• Proceedings of the KSRS Conference
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• 2009.03a
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• pp.121-125
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• 2009

농업과 환경분야에서 토양 상태를 신속하고 주기적으로 모니터링하는 것에 대한 관심이 높아지고 있다. 토양의 특성을 측정하는 기존의 화학분석 방식은 분석의 정밀도, 시료의 수, 분석항목 등에 따라 시간, 인력, 비용적 소모가 커진다. 최근에는 식품, 농업, 환경 분야에서 신속하고 비파괴적 분석 방법으로 가시 근적외선 분광학을 도입하고 있다. 가시 근적외선 영역(VNIR, 400-2400 nm)에는 다양한 물질의 고유한 흡수분광형태가 존재한다는 이론적 토대로부터 물질의 정성 정량적 분석이 가능하다고 알려져 있다. 본 연구에서는 VNIR 분광 스펙트럼으로부터 Al, organic carbon (OC), clay, silt, sand, CEC (Cation exchange capacity), CEC/clay 등의 토양 특성을 정량하고자 하였다. 농경지에서 채취한 94개 토양시료를 기존의 화학분석 방법으로 분석하고 실내에서 VNIR 스펙트럼을 측정하였다. 스펙트럼은 원시형태와, 1차, 2차 도함수로 변환된 형태 모두 partial least square regression (PLSR) 모델에 적용하였다. PLSR에 의한 토양특성 추정식은 RMSE, $R^2$, SDE, RPD 값을 이용하여 검증하였다. Al, OC, silt, sand 함량에 대해서는 통계적으로 유의한 수준의 추정값을 산출하였고, clay와 CEC/clay에 대해 추정한 값은 실측값과 약한 상관성을 나타내었다. 이러한 분광학적인 추정 기법은 영상을 이용한 정성 정량분석에 활용될 수 있을 것으로 사료된다.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.581-589
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• 2021

Using visible-near infrared(Vis-NIR) spectra combined with machine learning methods, the feasibility of quick and non-destructive classification of Convolvulaceae species was studied. The main aim of this study is to classify six Convolvulaceae species in the field in different geographical regions of South Korea using a handheld spectrometer. Spectra were taken at 1.5 nm intervals from the adaxial side of the leaves in the Vis-NIR spectral region between 400 and 1,075 nm. The obtained spectra were preprocessed with three different preprocessing methods to find the best preprocessing approach with the highest classification accuracy. Preprocessed spectra of the six Convolvulaceae sp. were provided as input for the machine learning analysis. After cross-validation, the classification accuracy of various combinations of preprocessing and modeling ranged between 43.4% and 98.6%. The combination of Savitzky-Golay and Support vector machine methods showed the highest classification accuracy of 98.6% for the discrimination of Convolvulaceae sp. The growth stage of the plants, different measuring locations, and the scanning position of leaves on the plant were some of the crucial factors that affected the outcomes in this investigation. We conclude that Vis-NIR spectroscopy, coupled with suitable preprocessing and machine learning approaches, can be used in the field to effectively discriminate Convolvulaceae sp. for effective weed monitoring and management.

• Journal of the Korean Wood Science and Technology
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• v.38 no.6
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• pp.526-531
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• 2010

For efficient use of wood, it is important to control moisture of wood in processing wood. Near-infrared (NIR) spectroscopy can be used to estimate the physical and chemical properties of materials quickly and nondestructively. In this study, it was intended to measure the moisture contents on the surface of wood using NIR spectroscopy coupled with multivariate analytic statistical techniques. Because NIR spectroscopy is affected by the chemical components of the specimens and contains signal noise, a regression model for detecting moisture content of wood was established after carrying out several numerical pretreatments such as Smoothing, Derivative and Normalization in this study. It shows that the regression model using NIR absorbance in the range of 750~2,500 nm predicts the actual surface moisture content very well. Near-infrared spectroscopy technique developed in this study is expected to improve a technology to control moisture content of wood in using and drying process.

• Journal of Sensor Science and Technology
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• v.10 no.6
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• pp.273-278
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• 2001

Absorption spectra of blood components have been measured for the purpose of predicting the total hemoglobin concentration. We obtained absorption spectra of major blood components from the visible to near-infrared of $400{\sim}2500nm$ region. In the near-infrared, water is the main absorbing constituent. The amount of water in the sample cell varies depending on the volume of solute concentration(water displacement). We acquired water-compensated spectra by considering the variation of water volume depending on the variation of analyze concentration. Those spectra show inherent absorption peaks of analyzes and linearity with respect to concentration. We selected wavelengths for non-invasive measurement of hemoglobin concentration considering the scattering effect of tissue and the interference of other blood components.

