• YAKHAK HOEJI
• /
• v.48 no.1
• /
• pp.60-64
• /
• 2004

Near-infrared (NIR) spectroscopy was used to determine rapidly and nondestructively the content of ambroxol in intact ambroxol tablets containing 30 mg (12.5％ m/m nominal concentration) by collecting NIR spectra in range 1100-1750 nm. The laboratory-made samples had 10.3∼15.9％ m/m nominal ambroxol concentration. The measurements were made by reflection using a fiber-optic probe and calibration was carried out by partial least square regression (PLSR) with autoscaling. Model validation was performed by randomly splitting the data set into calibration and validation data set (7 samples as a calibration data set and 5 samples as a validation data set). The developed NIR method gave results comparable to the known values of tablets in a laboratorial manufacturing Process, standard error of calibration (SEC) and standard error of prediction (SEP) being 0.49％ and 0.49％ m/m respectively. The method showed good accuracy and repeatability NIR spectroscopic determination in intact tablets allowed the potential use of real time monitoring for a running production process.

• Archives of Pharmacal Research
• /
• v.28 no.4
• /
• pp.458-462
• /
• 2005

The water content of skin was determined using a FT near infrared (NIR) spectrometer. NIR diffuse reflectance spectra were collected from hairless mouse, in vitro, and from human inner arm, in vivo. It was found that the variation of NIR absorbance band 1450 nm from OH vibration of water and 1940 nm from the combination involving OH stretching and OH deformation, depending on the absolute water content of separated hairless mouse skin, in vitro, using the FT NIR spectrometer. Partial least squares regression (PLSR) was applied to develop a calibration model. The PLS model showed good correlation. For practical use of the evaluation of human skin moisture, the PLS model for human skin moisture was developed in vivo on the basis of the relative water content of stratum corneum from the conventional capacitance method. The PLS model predicted human skin moisture with a standard errors of prediction (SEP) of 3.98 at 1130-1830 nm range. These studies showed the possibility of a rapid and nondestructive skin moisture measurement using FT NIR spectrometer.

• YAKHAK HOEJI
• /
• v.48 no.3
• /
• pp.177-181
• /
• 2004

Non-invasive blood glucose measurement from mouse tail was performed by near-infrared (NIR) spectroscopy. Three groups; normal, type I diabetes (insulin dependent diabetes mellitus, IDDM), type II diabetes (non-insulin dependent diabetes mellitus, NIDDM) group, were studied over a 10 weeks period with the collection of near-infrared (NIR) spectra. Spectral variations from long-term measurement (10 weeks) from dramatic and nonlinear changes in the optical properties of the live tissue sample were compensated by chemometrics techniques such as principle component analysis (PCA) and partial least squares (PLS) regression. The effect from mouse body temperature changes on NIR spectral data was also considered. This study showed that the compensation of variations from long-term measurement and temperature changes improved calibration accuracy of non-invasive blood glucose measurement.

• Proceedings of the PSK Conference
• /
• 2002.10a
• /
• pp.399.3-399.3
• /
• 2002

Near-infrared (NIR) system was used to determine rapidly and simply indomethacin in buffer solution for a dissolution test for tablets and capsules. Indomethacin standards were prepared ranging from 10 to 50ppm using mixture of phosphate buffer(pH 7,2) and water(1:4), The near infrared(NIR) transmittance spectra of indomethacin standard solutions were collected by using a quartz cell in 1 mm and 2mm pathlength, Partial least-square regression (PLSR) was explored to develop calibration models over the spectral range 1100-1700nm. (omitted)

• Proceedings of the PSK Conference
• /
• 2003.04a
• /
• pp.279.1-279.1
• /
• 2003

NIR reflectance spectroscopy, using a fiber-optic probe was used to determine rapidly and non-destructively the content of ambroxol in intact ambroxol 30 mg (nominal content 12.5％ m/m ambroxol) tablets by collecting NIR spectra in range 1100 - 1750 nm and using PLSR calibration method. The tablets (10.3 - 15.9％ m/m ambroxol, i.e., 82 - 127％ of the nominal label content) were used 7 calibration set and 5 validation set. (omitted)

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2017.04a
• /
• pp.40-40
• /
• 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
• /
• v.35 no.11
• /
• pp.51-62
• /
• 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.

