• Journal of the Society of Disaster Information
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• v.16 no.3
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• pp.542-549
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• 2020

Purpose: As nitrate nitrogen produced during fermentation of liquid fertilizer is a pollution indicator of water, in this study, four research areas where liquid fertilizer was sprayed were selected, and a model was designed to estimate the concentration of nitrate nitrogen pollution. Method: Prior to shooting on site, a spectrum library was constructed by dividing the ratio of liquid fertilizer into 5 groups: 0%, 25%, 50%, 75%, and 100%. PLSR (Partial least squares regression) method was applied to hyperspectral images acquired in the study area based on the aspect of spectrum. Result: The behavior of nitrate nitrogen was confirmed by 1st and 2nd differentiation of the spectrum of the constructed liquid fertilizer. PLSR concentration estimation modeling was implemented using images from field experiments and compared with actual concentration of nitrate nitrogen. Conclusion: When comparing the PLSR concentration estimation model with the actual concentration of nitrate nitrogen, it was measured that the detection is possible in high concentration areas where the concentration of nitrate nitrogen is 70mg/kg or more.

• The Korean Journal of Applied Statistics
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• v.10 no.2
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• pp.227-239
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• 1997

PLSR is a powerful multivariate statistical tool that has been successfully applied to the quantitative analyses of data in spectroscopy, chemistry, and industrial process control. Data in spectorscopy is represented by spectrum matrix measured in many wavelengths. Problems of many kinds of noise in data and itercorrelation between wavelengths are quite common in such data. PLSR utilizes whole data set measured in many wavelengths to the analysis, and handles such problems through data compression method. We investigated the PLSR theory, and applied this method to the data for spectroscopic diagnosis of Total Hemoglobin in whole blood.

• Resources Recycling
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• v.27 no.1
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• pp.84-91
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• 2018

In this study, a novel soft information based most probable classification scheme is proposed for sorting recyclable metal alloys with laser induced breakdown spectroscopy (LIBS). Regression analysis with LIBS captured spectrums for estimating concentrations of common elements can be efficient for classifying unknown arbitrary metal alloys, even when that particular alloy is not included for training. Therefore, partial least square regression (PLSR) is employed in the proposed scheme, where spectrums of the certified reference materials (CRMs) are used for training. With the PLSR model, the concentrations of the test spectrum are estimated independently and are compared to those of CRMs for finding out the most probable class. Then, joint soft information can be obtained by assuming multi-variate normal (MVN) distribution, which enables to account the probability measure or a prior information and improves classification performance. For evaluating the proposed schemes, MVN soft information is evaluated based on PLSR of LIBS captured spectrums of 9 metal CRMs, and tested for classifying unknown metal alloys. Furthermore, the likelihood is evaluated with the radar chart to effectively visualize and search the most probable class among the candidates. By the leave-one-out cross validation tests, the proposed scheme is not only showing improved classification accuracies but also helpful for adaptive post-processing to correct the mis-classifications.

• Analytical Science and Technology
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• v.12 no.5
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• pp.375-381
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• 1999

Near infrared spectrophotometry(NIR) was developed as a non-invasive determination of blood alcohol. The first pure alcohol/water samples were prepared with ethanol concentration from 0.01 to 0.1%(w/w). Analysis of the second-derivative data was accomplished with multilinear regression(MLR). The standard error of calibration(SEC) of ethanol in ethanol/water solutions was approximately 0.0039%. The calibration models were established from the blood alcohol spectra by MLR and PLSR analysis. The best calibration was built with the second-derivative spectra of 2266 and 2326 nm by MLR. Second-derivative spectra in the spectral ranges of 1100~1340, 1500~1796 and 2064~2300 nm with four PLSR factors provided the standard error of prediction(SEP) of 0.030%(w/w). These results indicate that NIR may be applied for a fast non-invasive determination of alcohol in the blood.

• 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 Soil Science and Fertilizer
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• v.41 no.4
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• pp.267-272
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• 2008

Mid-infrared (MIR) spectroscopy, particularly Fourier transform infrared spectroscopy (FTIR), has emerged as an important analytical tool in quantification as well as identification of multi-atomic inorganic ions such as nitrate. In the present study, the possibility of quantifying soil nitrate via diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) without change of a sample phase or with least treated samples was examined. Four types of soils were spectrally characterized in terms of unique bands of soil contents and interferences with nitrate bands in the range of $2000-1000cm^{-1}$. In order to reduce the effects of soil composition on calibration model for nitrate, spectra transformed to the 1st order derivatives were used in the partial least squared regression (PLSR) model and the classification procedure associated with input soil types was involved in calibration system. PLSR calibration models for each soil type provided better performance results ($R^2$>0.95, RPD>6.0) than the model considering just one type of soil as a standard.