• The Bulletin of The Korean Astronomical Society
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• v.43 no.1
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• pp.48.1-48.1
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• 2018

SPHEREx의 중요 임무 중 하나는 $0.75{\mu}m$와 $5{\mu}m$ 사이에서 $H_2O$, CO, $CO_2$, XCN, OCS, 그리고 $CH_3OH$와 같은 얼음 분자의 전천 탐사 스펙트럼을 제공하는 것이다. 이러한 얼음 분자는 성간분자운의 먼지 티끌 표면에서 생성되어 별 탄생의 필연적 산물이며, 행성이 형성되는 원시행성계원반에서 다양한 변화를 겪게 되고, 복잡한 유기분자를 합성하게 된다. 하지만 충분하지 않은 관측 자료로 인해, 얼음 분자의 진화에 대한 이해가 미약한 상태이다. 현재까지는 근적외선에서 충분히 밝은 100 여개의 배경별이나 원시성에 대해서만 얼음 스펙트럼을 관측할 수 있었다. SPHEREx를 이용한 고감도 전천 탐사 미션은 약 20,000 여개의 배경별과 원시성에 대해 얼음 분자 스펙트럼을 제공할 것이다. 이렇게 100 배 이상 늘어난 샘플 스펙트럼 수로 인해, 얼음 분자의 진화에 대해서 통계적으로 의미있는 연구가 가능해 질 것이다. 본 발표에서는 SPHEREx의 Ice Program을 소개하고, 기대되어지는 결과에 대해서 논의하고자 한다.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.8
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• pp.628-635
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• 2017

It is important to prescribe and take medicines that are appropriate for symptoms, since medicines are closely related to human health and life. Moreover, it becomes more important to accurately classify genuine medicines with counterfeit, since the number of counterfeit increases worldwide. However, the number of high-quality experts who have enough experience to properly classify them is limited and there exists a need for the automatic technique to classify medicine tablets. In this paper, we propose a method to classify the tablets by using a handheld spectrometer which provides both Near Infra-Red (NIR) and visible light spectrums. We adopted Support Vector Machine(SVM) as a machine learning algorithm for tablet classification. As a result of the simulation, we could obtain the classification accuracy of 99.9 % on average by using both NIR and visible light spectrums. Also, we proposed a two-step SVM approach to discriminate the counterfeit tablets from the genuine ones. This method could improve both the accuracy and the processing time.

• Korean Journal of Food Science and Technology
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• v.34 no.3
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• pp.356-360
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• 2002

The honey samples harvested in 1996, 1997, and 1998 were used for calibration and validation. NIR spectra were obtained using NIR spectrometer and quartz glass device with gold coating diffuser. Multiple linear regression and partial least square were used for calibrations. The correlation coefficient (RSQ) and standard error of prediction (SEP) obtained for moisture were 0.997 and 0.1%, respectively. The RSQ and SEP for fructose and glucose were 0.926 and 0.951%, and the SEP were 0.54% and 0.52% respectively. The validation results for sucrose, maltose, HMF definition, and acidity of honey were considered to be sufficient for practical use RSQ and SEP for SCIR were 0.950 and $1.08%_{\circ}$, respectively. These results are indications of the rapid determination of purity of the honey through NIR analysis.

• Journal of Animal Environmental Science
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• v.13 no.2
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• pp.113-120
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• 2007

This study was conducted to measure Nitrogen(N), Phosphate($P_2O_5$), Potassium ($K_2O$), Organic matter(OM) and Moisture content of liquid pig manure by Near Infrared Spectroscopy(NIRS) and to develop an alternative and analytical instrument which are used for measurement of N, $P_2O_5$, $K_2O$, OM, and Moisture contents for liquid pig manure. The liquid pig manure sample's transmittance spectra were measured with a NIRS in the wavelength range of 400 to 2,500 nm. Multiple linear regression and partial least square regression were used for calibrations. The correlation coefficient(RSQ) and standard error of calibration(SEC) obtained for nitrogen were 0.9190 and 2.1649, respectively. The RSQ for phosphate, potassium, organic matter and moisture contents were 0.9749, 0.5046, 0.9883 and 0.9777, and the SEC were 0.5019, 1.9252, 0.1180 and 0.0789, respectively. These results are indications of the rapid determination of components of liquid pig manure through the NIR analysis. The simple analytical instrument for liquid pig manure consisted of a tungsten halogen lamp for light source, a sample holder, a quartz cell, a SM 301 spectrometer for spectrum analyzer, a power supply, an electronics, a computer and a software. Results showed that the simple analytical instrument that was developed can approximately predict the phosphate, organic matter and moisture content of the liquid pig manure when compared to the analysis taken by NIRS. The low predictability value of potassium however, needs further investigation. Generally, the experiment proved that the simple analytical instrument was reliable, feasible and practical for analyzing liquid pig manure.

• Proceedings of the Korean Society of Crop Science Conference
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• 2003.10a
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• pp.152-153
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• 2003

• Proceedings of the Optical Society of Korea Conference
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• 2005.07a
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• pp.108-109
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• 2005