• Journal of Biosystems Engineering
• /
• v.43 no.2
• /
• pp.148-159
• /
• 2018

Purpose: Unmanned air vehicle (UAV) remote sensing was applied to test various vegetation indices and make prediction models of protein content of rice for monitoring grain quality and proper management practice. Methods: Image acquisition was carried out by using NIR (Green, Red, NIR), RGB and RE (Blue, Green, Red-edge) camera mounted on UAV. Sampling was done synchronously at the geo-referenced points and GPS locations were recorded. Paddy samples were air-dried to 15% moisture content, and then dehulled and milled to 92% milling yield and measured the protein content by near-infrared spectroscopy. Results: Artificial neural network showed the better performance with $R^2$ (coefficient of determination) of 0.740, NSE (Nash-Sutcliffe model efficiency coefficient) of 0.733 and RMSE (root mean square error) of 0.187% considering all 54 samples than the models developed by PR (polynomial regression), SLR (simple linear regression), and PLSR (partial least square regression). PLSR calibration models showed almost similar result with PR as 0.663 ($R^2$) and 0.169% (RMSE) for cloud-free samples and 0.491 ($R^2$) and 0.217% (RMSE) for cloud-shadowed samples. However, the validation models performed poorly. This study revealed that there is a highly significant correlation between NDVI (normalized difference vegetation index) and protein content in rice. For the cloud-free samples, the SLR models showed $R^2=0.553$ and RMSE = 0.210%, and for cloud-shadowed samples showed 0.479 as $R^2$ and 0.225% as RMSE respectively. Conclusion: There is a significant correlation between spectral bands and grain protein content. Artificial neural networks have the strong advantages to fit the nonlinear problem when a sigmoid activation function is used in the hidden layer. Quantitatively, the neural network model obtained a higher precision result with a mean absolute relative error (MARE) of 2.18% and root mean square error (RMSE) of 0.187%.

• Journal of Biosystems Engineering
• /
• v.43 no.2
• /
• pp.138-147
• /
• 2018

Purpose: A narrowband hyperspectral imaging sensor of high-dimensional spectral bands is advantageous for identifying the reflectance by selecting the significant spectral bands for predicting crop yield over the broadband multispectral imaging sensor for each wavelength range of the crop canopy. The images acquired by each imaging sensor were used to develop the models for predicting the Chinese cabbage yield. Methods: The models for predicting the Chinese cabbage (Brassica campestris L.) yield, with multispectral images based on unmanned aerial vehicle (UAV), were developed by simple linear regression (SLR) using vegetation indices, and forward stepwise multiple linear regression (MLR) using four spectral bands. The model with hyperspectral images based on the ground were developed using forward stepwise MLR from the significant spectral bands selected by dimension reduction methods based on a partial least squares regression (PLSR) model of high precision and accuracy. Results: The SLR model by the multispectral image cannot predict the yield well because of its low sensitivity in high fresh weight. Despite improved sensitivity in high fresh weight of the MLR model, its precision and accuracy was unsuitable for predicting the yield as its $R^2$ is 0.697, root-mean-square error (RMSE) is 1170 g/plant, relative error (RE) is 67.1%. When selecting the significant spectral bands for predicting the yield using hyperspectral images, the MLR model using four spectral bands show high precision and accuracy, with 0.891 for $R^2$, 616 g/plant for the RMSE, and 35.3% for the RE. Conclusions: Little difference was observed in the precision and accuracy of the PLSR model of 0.896 for $R^2$, 576.7 g/plant for the RMSE, and 33.1% for the RE, compared with the MLR model. If the multispectral imaging sensor composed of the significant spectral bands is produced, the crop yield of a wide area can be predicted using a UAV.

