• Korean Journal of Food Science and Technology
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• v.53 no.4
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• pp.391-398
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• 2021

As global interest in herbal medicines has increased, the adulteration of herbal medicines has become a critical safety issue. Adulteration with dyes to improve the appearance of low-quality products is of particular concern. This study aimed to develop a high-performance liquid chromatography (HPLC) method to detect dyes added as adulterants to Phellodendron. Samples were analyzed on a C18 column using 50 mM ammonium acetate and acetonitrile as the mobile phase. All calibration curves showed good linearity (r² ≥0.9999) over the five-point concentration range (1-50 mg/kg). Limit of detection ranged from 0.04-0.35 mg/kg, and limit of quantification ranged from 0.11-1.07 mg/kg. The repeatability and reproducibility for these measurements were 94.2-103.3% and 96.6-103.8% for accuracy and 0.14-2.28 RSD (%) and 0.80-2.37 RSD (%) for precision. Moreover, the measurement uncertainty of the low, medium, and high concentrations for 10 dyes was considered. Thus, this HPLC method is suitable for detecting color adulteration of Phellodendron.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.423-423
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• 2022

수자원 계획 및 관리 관점에 있어 수문모형은 중요한 도구 중 하나이며 모의의 신뢰성을 높이기 위하여 검정 및 보정 과정을 거친다. 이는 일반적으로 장기간의 과거 수문기상자료를 활용하며 자료가 정상성(stationarity)이라는 가정에 따라 매개변수를 산정한다. 그러나 최근 기후변화 문제가 심화되며, 우리나라의 경우 여름철 호우의 강도 및 빈도가 증가할 것으로 전망되는 실정에서 수문 모형의 정상성을 가정한 매개변수 추정은 강우-유출 관계에 왜곡을 초래할 수 있다. 이러한 점에서 수문기상자료의 변동성을 고려한 수문모형의 검정 및 보정기법이 필요할 것으로 판단된다. 본 연구에서는 개념적 강우-유출 모형을 활용하여 산정된 소양강댐의 기존 매개변수와 수문기상자료의 경향성을 비교하여 모형의 적합성 향상 및 다양한 매개변수 산정 방식을 제공하고자 한다. 이를 위해 전역최적화 기법(global optimization method)을 도입하여 매개변수 추정시 발생하는 불확실성을 정량화하였고 동적 기후 예측 매개변수(dynamic climate predictors)를 활용하여 최적화를 수행하였다. 교차검증을 통하여 기존의 매개변수 추정 절차와 비교 검토를 수행하였다.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1125-1139
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• 2022

In an unmanned aerial vehicles (UAVs) system, a physical offset can be existed between the global positioning system/inertial measurement unit (GPS/IMU) sensor and the observation sensor such as a hyperspectral sensor, and a lidar sensor. As a result of the physical offset, a misalignment between each image can be occurred along with a flight direction. In particular, in a case of multi-sensor system, an observation sensor has to be replaced regularly to equip another observation sensor, and then, a high cost should be paid to acquire a calibration parameter. In this study, we establish a precise sensor model equation to apply for a multiple sensor in common and propose an independent physical offset estimation method. The proposed method consists of 3 steps. Firstly, we define an appropriate rotation matrix for our system, and an initial sensor model equation for direct-georeferencing. Next, an observation equation for the physical offset estimation is established by extracting a corresponding point between a ground control point and the observed data from a sensor. Finally, the physical offset is estimated based on the observed data, and the precise sensor model equation is established by applying the estimated parameters to the initial sensor model equation. 4 region's datasets(Jeon-ju, Incheon, Alaska, Norway) with a different latitude, longitude were compared to analyze the effects of the calibration parameter. We confirmed that a misalignment between images were adjusted after applying for the physical offset in the sensor model equation. An absolute position accuracy was analyzed in the Incheon dataset, compared to a ground control point. For the hyperspectral image, root mean square error (RMSE) for X, Y direction was calculated for 0.12 m, and for the point cloud, RMSE was calculated for 0.03 m. Furthermore, a relative position accuracy for a specific point between the adjusted point cloud and the hyperspectral images were also analyzed for 0.07 m, so we confirmed that a precise data mapping is available for an observation without a ground control point through the proposed estimation method, and we also confirmed a possibility of multi-sensor fusion. From this study, we expect that a flexible multi-sensor platform system can be operated through the independent parameter estimation method with an economic cost saving.

