• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1152-1152
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• 2001

Food adulteration is a serious consumer fraud and a matter of concern to food processors and regulatory agencies. A range of analytical methods have been investigated to facilitate the detection of adulterated or mis-labelled foods & food ingredients but most of these require sophisticated equipment, highly-qualified staff and are time-consuming. Regulatory authorities and the food industry require a screening technique which will facilitate fast and relatively inexpensive monitoring of food products with a high level of accuracy. Near infrared spectroscopy has been investigated for its potential in a number of authenticity issues including meat speciation (McElhinney, Downey & Fearn (1999) JNIRS, 7(3), 145 154; Downey, McElhinney & Fearn (2000). Appl. Spectrosc. 54(6), 894-899). This report describes further analysis of these spectral sets using a hierarchical approach and binary decisions solved using logistic regression. The sample set comprised 230 homogenized meat samples i. e. chicken (55), turkey (54), pork (55), beef (32) and lamb (34) purchased locally as whole cuts of meat over a 10-12 week period. NIR reflectance spectra were recorded over the wavelength range 400-2498nm at 2nm intervals on a NIR Systems 6500 scanning monochromator. The problem was defined as a series of binary decisions i. e. is the meat red or white\ulcorner is the red meat beef or lamb\ulcorner, is the white meat pork or poultry\ulcorner etc. Each of these decisions was made using an individual binary logistic model based on scores derived from principal component or partial least squares (PLS1 and PLS2) analysis. The results obtained were equal to or better than previous reports using factorial discriminant analysis, K-nearest neighbours and PLS2 regression. This new approach using a combination of exploratory and logistic analyses also appears to have advantages of transparency and the use of inherent structure in the spectral data. Additionally, it allows for the use of different data transforms and multivariate regression techniques at each decision step.

• Journal of Biosystems Engineering
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• 제27권2호
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• pp.117-124
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• 2002

This research was conducted to develop a method to remove the effect of surface temperature of Shingo pears for sugar content measurement. Sugar content was measured by a near-infrared spectrum analysis technique. Reflected spectrum and sugar content of a pear were used for developing regression models. For the model development, reflected spectrums having wavelengths in the range of 654 to 1,052nm were used. To remove the effect of surface temperature, special sample preparation techniques and partial least square (PLS) regression models were proposed and tested. 71 Shingo pears stored in a cold storage, which had 2$^{\circ}C$ inside temperature, were taken out and left in a room temperature for a while. Temperature and reflected spectrum of each pear was measured. To increase the temperature distribution of samples, temperature and reflected spectrum of each pear was measured four times with one hour twenty minutes interval. During the experiment, temperature of pears increased up to 17 $^{\circ}C$. The total number of measured spectrum was 284. Three groups of spectrum data were formed according to temperature distribution. First group had surface temperature of 14$^{\circ}C$ and total number of 51. Second group consisted of the first and the fourth experiment data which contained the minimum and the maximum temperatures. Third group consisted of 155 data with normal temperature-distribution. The rest data set were used for model evaluation. Results shelved that PLS model I, which was developed by using the first data group, was inadequate for measuring sugar content of pears which had different surface temperatures from 14$^{\circ}C$. After temperature compensation, sugar content predictions became close to the measured values. Since using many data which had wide range of surface temperatures, PLS model II and III were able to predict sugar content of pears without additional temperature compensation. PLS model IV, which included the surface temperatures as an independent variable. showed slightly improved performance(R$^2$=0.73). Performance of the model could be enhanced by using samples with more wide range of temperatures and sugar contents.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.3115-3115
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• 2001

In this study, portable near infrared (NIR) system was newly integrated with a photodiode array detector, which has no moving parts and this system has been successfully applied for evaluation of human skin moisture. The good correlation between NIR absorbance and absolute water content of separated hairless mouse skin was, in vitro, showed depending on the water content (7.42-84.94%) using this portable NIR system. Partial least squares (PLS) regression was used for the calibration with the 1100-1650 nm wavelength range. For the practical use for the evaluation of human skin based on moisture, PLS model for human skin moisture was, in vivo, developed using the portable NIR system based on the relative water content values of stratum corneum from the conventional capacitance method. The PLS model showed a good correlation. This study indicated that the portable NIR system could be a powerful tool for human skin moisture, which may be much more stable to environmental conditions such as temperature and humidity, compared to conventional methods. Furthermore, in order to confirm the performance of newly integrated portable NIR system, scanning type conventional NIR spectrometer was used in the same experiments and the results were compared.

