• Journal of The Korean Society of Grassland and Forage Science
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• v.31 no.4
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• pp.409-414
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• 2011

This study was conducted to find out an alternative way of rapid and accurate analysis of forage quality. Near Infrared Reflectance Spectroscopy (NIRS) was used to evaluate the possibility of forage analysis. 175 samples consisted of Italian ryegrass, whole crop barley and pea seeded spring in 2009 were collected. The samples were analyzed for moisture, crude protein (CP), crude ash (CA), acid detergent fiber (ADF), and neutral detergent fiber (NDF), and also scanned using NIRSystem with wavelength from 400~2,500 nm. Multiple linear regression was used with wet analysis data for developing the calibration model and validated unknown samples. The important index in this experiment were SEC, SEP. The r2 value for moisture, CP, CA, ADF, and NDF in calibration set was 0.65, 0.97, 0.93, 0.99, and 0.97 and also was 0.15, 0.94, 0.96, 0.98 and 0.98 in validation set, respectively. The results of this experiment indicates that NIRS was reliable analytical method to assess forage quality for CP, CA ADF and NDF except moisture content in forage when proper samples incorporated into the equation development.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.40 no.6
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• pp.716-722
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• 1995

Near Infrared Reflectance Spectroscopy (NIRS) has been used as a tool for the rapid, accurate and nondestructive assay of small grain and forage quality analysis. The objective of this study was to establish the rapid, easy and accurate analysis method for major components of covered barley using NIRS system. NIRS used in this study was filter type instrument, Neotec 102. To obtain a useful calibration equation, standard regression between the data was analyzed by chemical analysis and by NIRS method. Standard errors of prediction (SEP) and simple correlations for unknown samples were calculated using obtained equation. SEPs for starch, $\beta$-glucan, protein and ash contents were 2.75%, 0.64%, 0.26% and 0.19%, respectively. The simple correlations for starch, $\beta$-glucan, protein and ash contents were 0.932, 0.588, 0.984 and 0.867, respectively. It was concluded that the NIRS method would be applicabl for the rapid determination of starch, protein and ash contents in barley grains.

• Korean Journal of Remote Sensing
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• v.35 no.6_4
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• pp.1319-1326
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• 2019

The KARI(Korea Aerospace Research Institute) operates the government satellite information application consultation to cope with ever-increasing demand for satellite images in the public sector, and carries out various support projects including the generation and provision of mosaic images on the Korean Peninsula every year to enhance user convenience and promote the use of satellite images. In particular, the government has wanted to increase the utilization of mosaic images on the Korean Peninsula and seek to classify and update mosaic images so that users can use them in their businesses easily. However, it is necessary to test and verify whether the classification results of the mosaic images can be utilized in the field since the original spectral information is distorted during pan-sharpening and color balancing, and there is a limitation that only R, G, and B bands are provided. Therefore, in this study, the reliability of the classification result of the mosaic image was compared to the result of KOMPSAT-3 image. The study found that the accuracy of the classification result of KOMPSAT-3 image was between 81~86% (overall accuracy is about 85%), while the accuracy of the classification result of mosaic image was between 69~72% (overall accuracy is about 72%). This phenomenon is interpreted not only because of the distortion of the original spectral information through pan-sharpening and mosaic processes, but also because NDVI and NDWI information were extracted from KOMPSAT-3 image rather than from the mosaic image, as only three color bands(R, G, B) were provided. Although it is deemed inadequate to distribute classification results extracted from mosaic images at present, it is believed that it will be necessary to explore ways to minimize the distortion of spectral information when making mosaic images and to develop classification techniques suitable for mosaic images as well as the provision of NIR band information. In addition, it is expected that the utilization of images with limited spectral information could be increased in the future if related research continues, such as the comparative analysis of classification results by geomorphological characteristics and the development of machine learning methods for image classification by objects of interest.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.1
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• pp.15-33
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• 2021