• Journal of the Korean Association of Geographic Information Studies
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• v.20 no.3
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• pp.141-152
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• 2017

This paper presents a technical method for flood estimation based on satellite rainfall and satellite rainfall correction method for watersheds lacking measurement data. The study area was the Sebou Watershed, Morocco. The Integrated Flood Analysis System(IFAS) and Grid-based Rainfall-Runoff Model(GRM) were applied to estimate watershed runoff. Daily rainfall from ground gauges and satellite-derived hourly data were used. In the runoff simulation using satellite rainfall data, the composites of the daily gauge rainfall and the hourly satellite data were applied. The Shuttle Radar Topographic Mission Digital Elevation Model(SRTM DEM) with a 90m spatial resolution and 1km resolution data from Global map land cover and United States Food and Agriculture Organization(US FAO) Harmonized World Soil Database(HWSD) were used. Underestimated satellite rainfall data were calibrated using ground gauge data. The simulation results using the revised satellite rainfall data were $5,878{\sim}7,434m^3/s$ and $6,140{\sim}7,437m^3/s$ based on the IFAS and GRM, respectively. The peak discharge during flooding of Sebou River Watershed in 2009~2010 was estimated to range from $5,800m^3/s$ to $7,500m^3/s$. The flood estimations from the two hydrologic models using satellite-derived rainfall data were similar. Therefore, the calibration method using satellite rainfall suggested in this study can be applied to estimate the flood discharge of watersheds lacking observational data.

• Journal of Wetlands Research
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• v.24 no.2
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• pp.83-92
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• 2022

The amount of the plastic waste has been increasing according to global demand for plastic. Microplastics are the most hazardous among all plastic pollutants due to their toxicity and unknown physicochemical properties. This study investigates the optimal methodology that can be applied to sewage samples for detecting microplastics before discussing reducing microplastics in MWTPs. In this study, the effect of different pretreatment methods while detecting microplastic analysis of MWTP influent samples was investigated; the samples were collected from the J sewage treatment plant. There are many pretreatment methods but two of them are widely used: Fenton digestion and hydrogen peroxide oxidation. Although there are many pretreatment methods that can be applied to investigate microplastics, the most widely used methods for sewage treatment plant samples are Fenton digestion and H₂O₂ oxidation. For each pretreatment method, there were factors that could cause an error in the measurement. To overcome this, in the case of the Fenton digestion pretreatment, it is recommended to proceed with the analysis by filtration instead of the density separation method. In the case of the H₂O₂ oxidation method, the process of washing with distilled water after the reaction is recommended. As a result of the analysis, the concentration of microplastics was measured to be 2.75ea/L for the sample using the H₂O₂ oxidation method and 3.2ea/L for the sample using the Fenton oxidation method, and most of them were present in the form of fibers. In addition, it is difficult to guarantee the reliability of measurement results from quantitative analysis performed via microscope with eyes. A calibration curve was created for prove the reliability. A total of three calibration curves were drawn, and as a result of analysis of the calibration curves, all R² values were more than 0.9. This ensures high reliability for quantitative analysis. The qualitative analysis could determine the series of microplastics flowing into the MWTP, but could not confirm the chemical composition of each microplastic. This study can be used to confirm the chemical composition of microplastics introduced into MWTP in the future research.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.3
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• pp.143-149
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• 2018

Recently, due to the large-scale damage of natural disasters caused by global climate change, a monitoring system applying remote sensing technology is being constructed in disaster areas. Among remote sensing platforms, the drone has been actively used in the private sector due to recent technological developments, and has been applied in the disaster areas owing to advantages such as timeliness and economical efficiency. This paper deals with the development of a preprocessing system that can map the drone image data in a near-real time manner as a basis for constructing the disaster monitoring system using the drones. For the research purpose, our system is based on the SURF algorithm which is one of the computer vision technologies. This system aims to performs the desired correction through the feature point matching technique between reference images and shot images. The study area is selected as the lower part of the Gahwa River and the Daecheong dam basin. The former area has many characteristic points for matching whereas the latter area has a relatively low number of difference, so it is possible to effectively test whether the system can be applied in various environments. The results show that the accuracy of the geometric correction is 0.6m and 1.7m respectively, in both areas, and the processing time is about 30 seconds per 1 scene. This indicates that the applicability of this study may be high in disaster areas requiring timeliness. However, in case of no reference image or low-level accuracy, the results entail the limit of the decreased calibration.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1247-1247
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• 2001