• Bulletin of the Korean Chemical Society
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• 제27권6호
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• pp.863-868
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• 2006

The partial least squares (PLS-1) calibration model based on spectrophotometric measurement, for the simultaneous determination of sulfite and sulfide is described. This method is based on the difference between the rate of the reaction of sulfide and sulfite with Malachite Green in pH 7.0 buffer solution and at 25 ${^{\circ}C}$. The absorption kinetic profiles of the solutions were monitored by measuring the decrease in the absorbance of Malachite Green at 617 nm in the time range 10-180 s after initiation of the reactions with 2 s intervals. The experimental calibration matrix for partial least squares (PLS-1) calibration was designed with 24 samples. The cross-validation method was used for selecting the number of factors. The results showed that simultaneous determination could be performed in the range 0.030-1.5 and 0.030-1.2 $\mu$g m$L ^{-1}$ for sulfite and sulfide, respectively. The proposed method was successfully applied to simultaneous determination of sulfite and sulfide in water samples and whole human blood.

• Toxicological Research
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• 제34권2호
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• pp.127-132
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• 2018

Alcohol consumption triggers toxic effect to organs and tissues in the human body. The risks are essentially thought to be related to ethanol content in alcoholic beverages. The identification of ethanol in blood samples requires rapid, minimal sample handling, and non-destructive analysis, such as Raman Spectroscopy. This study aims to apply Raman Spectroscopy for identification of ethanol in blood samples. Silver nanoparticles were synthesized to obtain Surface Enhanced Raman Spectroscopy (SERS) spectra of blood samples. The SERS spectra were used for Partial Least Square (PLS) for determining ethanol quantitatively. To apply PLS method, $920{\sim}820cm^{-1}$ band interval was chosen and the spectral changes of the observed concentrations statistically associated with each other. The blood samples were examined according to this model and the quantity of ethanol was determined as that: first a calibration method was established. A strong relationship was observed between known concentration values and the values obtained by PLS method ($R^2=1$). Second instead of then, quantities of ethanol in 40 blood samples were predicted according to the calibration method. Quantitative analysis of the ethanol in the blood was done by analyzing the data obtained by Raman spectroscopy and the PLS method.

• 한국토양비료학회지
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• 제37권4호
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• pp.259-265
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• 2004

사과의 영양진단에서 사과잎 분석을 신속히 하기 위한 방법을 모색하기 위해 생잎과 건조잎을 이용해 근적의 스펙트럼을 측정하고 이를 질소 함량과의 최적의 상관관계를 도출하기 위해 부분소자승(PLS)과 주성분회귀(PCR)과 같은 다변량 분석법을 이용하여 비파괴 검량식을 작성하였다. 또한 검량식 작성에서 비파괴 측정 정확도를 향상시키기 위하여 smoothing, mean normalization, multiplicative scatter correction (MSC). derivative 등의 다양한 데이터 전처리 조작을 수행하여 정확도 향상 가능성을 조사하였다. 사과 건조잎의 비파괴 측정 가능성을 조사한 결과 PLS-1 모델에서 Norris first derivate하였을 태 RMSEP가 $0.6999g\;kg^{-1}$ 로 가장 좋았으며, 생잎은 Savitzky-Golay first derivate하였을 때에 RMSEP 가 $1.202g\;kg^{-1}$으로 가장 좋았다. 건조잎의 PCR 모델은 mean normalization 처리 후 Savitzky-Golay first derivative하였을 때가 RMSEP 가 $0.553g\;kg^{-1}$, 이었으며 생잎에서도 RMSEP는 $1.047g\;kg^{-1}$로 나타났다. 이와 같은 견과로서 사과의 생잎과 건조잎의 분석이 근적외분석기술에 의해 가능할 것으로 판단된다.

