound in lung cancer cell line, A549 and discuss the potentiality of metabolomic approach for evaluation of hit compounds.

• Journal of Integrative Natural Science
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• v.5 no.1
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• pp.1-5
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• 2012

Quantitative structure-activity relationship (QSAR) methodologies have been applied for many years, to correlate the relationship between physicochemical properties of chemical substances and their biological activities to generate a statistical model for prediction of the activities of new chemical entities. The basic principle behind the QSAR models is that, how structural variation is responsible for the difference in biological activities of the compounds. 3D-QSAR has emerged as a natural extension to the classical Hansch and Free-Wilson approaches, which develops the 3D properties of the ligands to predict their biological activities using various chemometric techniques (PLS, G/PLS, ANN etc). It has served as a valuable predictive tool in the design of pharmaceuticals and agrochemicals. This review seeks to provide different 3D-QSAR approaches involved in drug designing process to develop structure-activity relationships and also discussed the fundamental limitations, as well as those that might be overcome with the improved methodologies.