Constituents of animal biofluids such as milk, blood and urine contain information specifically related to metabolic and health status of the ruminant animals. Some changes in composition of biofluids can be attributed to disease response of the animals. Mastitis is a major problem for the global dairy industry and causes substantial economic losses from decreasing milk production and reducing milk quality. The purpose of this study was to investigate potential of NIRS combined with multivariate analysis for cow's mastitis diagnosis based on NIR spectra of milk, blood and urine. A total of 112 bulk milk, urine and blood samples from 4 Holstein cows were analyzed. The milk samples were collected from morning milking. The urine samples were collected before morning milking and stored at -35$^{\circ}C$ until spectral analysis. The blood samples were collected before morning milking using a catheter inserted into the carotid vein. Heparin was added to blood samples to prevent coagulation. All milk samples were analyzed for somatic cell count (SCC). The SCC content in milk was used as indicator of mastitis and as quantitative parameter for respective urine and blood samples collected at same time. NIR spectra of blood and milk samples were obtained by InfraAlyzer 500 spectrophotometer, using a transflectance mode. NIR spectra of urine samples were obtained by NIR System 6500 spectrophotometer, using 1 mm sample thickness. All samples were divided into calibration set and test set. Class variable was assigned for each sample as follow: healthy (class 1) and mastitic (class 2), based on milk SCC content. SIMCA was implemented to create models of the respective classes based on NIR spectra of milk, blood or urine. For the calibration set of samples, SIMCA models (model for samples from healthy cows and model for samples from mastitic cows), correctly classified from 97.33 to 98.67% of milk samples, from 97.33 to 98.61% of urine samples and from 96.00 to 94.67% of blood samples. From samples in the test set, the percent of correctly classified samples varied from 70.27 to 89.19, depending mainly on spectral data pretreatment. The best results for all data sets were obtained when first derivative spectral data pretreatment was used. The incorrect classified samples were 5 from milk samples,5 and 4 from urine and blood samples, respectively. The analysis of changes in the loading of first PC factor for group of samples from healthy cows and group of samples from mastitic cows showed, that separation between classes was indirect and based on influence of mastitis on the milk, blood and urine components. Results from the present investigation showed that the changes that occur when a cow gets mastitis influence her milk, urine and blood spectra in a specific way. SIMCA allowed extraction of available spectral information from the milk, urine and blood spectra connected with mastitis. The obtained results could be used for development of a new method for mastitis detection.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1273-1273
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• 2001

The efficiency of the luminal fermentation process influences overall efficiency of luminal production, animal health and reproduction. Ruminant production systems have a significant impact on the global environment, as well. Animal wastes contribute to pollution of the environment as ammonia volatilized to the air and nitrate leached to ground water. Microbial protein synthesis in the rumen satisfies a large proportion of the protein requirements of animals. Quantifying the microbial synthesis is possible by using markers for lumen bacteria and protozoa such as nucleic acids, purine bases, some specific amino acids, or by isotopic $^{15}N,^{32}P,\;and\;^{35}S$ labelled feeds. All those methods require cannulated animals, they are time-consuming and some methods are very expensive as well. Many attempts have been made to find an alternative method for indirect measurement of microbial synthesis in intact animals. The present investigations aimed to assess possibilities of NIRS for prediction of purine nitrogen excretion and ruminal microbial nitrogen synthesis by NIR spectra of urine. Urine samples were collected from 12 growing sheep,6 of them male, and 6- female. The sheep were included in feeding experiment. The ration consisted of sorghum silage and protein supplements -70:30 on dry matter basis. The protein supplements were chosen to differ in protein degradability. The urine samples were collected daily in a vessel containing $60m{\ell}$ 10% sulphuric acid to reduce pH below 3 and diluted with tap water to 4 liters. Samples were stored in plastic bottles and frozen at $-20^{\circ}C$ until chemical and NIRS analysis. The urine samples were analyzed for purine derivates - allantoin, uric acid, xantine and hypoxantine content. Microbial nitrogen synthesis in the lumen was calculated according to Chen and Gomes, 1995. Transmittance urine spectra with sample thickness 1mm were obtained by NIR System 6500 spectrophotometer in the spectral range 1100-2500nm. The calibration was performed using ISI software and PLS regression, respectively. The following statistical results of NIRS calibration for prediction of purine derivatives and microbial protein synthesis were obtained.(Table Omitted). The result of estimation of purine nitrogen excretion and microbial protein synthesis by NIR spectra of urine showed accuracy, adequate for rapid evaluation of microbial protein synthesis for a large number of animals and different diets. The results indicate that the advantages of the NIRS technology can be extended into animal physiological studies. The fast and low cost NIRS analyses could be used with no significant loss of accuracy when microbial protein synthesis in the lumen and the microbial protein flow in the duodenum are to be assessed by NIRS.