• 대한화학회지
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• 제47권1호
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• pp.19-25
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• 2003

흡수 분광법에 의해 얻은 스펙트럼을 주성분분석(principal analysis, PCA) 으로 자료를 요약하여 주성분 회귀분서(principal component regression, PCR)과 부분 최소자승법(partial least squares, PLS)으로 음이온과 비이온 계면활성제(anionic and nonionic surfactant)를 동시에 정량하는 방법에 대하여 연구하였다. 두 가지 계면활성제가 서로 다른 농도로 혼합되어 있는 26개의 시료용액을 400~700 nm 범위에서 스펙트럼을 얻었고, 이를 이용하여 PCR과 PLS회귀모델을 얻었다. 두 가지 계면활성제가 서로 다른 농도로 포함된 5개의 외부검정용 시료들의 스펙트럼들을 이용해서 회귀모델의 적합성을 검정하기 위하여 외부검정용 시료의 농도를 계산하였다. 계산된 농도를 이용하여 relative standard error of prediction(RSEP$_{\alpha}$)를 구하여 회귀모델의 적합성을 검정하였다.

• Korean Chemical Engineering Research
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• 제45권1호
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• pp.67-72
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• 2007

부분 최소자승회귀, 균형 잡힌 realization, 균형 잡힌 truncation을 결합함으로써, MIMO 상태공간 모델의 모델인식을 위한 효과적인 방법이 개발되었다. 개발된 방법에서 MIMO 시스템은 고차 ARX 모델로 표현되는 다중 MISO 시스템으로 분해된다. 이 때, ARX 모델의 파라미터는 부분 최소자승회귀에 의해 추정된다. 그 후, realization을 통해 각각의 MISO ARX 전달함수에 대한 MISO 상태공간 모델이 만들어지며, MIMO 상태공간 모델로 결합된다. 최종적으로, 균형 잡힌 realization과 균형 잡힌 truncation을 통해 최소의 균형 잡힌 MIMO 상태공간 모델이 얻어진다. 제안된 방법은 고압 $CO_2$ 용해도 측정 실험 장치의 온도제어를 위한 모델 예측 제어의 설계에 적용되었다.

• 응용통계연구
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• 제30권5호
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• pp.633-645
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• 2017

본 연구는 컨테이너선의 연료 소비 패턴의 발견을 위해 운항데이터 분석의 통계적 절차를 제안한다. 우리는 현 시점의 연료 소비를 발견하기 위해 연료 소비에 영향을 미치는 변수들을 파악하는 동시에 예측 모델을 개발 및 적용하는 것을 목적으로 한다. 선박의 데이터는 크게 운항데이터와 기기데이터로 분류할 수 있으며, 운항데이터는 항로, 항해 정보, 대수속도, 대지속도, 바람과 같은 외력에 대한 정보 등이 있고, 기기데이터는 엔진출력, RPM, 연료 소모량, 기기들의 온도 및 압력 등이 있다. 본 연구에서, 우리는 선박에 미치는 외력의 영향을 Beaufort Scale (BFS)을 기준으로 구분한 후에 PLS 회귀분석을 통한 예측 모델을 개발하였다.

• Asian-Australasian Journal of Animal Sciences
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• 제18권4호
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• pp.580-583
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• 2005

We have evaluated the application of spectroscopy using an insertion-type fiber-optic probe and a sensor at wavelengths from 400 to 1,100 nm to the measurement of milk fat content on dairy farms. The internal reflectance ratios of 183 milk samples were determined with a fiber-optic spectrophotometer at 5$^{\circ}C$, 20$^{\circ}C$ and 40$^{\circ}C$. Partial least squares (PLS) regression was used to develop calibration models for the milk fat. The best accuracy of determination was found for an equation that was obtained using smoothed internal reflectance data and three PLS factors at 20$^{\circ}C$. The correlation coefficients between predicted and reference milk fat at 5$^{\circ}C$, 20$^{\circ}C$ and 40$^{\circ}C$ were r=0.753, r=0.796 and r=0.783, respectively. The predictive explained variances ($Q^2$) of the final model, moreover, were more than 0.550 at all temperatures, and the regression coefficients of determination ($R^2$) were more than 0.6 (60%). Our results indicate that milk has different internal reflectance measured in the range of visible and near infrared wavelengths (400 to 1,100 nm), depending on its fat content.