In this study, the possibility of discriminating Fire blight (FB) infection tested using the hyperspectral imagery. The reflectance of healthy and infected leaves and branches was acquired with 5 nm of full width at high maximum (FWHM) and then it was standardized to 10 nm, 25 nm, 50 nm, and 80 nm of FWHM. The standardized samples were divided into training and test sets at ratios of 7:3, 5:5 and 3:7 to find the optimal bands of FWHM by the decision tree analysis. Classification accuracy was evaluated using overall accuracy (OA) and kappa coefficient (KC). The hyperspectral reflectance of infected leaves and branches was significantly lower than those of healthy green, red-edge (RE) and near infrared (NIR) regions. The bands selected for the first node were generally 750 and 800 nm; these were used to identify the infection of leaves and branches, respectively. The accuracy of the classifier was higher in the 7:3 ratio. Four bands with 50 nm of FWHM (450, 650, 750, and 950 nm) might be reasonable because the difference in the recalculated accuracy between 8 bands with 10 nm of FWHM (440, 580, 640, 660, 680, 710, 730, and 740 nm) and 4 bands was only 1.8% for OA and 4.1% for KC, respectively. Finally, adding two bands (550 nm and 800 nm with 25 nm of FWHM) in four bands with 50 nm of FWHM have been proposed to improve the usability of multispectral image sensors with performing various roles in agriculture as well as detecting FB with other combinations of spectral bands.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1617-1617
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• 2001

It is well known now that near infrared spectroscopy (NIRS) is a fast, no destructive, and inexpensive analytical technique that could be used to classify, identify, and authenticate a wide range of foods and food items. Therefore, the main aims of this study were to provide a new insight into the authentication of five strawberry (Fragaria x ananassa) varieties and to correlate them with geographical zones and the propagating methods used. Three weeks plants of five different strawberry varieties (F. x ananassa Duch. cv Camarosa, Seascape, Chandler, F. Chiloensis, and F. Virginiana) were cultivated in vitro first and then transferred to pots with special soil, and grown in a greenhouse at CINVESTAV, all varieties were acquired from California (USA). After 18 months, ten leaves from each variety were collected. Transmission spectra from each leave were recorded over a range of 10, 000-4, 000 cm$-^{1}$, 32 scans of each strawberry leave were collected using a resolution of 4 cm$-^{1}$ with a Paragon IdentiCheck FT-NIR System Spectrometer. Triplicates of each strawberry leave were used. All spectra were analyzed using principal component analysis (PCA) and soft independent modeling class analogy (SIMCA). The optimum number of components to be used in the regression was automatically determined by the software. Camarosa was the only variety grown from the same shoot but propagated by a different method (direct or in vitro). Five different classes (varieties) or clusters were observed among samples, however, larger inter class distances were presented by the two wildtype samples (F. Chiloensis and F. Virginiana). Camarosa direct and Camarosa in vitro displayed a small overlapping region between them. On the other hand, Seascape variety presented the smallest rejection percentage among all varieties (more similarities with the rest of the samples). Therefore, it can be concluded that the application of NIRS technique allowed the authentication of all strawberry varieties and geographical origin as well. It was also possible to form subclasses of the same materials. The results presented here demonstrate that NIRS is a very powerful and promising analytical tool since all materials were authenticated and classified based on their variety, origin, and treatment. This is of a tremendous relevance since the variety and origin of a plant material can be established even before it gives its typical fruit or flower.

• The Journal of Engineering Geology
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• v.30 no.3
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• pp.347-378
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• 2020

Based on monthly average groundwater recharge over a nearly 10 year period, results of fully integrated hydrologic modeling of SWAT-MODFLOW, land cover, land use, soil type and hydrologic response unit (HRU) was used to assess the dominant influencing factors of groundwater recharge spatial patterns in Jangseong district. As dominant factors, land cover was FRSE (forest-evergreen) and soil type was Samgag. Landsat-8 OLI imaging spectrometer data were acquired in the period 2003 to 2004 and seasonal bare soil lines (BSL) were estimated through NIR-RED plot. Extent of slope of BSL was from 1.092 to 1.343 and the intercept was from -0.004 to -0.015. To know correlation between spatial groundwater recharge and soil-vegetation indices (PVI, NDVI, NDTI, NDRI), this study employed frequency and regression analysis. On May, RED band increased up 3 to 4 times compared to other seasons and only one turning point appeared as recharge-index with upward parabola bell shape as results of existing research. Considering precipitation, if the various studies for relationship between groundwater recharge and soil-vegetation index just like NDVI are performed, it is possible to estimate groundwater recharge through analyzing remote sensing data.

