• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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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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• 제42권4호
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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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• 제15권4호
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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.

• 청정기술
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• 제18권3호
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• pp.265-271
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• 2012

화석연료에 대한 의존성을 줄이기 위한 대안으로서 재생에너지 사용이 요구되고 있다. 청정에너지원의 하나인 바이오매스는 그 물성치의 실시간 파악이 중요하기 때문에 다양한 종류의 바이오매스에 대해 널리 연구되어 왔으며, 방법론적인 측면에서는 비침투성이며 많은 정보를 가진 특징으로 인하여 근적외선 분광법이 성공적으로 적용되었다. 본 논문에서는 여러 바이오매스 종류에 대한 물성치의 빠른 예측을 위해 근적외선 데이터에 기반한 비선형 방법론의 적용성을 평가하였다. 다양한 방법론에 기반한 예측 모델들을 근적외선 데이터의 전처리방법과 조합하여 예측 성능을 평가하였다. 바이오매스 물성 예측 모델의 성능에서는 선형 모델보다는 비선형 모델에서 예측오차가 최소화되었으며 전처리 방법과 결합되었을 때 최적의 예측결과를 얻을 수 있었다.

• 농업과학연구
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• 제47권3호
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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.

• 한국식품과학회지
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• 제38권2호
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• pp.165-168
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• 2006

분석이 번거로웠던 현미의 총 식이섬유(TDF) 함량을 신속하면서도 친 환경적인 비파괴 분석방법인 FT-NIRS를 이용하여 예측 모델을 개발하였다. 현미는 국내산으로 전남 지방에서 재배된 47개 품종(516개 시료)에 대해서 AOAC 방법에 준한 효소법에 의해 각 측정 시료별 TDF 함량을 분석하였다. 습식 분석된 TDF 함량의 분석오차범위는 0.17-0.72% 이었다. FT.NIRS로 측정된 스렉트럼의 검량식은 빛의 산란 효과를 최소화하기 위해 수학적 처리를 하였고, 몇 개의 특정 파장이 아닌 전 파장 영역(1,000-2,500nm)에 대해서 PLS법으로 작성하였다 회귀분석과 검량식은 NIRCal chemometric software에 의해 작성되었다. 얻어진 검량식의 정확도는 상관계수(r), SEE 및 SEP로 확인하였다. 현미 중 총 식이섬유 함량에 대한 회귀분석을 행한 결과, 상관계수는 0.9780, SEE는 0.0636, SEP는 0.0642로 측정 정확도가 우수함으로 현장 적용을 위한 실용화도 가능할 것으로 판단된다.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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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.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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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.

• 한국자기공명학회논문지
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• 제18권1호
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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.

• 통합자연과학논문집
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• 제5권1호
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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.