• 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.

• Journal of the Korean Chemical Society
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• v.49 no.6
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• pp.531-536
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• 2005

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2017.04a
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• pp.40-40
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• 2017

본 연구는 빛의 파장대가 넓어 보다 다양한 접근과 검출이 가능한 초분광 카메라 (VNIR spectral camera PS, SPECIN Filand)를 이용하여 정식시기가 다른 배추를 생육단계별로 영상을 취득한 후 배추 캐노피의 전 파장 (400~1000nm)으로 생육 추정모델을 개발하기 위해 수행하였다. 정식시기가 다른 배추를 생육단계별로 초분광 카메라로 영상을 취득한 후 취득된 영상 ($348{\times}1040$)을 ENVI (ver. 5.2, Exelis Visual Information Solutions, USA) 프로그램을 이용하여 식생지수 NDVI로 작물과 배경을 구분하였다. 배추 캐노피 영역에 전 파장을 산출한 후 반사판 영역의 전 파장을 이용하여 광 보정된 반사율을 산출하였다. 통계 프로그램인 R Project (ver.3.3.3, Development Core Team, Vienna, Austria)를 이용하여 배추의 반사율과 계측한 생육 정보를 PLSR (Partial least squares regression) 분석하여 정확도($R^2$) 및 정밀도 (RMSE [g,cm,count], RE [%])로 나타내었고 그 모델은 full-cross validation (FV) 하여 타당성을 검증하였다. 정식시기가 다른 배추의 모든 생육단계의 생육정보를 이용하여 PLSR (Partial least squares regression) 결과 엽장을 추정한 모델의 $R^2$는 84% 이상의 정확도와 RMSE 3.2cm 이하의 좋은 정밀도를 보였다. 엽폭을 추정한 모델의 $R^2$는 73% 이상의 정확도와 RMSE 3.5cm 이하의 정밀도를 보였고 엽수를 추정한 모델의 $R^2$는 93% 이상의 정확도와 RMSE 6.3Count 이하의 정밀도로 보여 캐노피의 전 파장을 이용해 생육을 추정하는 것이 가능하다고 판단되었으며 이 모델들의 타당성 검증에서도 좋은 정확도와 정밀도를 보였다. 그러나 배추의 중요한 생육인자 중 생체중을 추정한 모델의 $R^2$는 89% 이상으로 정확도가 높았으나 RMSE 571.1g 이하로 낮은 정밀도를 보여 생체중을 정확히 추정하기 어려웠다. 따라서 다른 통계분석방법으로 전 파장과 생육정보를 분석하거나 특정 밴드를 선택하여 산출한 식생지수를 이용한 추정 모델의 개발을 통하여 오차를 개선할 필요가 있다고 사료된다. 추후 반복 실험하여 분석한 추정 모델과 비교 분석하여 다양한 환경 및 생물 조건에 범용성을 가진 모델을 개발할 필요가 있다.

• Journal of the Korean Geotechnical Society
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• v.35 no.11
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• pp.51-62
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• 2019

In this study, a simple method to estimate the soil water content variation in a wide area was proposed using hyperspectral near-infrared images. The reflectance data of a sand, granite soils, and a kaolinite were measured by reflecting the soil samples with different wavelengths in the visible and near-infrared (VNIR) regions using hyperspectral cameras. The measured reflectances and parameters were used to build a water content prediction model using the Partial Least Square Regression (PLSR) analysis. In the water content prediction model, the Area of Reflectance (Near-infrared, NIR) parameter was the most suitable parameter to determine the water content. The parameter was applicable regardless of the soil type, as the coefficient of determination (R²) exceeded 0.9 for each soil sample. Additionally, the mean absolute percentage error (MAPE) was less than 15% when compared with the actual water content of the soil. Therefore, the predictability of water content variation for soils with water content lower than 50% was confirmed. Accordingly through this study, the predictability of water content variation in several soil types using the hyperspectral near-infrared images was confirmed. For further development, a model that incorporates soil classification would be required to improve the accuracy of the model and to predict higher range of water contents.