• Analytical Science and Technology
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• v.15 no.2
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• pp.102-107
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• 2002

This paper describes a rapid determination of phenobarbital in intact phenobarbital tablets using partial least squares regression(PLSR) method of transmittance spectrum of near infrared (NIR) compared with the analytical data of liquid chromatograpy. The linearity, concentration range and precision of this analytical method are studied. The correlation coefficient of the calibration curve is 0.9983 and the standard error of calibration(SEC) is 0.14 %. Intra-day precision and Inter-day precision obtained in this method are CV = 0.45, CV =0.56, respectively.

• Communications for Statistical Applications and Methods
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• v.13 no.3
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• pp.567-576
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• 2006

In this paper, we applied the multiblock dimension reduction methods to the classification of tumor based on microarray gene expressions data. This procedure involves clustering selected genes, multiblock dimension reduction and classification using linear discrimination analysis and quadratic discrimination analysis.

• 한국가스학회:학술대회논문집
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• 2001.10a
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• pp.145-149
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• 2001

• 한국가스학회:학술대회논문집
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• no.5
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• pp.137-142
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• 2002

• The Korean Journal of Applied Statistics
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• v.29 no.6
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• pp.1117-1128
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• 2016

In genetic association studies with high-dimensional genomic data, regularization procedures based on penalized likelihood are often applied to identify genes or genetic regions associated with diseases or traits. A network-based regularization procedure can utilize biological network information (such as genetic pathways and signaling pathways in genetic association studies) with an outstanding selection performance over other regularization procedures such as lasso and elastic-net. However, network-based regularization has a limitation because cannot be applied to high-dimension genomic data with a group structure. In this article, we propose to combine data dimension reduction techniques such as principal component analysis and a partial least square into network-based regularization for the analysis of high-dimensional genomic data with a group structure. The selection performance of the proposed method was evaluated by extensive simulation studies. The proposed method was also applied to real DNA methylation data generated from Illumina Innium HumanMethylation27K BeadChip, where methylation beta values of around 20,000 CpG sites over 12,770 genes were compared between 123 ovarian cancer patients and 152 healthy controls. This analysis was also able to indicate a few cancer-related genes.

• Animal Bioscience
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• v.37 no.5
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• pp.918-928
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• 2024

Objective: The adulteration of raw beef (BMr) with dog meat (DMr) and pork (PMr) becomes a serious problem because it is associated with halal status, quality, and safety of meats. This research aimed to develop an effective authentication method to detect non-halal meats (dog meat and pork) in beef using metabolomics approach. Methods: Liquid chromatography-high resolution mass spectrometry (LC-HRMS) using untargeted approach combined with chemometrics was applied for analysis non-halal meats in BMr. Results: The untargeted metabolomics approach successfully identified various metabolites in BMr DMr, PMr, and their mixtures. The discrimination and classification between authentic BMr and those adulterated with DMr and PMr were successfully determined using partial least square-discriminant analysis (PLS-DA) with high accuracy. All BMr samples containing non-halal meats could be differentiated from authentic BMr. A number of discriminating metabolites with potential as biomarkers to discriminate BMr in the mixtures with DMr and PMr could be identified from the analysis of variable importance for projection value. Partial least square (PLS) and orthogonal PLS (OPLS) regression using discriminating metabolites showed high accuracy (R² >0.990) and high precision (both RMSEC and RMSEE <5%) in predicting the concentration of DMr and PMr present in beef indicating that the discriminating metabolites were good predictors. The developed untargeted LC-HRMS metabolomics and chemometrics successfully identified non-halal meats adulteration (DMr and PMr) in beef with high sensitivity up to 0.1% (w/w). Conclusion: A combination of LC-HRMS untargeted metabolomic and chemometrics promises to be an effective analytical technique for halal authenticity testing of meats. This method could be further standardized and proposed as a method for halal authentication of meats.

• Journal of Pharmaceutical Investigation
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• v.41 no.6
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• pp.377-380
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• 2011

The Artemisia princeps (Compositae) has been used in traditional Korean medicine for the treatment of microbial infections and inflammatory diseases. Since A. princeps is generally difficult to be discriminated from A. capillaris, A. caplillaris has been misused in place of A. princeps. To solve this problem, a rapid and nondestructive method for discrimination of A. princeps and A. capillaris samples was developed using near infrared spectroscopy (NIRS) in the present study. A principal component analysis (PCA) and a partial least squares discrimination analysis (PLS-DA) were performed to discriminate two species. As a result, with the use of PLS-DA, A. princeps and A. capillaris were clustered according to their genus. These outcomes indicated that the NIRS could be useful for the discrimination between Artemisia princeps and Artemisia capillaris.

• Journal of Satellite, Information and Communications
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• v.3 no.1
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• pp.15-21
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• 2008

The high peak to average power ratio (PAPR) of OFDM (Orthogonal Frequency Division Multiplexing) system introduces inevitable non-linear distortion in the transmission due to the amplifier non-linear property. This causes both in-band distortion and out of band spectrum re-growth. In this paper we tried to compensate the problem by using polynomial based pre-distortion. Estimation of both the non-linear and inverse non-linear polynomial is achieved using the Least Square Error (LSE) method. Using these parameters closed form pre-distorter can be easily created. We also used the 'partial peak cancellation and clipping' method to remove the high peak present in the non constant amplitude of the OFDM signal responsible to drive the amplifier in near saturation region for better performance of the system

• Clean Technology
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• v.17 no.3
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• pp.215-224
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• 2011

It is an important issue to develop quality assessing system for biofuel for the purpose of accelerating the mass production of biofuel. It is particularly challenging to conduct testing method in the mass production of bioethanol while meeting quality specifications such as ASTM (American Society for Testing & Materials) D4806-10. In order to address this challenge, this paper proposes on-line spectroscopic quality assesment system based on Near Infrared spectrum and Partial Least Squares method in Chemometrics. As a result of testing a number of preprocessing methods and Partial Least Squares, it was found out that Savitzky-Golay method showed the best performance in terms of spectrum correction, noise reduction, and model maintenance. The proposed system allows us to assess multiple quality components continuously using spectroscopic facilities with the cheap cost. Since the value of R2 is more than 0.99, it is possible to replace the laboratory analysis.

• Korean Journal of Soil Science and Fertilizer
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• v.34 no.4
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• pp.273-277
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• 2001

Traditional methods of chemical analysis for the soil properties take time and produce harmful waste. The purpose of this research was to evaluate an NIR technique for measuring some soil properties that are rapid and accurate in soil fertility assessments. The NIR instrument (InfraAlyzer 500, Bran & Luebbe Co.) was used for obtaining spectral data from 140 finely ground soil for calibrations and validation estimating pH, CEC, extractable Ca, Mg, K, $SiO_2$, humic acid and EC. Partial least square regression analysis was used to develop a calibration of NIR spectroscopy method. The results indicated that NIR spectroscopy could be used as a routine nondestructive method quantitatively determining soil chemical properties quickly. However the NIR technique may require sample preparation to obtain even diffuse reflection spectra from the soil and data manipulations to obtain optimal predictions.