• The Journal of Engineering Geology
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• v.27 no.4
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• pp.429-438
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• 2017

Monte Carlo N-Particle (MCNP) modeling algorithm based on the Monte Carlo method was used to perform the simulation of neutron logging in order to increase the reliability and utilization of neutron logs applied in geological and resource engineering fields. To perform the simulation using MCNP, we used a realistic three-dimensional configuration of neutron sonde and formation. Validation of the modeling was confirmed by comparing the calibration curves of sonde manufacture with those calculated by MCNP modeling. After the validation, lithology effects, pore fluid effects, borehole diameter change, casing effect, and effects of borehole water level were investigated through modeling experiments. Numerical tests indicate that changes in neutron count ratio according to the lithology were quantitatively understood. In case of a borehole with a diameter of 3 inches, ratio of counting rates was higher than expected to be interpreted as borehole fluid has small effects on neutron logging. Effect of casing was also small in general, particular when porosity increases. Since modeling results above the groundwater level showed a tendency opposite to those below the groundwater level, neutron logs can be used to detect groundwater level. The modeling results simulated in this study for various borehole environments are expected to be used for data processing and interpretation of neutron log.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.217-217
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• 2015

The objective of this study was mainly to evaluate the water resources potential of Lake Tana Basin (LTB) by using Soil and Water Assessment Tool (SWAT). From SWAT simulation of LTB, about 5236 km2 area of LTB is gauged watershed and the remaining 9878 km2 area is ungauged watershed. For calibration of model parameters, four gauged stations were considered namely: Gilgel Abay, Gummera, Rib, and Megech. The SWAT-CUP built-in techniques, particle swarm optimization (PSO) and generalized likelihood uncertainty estimation (GLUE) method was used for calibration of model parameters and PSO method were selected for the study based on its performance results in four gauging stations. However the level of sensitivity of flow parameters differ from catchment to catchment, the curve number (CN2) has been found the most sensitive parameters in all gauged catchments. To facilitate the transfer of data from gauged catchments to ungauged catchments, clustering of hydrologic response units (HRUs) were done based on physical similarity measured between gauged and ungauged catchment attributes. From SWAT land use/ soil use/slope reclassification of LTB, a total of 142 HRUs were identified and these HRUs are clustered in to 39 similar hydrologic groups. In order to transfer the optimized model parameters from gauged to ungauged catchments based on these clustered hydrologic groups, this study evaluates three parameter transfer schemes: parameters transfer based on homogeneous regions (PT-I), parameter transfer based on global averaging (PT-II), and parameter transfer by considering Gilgel Abay catchment as a representative catchment (PT-III) since its model performance values are better than the other three gauged catchments. The performance of these parameter transfer approach was evaluated based on values of Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2). The computed NSE values was found to be 0.71, 0.58, and 0.31 for PT-I, PT-II and PT-III respectively and the computed R2 values was found to be 0.93, 0.82, and 0.95 for PT-I, PT-II, and PT-III respectively. Based on the performance evaluation criteria, PT-I were selected for modelling ungauged catchments by transferring optimized model parameters from gauged catchment. From the model result, yearly average stream flow for all homogeneous regions was found 29.54 m3/s, 112.92 m3/s, and 130.10 m3/s for time period (1989 - 2005) for region-I, region-II, and region-III respectively.