• 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.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1998.06b
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• pp.171-177
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• 1998

The part Ⅰ was for developing regression models to predict the moisture content, protein content and viscosity of brown and milled rice using Near Unfrared (NIR) Reflectance analyzer. The purpose of this study(part Ⅱ) is to measure fundamental data required for the prediction of rice quality , and to develop regression models to predict the protein content of brown and milled rice, brown rice yield from undreid paddy powder by using Near Infrared (NIR) Reflectance analyzer. The results of this study were summarized as follows . The predicted values of protein contents obtained from the undried paddy powder were will correlated to the measured values from brown and milled rice. The predicted yields of brown rice from undried paddy powder were not well correlated to be lab measured values from dried paddy. Continuous study in wavelength selection and of constituent relationship is necessary for practical application.

• Proceedings of the PSK Conference
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• 2002.10a
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• pp.400.2-400.2
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• 2002

Near-infrared(NIR) reflectance spectroscopy was employed to qualify various ploymorphs. We collected 8 potential polymorphs forms of Medicine T for this study. Near-infared spectra of the powder samples contained in glass vials were obtained over the wavelength region of 1100-1750nm. There were the peak around 1560nm in the 6 spectra among 8 spectra. Principal component analysis(PCA) has been performed to examine the qualitative difference of 8 polymorphs PC space. (omitted)

• Korean Journal of Agricultural Science
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• v.43 no.5
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• pp.715-722
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• 2016

Rapid determination of food quality is important in food distribution. In this study, the chemical properties of oats were analyzed using visible-near infrared (VIS-NIR) spectroscopy. The objective of this study was to develop and validate a predictive model of oat quality by VIS-NIR spectroscopy. A total of 200 oat samples were collected from domestic and import markets. Reflectance spectra, moisture, protein, fat, Fe, and K of oat samples were measured. Reflectance spectra were measured in the wavelength range of 400 - 2,500 nm at 2 nm intervals. The reflectance spectrum of an oat sample was measured after sample cell and reflectance plate spectrum measurement. Preprocessing methods such as normalization and $1^{st}$ and $2^{nd}$ derivations were used to minimize the spectroscopic noise. The partial-least-square (PLS) models were developed to predict chemical properties of oats using a commercial software package, Unscrambler. The PLS models showed the possibility to predict moisture, protein, and fat content of oat samples. The coefficient of determination ($R^2$) of moisture, protein, and fat was greater than 0.89. However, it was hard to predict Fe and K concentrations due to their low concentrations in the oat samples. The coefficient of determinations of Fe and K were 0.57 and 0.77, respectively. In future studies, the stability and practicability of these models should be improved by using a high accuracy spectrophotometer and by performing calibrations with a wider range of oat chemicals.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.55 no.4
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• pp.357-364
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• 2010

Kjeldahl method used in many materials from various plant parts to determine protein contents, is laborious and time-consuming and utilizes hazardous chemicals. Near-infrared (NIR) reflectance spectroscopy, a rapid and environmentally benign technique, was investigated as a potential method for the prediction of protein content. Near-infrared reflectance spectra(1100-2400 nm) of coarse cereal grains(n=100 for each germplasm) were obtained using a dispersive spectrometer as both of grain itself and flour ground, and total protein contents determined according to Kjeldahl method. Using multivariate analysis, a modified partial least-squares model was developed for prediction of protein contents. The model had a multiple coefficient of determination of 0.99, 0.99, 0.99, 0.96 and 0.99 for foxtail millet, sorghum, millet, adzuki bean and mung bean germplasm, respectively. The model was tested with independent validation samples (n=10 for each germplasm). All samples were predicted with the coefficient of determination of 0.99, 0.99, 0.99, 0.91 and 0.99 for foxtail millet, sorghum, millet, adzuki bean and mung bean germplasm, respectively. The results indicate that NIR reflectance spectroscopy is an accurate and efficient tool for determining protein content of diverse coarse cereal germplasm for nutrition labeling of nutritional value. On the other hands appropriate condition of cereal material to predict protein using NIR was flour condition of grains.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.23 no.4
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• pp.367-374
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• 2005