• Proceedings of the Korean Society of Postharvest Science and Technology of Agricultural Products Conference
• /
• 2003.04a
• /
• pp.120-121
• /
• 2003

과수원에서 재배되는 배는 과수원 내의 위치, 시비, 토양 등의 요인에 따라 다양한 품질을 나타내며, 당도와 숙도의 편차가 크기 때문에 과수농가에서는 경험에 의존하여 적정 숙기로 판단되는 배를 수확하고 있다. 그러나 과학적이지 못한 사실에 기초한 수확 관행은 시장유통되는 배에 대하여 소비자들의 신뢰성 저하를 초래하게 되고 소비 감소와 더불어 농가 소득 감소로 이어지게 된다. 최근, 전국의 청과물 산지유통센터에는 근적외선을 이용하여 과일 내부의 당도, 산도, 결함 등을 실시간으로 판정할 수 있는 비파괴 선별기가 보급되고 있으나 이는 수확이후의 선별.규격화 유통을 위한 것이다. 본 연구에서는 이와는 달리, 수확 이전, 즉 재배 단계에서 배의 당도와 숙도를 판정하여 수확적기를 판단할 수 있도록 나무에 매달린 배에 대하여 가시광선과 근적외선 반사스펙트럼을 측정할 수 있고 이를 이용하여 당도와 숙도가 판정가능한 휴대형 센서를 개발하였으며, 개발된 시작기를 이용하여 당도판정의 가능성을 시험하였다. 휴대형 당도판정센서는 광원과 광섬유프로브, 광검출부, 당도판정부, 전원공급부로 구성된다. 광원은 할로겐램프(6V)를 이용하였고, 광섬유프로브는 동심원 형태로서 외부의 광섬유를 통하여 광원에서 시료로 빛이 조사되게 하고, 내부의 광섬유를 통하여 광검출기로 확산반사되는 광이 전달될 수 있도록 하였다. 전원공급부는 휴대와 충전이 가능한 배터리(12V, 2AH)와 이 배터리에서 정전류가 광원으로 보내어 질 수 있도록 제작된 회로로 구성하였다. 당도 판정을 위하여 518nm에서 1046nm의 파장대역에서의 반사스펙트럼을 이용하였고, 레퍼런스로써 백색 테플론 구를 제작하여 사용하였다. 수원 농산물 도매시장에서 판매중인 2002년산 신고 배를 구매하고, 시작기를 이용하여 총 113개의 배에 대한 반사스펙트럼을 측정하였다. 다음으로 굴절당도계로 당도값을 측정하고 반사스펙트림을 이용하여 당도값을 예측하기 위한 부분최소제곱회귀(PLSR)모델을 개발하였다. 여기서 모델의 정밀도는 교차검정법을 이용하여 검증하였다. 시료 표면과 광섬유프로브와의 접촉상태 불균일, 광원의 시간에 따른 경시 변화, 과일 형상의 차이 등에 의하여 측정된 반사스펙트럼은 상당한 변이를 나타내었으므로 이를 보정하기 위하여 반사 스펙트럼은 다분산보정처리하여 이용하였다. 당도 예측용 PLSR모델 개발의 결과, 모델의 결정계수($R^2$)는 0.67, SEC는 $\pm$0.4brix.로 나타났으며, 교차검정에 의한 미지 시료의 예측에서 총 113개의 미지 시료에 대한 결정계수는 0.57, SEP는 $\pm$ 0.46brix.로 나타났으며, 이는 현장에서 충분히 활용가능할 것으로 판단되었다. 금후, 전체 시스템의 부피와 중량을 줄이고 각 부분품들의 전력소모의 최소화할 수 있도록 개선할 계획이다.