• Journal of the Korean Ceramic Society
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• v.56 no.2
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• pp.178-183
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• 2019

A process of fabricating the foamed glass that has closed pores with 8 ~ 580 ㎛ sizes without a blowing agent by sintering 10 ㎛ boron-free glass powder composed of CaO, MgO, SO₃, Al₂O₃-83 wt% SiO₂ at a molding pressure of 0 ~ 120 MPa and a sintering temperature of 750 ~ 1000℃ was investigated. To analyze the glass transition temperature of glass powder, thermogravimetric analysis-differential thermal analysis (TGA-DTA) method were used. The microstructure and pore size of foamed glass were examined using the optical microscopy and field emission scanning electron microscopy (FE-SEM). For the thermal diffusivity and color of the fabricated samples, a heat flow meter and ultraviolet-visible-near-infrared (UV-VIS-NIR)-colormetry were used, respectively. In the TGA-DTA result, the glass transition temperature of glass powder was confirmed to be 626℃. In the microstructure result, closed pores of 7 ~ 20 ㎛ were formed at 750 ~ 900℃, and they were not affected by the molding pressure and sintering temperature. However, at 1,000℃, when there was 0 MPa molding pressure, closed pores of 580 ㎛ were confirmed, and the pore size decreased as the molding pressure increased. Moreover, at a molding pressure of 30 MPa or higher, closed pores of approximately 400 ㎛ were formed. The porosity showed an increasing trend of smaller molding pressure and larger sintering temperature, and it was controllable in the range of 5.69 ~ 68.45%. In the thermal diffusivity result, there was no change according to the molding pressure, and, by increasing the sintering temperature, up to 0.115 W/m·K could be obtained. The Lab color index (CIE-Lab) results all showed a similar translucent white color regardless of molding pressure and sintering temperature. Therefore, based on the foamed glass without boron and blowing agent, it was confirmed that white foamed glass, which has closed pores of 8 ~ 580 ㎛ and a thermal diffusivity characteristic of 0.115 W/m·K, can be fabricated by changing the molding pressure and sintering temperature.

• Korean Journal of Soil Science and Fertilizer
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• v.37 no.4
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• pp.259-265
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• 2004

A quicker method was developed for foliar analysis in diagnosis of nitrogen in apple trees based on multivariate calibration procedure using partial least squares regression (PLSR) and principal component regression (PCR) to establish the relationship between reflectance spectra in the near infrared region and nitrogen content of fresh- and dry-leaf. Several spectral pre-processing methods such as smoothing, mean normalization, multiplicative scatter correction (MSC) and derivatives were used to improve the robustness and performance of the calibration models. Norris first derivative with a seven point segment and a gap of six points on MSC gave the best result of partial least squares-1 PLS-1) model for dry-leaf samples with root mean square error of prediction (RMSEP) equal to $0.699g\;kg^{-1}$, and that the Savitzky-Golay first derivate with a seven point convolution and a quadratic polynomial on MSC gave the best results of PLS-1 model for fresh-samples with RMSEP of $1.202g\;kg^{-1}$. The best PCR model was obtained with Savitzky-Golay first derivative using a seven point convolution and a quadratic polynomial on mean normalization for dry leaf samples with RMSEP of $0.553g\;kg^{-1}$, and obtained with the Savitzky-Golay first derivate using a seven point convolution and a quadratic polynomial for fresh samples with RMSEP of $1.047g\;kg^{-1}$. The results indicate that nitrogen can be determined by the near infrared reflectance (NIR) technology for fresh- and dry-leaf of apple.

• Journal of Biosystems Engineering
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• v.27 no.2
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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.

• Resources Recycling
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• v.26 no.1
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• pp.69-77
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• 2017

Used small household appliances(small e-waste) consists of a variety of complex materials and components. The small e-waste is mainly composed of plastics and an important potential source of waste plastic. The black plastics, particularly are very difficult to separate by resin type and therefore these are mainly recycled in the form of a mixtures. In the present study, the sorting technologies such as gravity and electro static separation, near-infrared ray(NIR) and IR/Raman optical sorting separation on mixture of black plastics were analyzed and their limitations on sorting process were also investigated. The Laser Induced Breakdown Spectroscopy(LIBS) spectrum of each black plastics was used for identification of black plastics by resin type, and after analyzing the normalization operation, Principal Component Analysis(PCA) was carried out. The spectrum data was optimized through PCA process. In order to improve the identification accuracy and sorting efficiency of black plastics, it is necessary to design a classifier with high efficiency and to improve the performance and reliability of the classifier by applying the field of intelligent algorithms.