Recently, forest damage has occurred often and made big issues. Among them, the damage by forest fire is not only damage of itself but also being connected with secondary damage like a flood. This is the fact that a forest fire is caused rather artificially by people than nature. In this study, we try to investigate damage of a forest fire through spectral reflectance of a plant community surveyed using a near infrared CCD camera and a SPM (Spectral Radiometer) as advanced work to use satellite image data. That is, damage of a forest fire by the naked eye observation was divided into the No damage, the light damage, the serious damage and we estimated activity of forest and grasped revival possibility of forest. Through correlation analysis between the spectral reflectance by SPM and the near infrared CCD camera, we could get high correlation in the No damage and light damage. Therefore, when we surveyed damage of a forest fire, we could grasp damage, that is hardly observed by the naked eye by, using jointly the spectral radiometer and the near infrared CCD camera.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.6
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• pp.4121-4124
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• 2015

Near infrared blocking function in energy saving window glass is required. The design, deposition and characteristics of optical thin films for reflection of near-infrared light were studied. The optical thin film is designed as laminated film structure with low refractive index film and high index film. Deposition experiments of $SiO_2$ and $TiO_2$ thin films with designed structure using the RF sputtering method were carried out. The characteristics of the thin film with deposition conditions were analyzed. High-refractive-index thin film of $TiO_2$/low refractive-index thin film of $SiO_2$ and high-refractive-index thin film of $TiO_2$ structure for reflection of near-infrared light was designed to be simulated. Results of simulation showed reflectance of 30% or more in the range from 930nm to 1682nm. Triple layer thin films fabricated with simulated results showed wavelength bands from 930nm to 1525nm for the reflectance of 33% or more.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.2102-2102
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• 2001

Near infrared, diffuse reflectance spectroscopy has shown significant potential for in vitro and in vivo assessment of metabolic status. However, the complexity of living samples can lead to ambiguous results. This presentation will focus on methods that provide controls for scattering and absorption estimation in tissue. For robust estimations, normalization procedures will be shown which can greatly improve interpretability of results. Normalization based on time, location and spectral property will be shown with data from models, tissue phantoms and in vivo measurements. In particular, interpretation of NIR spectra associated with major respiratory constituents will be examined. Measurement of constituents such as hemoglobin, myoglobin, tissue edema, and lactate will be shown. Results suggest that NIR may provide a valuable tool for physiological monitoring in critical care research and practice.

• Korean Journal of Soil Science and Fertilizer
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• v.45 no.4
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• pp.497-502
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• 2012

This study investigates the prediction of soil OM on Korean soils using the Visible-Near Infrared (Vis-NIR) spectroscopy. The ASD Field Spec Pro was used to acquire the reflectance of soil samples to visible to near-infrared radiation (350 to 2500 nm). A total of 503 soil samples from 61 Korean soil series were scanned using the instrument and OM was measured using the Walkley and Black method. For data analysis, the spectra were resampled from 500-2450 nm with 4 nm spacing and converted to the $1^{st}$ derivative of absorbance (log (1/R)). Partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil OM. Regression rules model estimates the target value by building conditional rules, and each rule contains a linear expression predicting OM from selected absorbance values. The regression rules model was shown to give a better prediction compared to PLSR. Although the prediction for Andisols had a larger error, soil order was not found to be useful in stratifying the prediction model. The stratification used by Cubist was mainly based on absorbance at wavelengths of 850 and 2320 nm, which corresponds to the organic absorption bands. These results showed that there could be more information on soil properties useful to classify or group OM data from Korean soils. In conclusion, this study shows it is possible to develop good prediction model of OM from Korean soils and provide data to reexamine the existing prediction models for more accurate prediction.

• Korean Journal of Food Science and Technology
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• v.23 no.4
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• pp.447-451
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• 1991

Red pepper peels stored with seeds or stems in the powder state at $30^{\circ}C$ resulted in decrease of quality components such as capsanthin, capsaicin and total sugars. The effect of seeds on the quality deterioration was larger than stems. A near-infrared reflectance spectroscopic(NIRS) method was evaluated for the determination of seed and stem contents in red pepper peels. The standard error of prediction was 1.76% in seeds and 0.43% in stems. It is concluded that the NIRS method is suitable for the determination of seen and stem contents in red pepper powder.