• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.100-100
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• 2020

Wheat is mainly composed of carbohydrate but it contains a moderate amount of protein, which gives a very useful characteristics to flour food such as the unique elasticity and stickiness of the dough. We developed a calibration equation for analyzing crude protein content using Near Infrared Spectroscopy to quick analyze the crude protein content of wheat germplasm stored in the National Agrobiodiversity Center, RDA, Korea. The 1,798 wheat germplasms were used to draw up the calibration formula. The crude protein's interval distribution of 1,798 wheat germplasms used for the calibration was 7.04-20.84%, the average content was 13.2%, and standard deviation was 2.6%. The germplasms distribution was composed of a suitable group for the preparation of the calibration formula because the content distribution was a normal, excluding the 13.0-15.5% content section. In order to verify the applicability of the NIRS prediction model, we measured the crude protein content of the 300 wheat germplasms that were not used for the calibration using both Kjeldahl analysis and NIR spectrum. The analysis value calculated using each method were statistically processed, and the test results and statistical indicators of the predictive model were compared. As a result, The R² value of the optimized NIRS prediction model was 0.997, and the Standard error of Calibration value(SEC) was 0.132, and slope value was 1.000. With prediction model selection, compared to Kjeldahl method, R² values were 0.994(Kjeldahl), 0.998(NIRS), and the SEC value were 0.191 and 0.132, respectively, comparing the statistical indices of the forecast model. And slope value were 1.013, 1.000, respectively. The analysis of crude protein content by the NIRS predictive model developed by each statistical index showing similar figures is judged to show a high degree of correlation with the Kjeldahl analysis. The proven calibration equation will be used to measure the crude protein content of wheat germplasms held by the National Agrobiodiversity Center, and by dividing the wheat germplasms by their use according to the crude protein content, it will provide useful information to relevant researchers.

• Journal of Surface Science and Engineering
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• v.51 no.2
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• pp.126-132
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• 2018

SnS thin films with different substrate temperatures ($150 {\sim}300^{\circ}C$) as process parameters were grown on soda-lime glass substrates by RF magnetron sputtering. The effects of substrate temperature on the structural and optical properties of SnS thin films were investigated by X-ray diffraction (XRD), Raman spectroscopy (Raman), field-emission scanning electron microscopy (FESEM), energy dispersive X-ray spectroscopy (EDS), and Ultraviolet-visible-near infrared spectrophotometer (UV-Vis-NIR). All of the SnS thin films prepared at various substrate temperatures were polycrystalline orthorhombic structures with (111) planes preferentially oriented. The diffraction intensity of the (111) plane and the crystallite size were improved with increasing substrate temperature. The three major peaks (189, 222, $289cm^{-1}$) identified in Raman were exactly the same as the Raman spectra of monocrystalline SnS. From the XRD and Raman results, it was confirmed that all of the SnS thin films were formed into a single SnS phase without impurity phases such as $SnS_2$ and $Sn_2S_3$. In the optical transmittance spectrum, the critical wavelength of the absorption edge shifted to the long wavelength region as the substrate temperature increased. The optical bandgap was 1.67 eV at the substrate temperature of $150^{\circ}C$, 1.57 eV at $200^{\circ}C$, 1.50 eV at $250^{\circ}C$, and 1.44 eV at $300^{\circ}C$.

• Journal of Biosystems Engineering
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• v.37 no.3
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• pp.177-183
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• 2012

Purpose: In near infrared spectroscopy, interactance configuration of a light source and a spectrometer probe can provide more information regarding fruit internal attributes, compared to reflectance and transmittance configuration. However, there is no through study on the parameters of interactance measurement setup. The objective of this study was to investigate the effect of the parameters on the estimation of soluble solids content (SSC) and firmness of muskmelons. Methods: Melon samples were taken from greenhouses at three different harvesting seasons. The prediction models were developed at three distances of 2, 5, and 8 cm between the light source and the spectrometer probe, three measurement points of 2, 3, and 6 evenly distributed on each sample, and different number of fruit samples for calibration models. The performance of the models was compared. Results: In the test at the three distances, the best results were found at a 5 cm distance. The coefficient of determination ($R_{cv}{^2}$) values of the cross-validation were 0.717 (standard error of prediction, SEP=$1.16^{\circ}Brix$) and 0.504 (SEP=4.31 N) for the estimation of SSC and firmness, respectively. The minimum measurement point required to fully represent the spectral characteristics of each fruit sample was 3. The highest $R_{cv}{^2}$ values were 0.736 (SEP=$0.87^{\circ}Brix$) and 0.644 (SEP=4.16 N) for the estimation of SSC and firmness, respectively. The performance of the models began to be saturated when 60 fruit samples were used for developing calibration models. The highest $R_{cv}{^2}$ of 0.713 (SEP=$0.88^{\circ}Brix$) and 0.750 (SEP=3.30 N) for the estimation of SSC and firmness, respectively, were achieved. Conclusions: The performance of the prediction models was quite different according to the condition of interactance measurement setup. In designing a fruit grading machine with interactance configuration, the parameters for interactance measurement setup should be chosen carefully.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.53 no.1
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• pp.15-20
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• 2008

Near infrared reflectance spectroscopy (NIRS) is a rapid and accurate analytical method for determining the composition of agricultural products and feeds. This study was conducted to measure anthocyanin contents in black colored soybean by using NIRS system. Total 300 seed coat of black colored soybean samples previously analyzed by HPLC were scanned by NIRS and over 250 samples were selected for calibration and validation equation. A calibration equation calculated by MPLS(modified partial least squares) regression technique was developed in which the coefficient of determination for anthocyanin pigment C3G, D3G and Pt3G content was 0.952, 0.936, and 0.833, respectively. Each calibration equation was applied to validation set that was performed with the remaining samples not included in the calibration set, which showed high positive correlation both in C3G and D3G content file. In case Pt3G, the prediction model was needed more accuracy because of low $R^2$ value in validation set. This results demonstrate that the developed NIRS equation can be practically used as a rapid screening method for quantification of C3G and D3G contents in black colored soybean.

• Journal of The Korean Society of Grassland and Forage Science
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• v.39 no.4
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• pp.258-263
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• 2019

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. The objective of this study was to evaluate the potential of NIRS, applied to imported forage, to estimate the moisture and chemical parameters for imported hays. A population of 392 imported hay representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1 nm intervals over the wavelength range 680-2500nm and the optical data was recorded as log 1/Reflectance(log 1/R), which scanned in intact fresh condition. The spectral data were regressed against a range of chemical parameters using partial least squares(PLS) multivariate analysis in conjunction with spectral math treatments to reduced the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation(R²) and the lowest standard error of cross-validation(SECV). The results of this study showed that NIRS predicted the chemical parameters with very high degree of accuracy. The R² and SECV for imported hay calibration were 0.92(SECV 0.61%) for moisture, 0.98(SECV 0.65%) for acid detergent fiber, 0.97(SECV 0.40%) for neutral detergent fiber, 0.99(SECV 0.06%) for crude protein and 0.97(SECV 3.04%) for relative feed value on a dry matter(%), respectively. Results of this experiment showed the possibility of NIRS method to predict the moisture and chemical composition of imported hay in Korea for routine analysis method to evaluate the feed value.

• Journal of the Korean Wood Science and Technology
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• v.39 no.4
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• pp.370-380
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• 2011

This study was carried out to investigate the properties of soils in the sampling green tea farms and the chemical constituents of the green tea leaves at the 3 harvest stages (Ujeon ${\rightarrow}$ Sejag ${\rightarrow}$ Jungjag). The results as follows; The properties of the soils were sandy loam, loam, well drained and fertile-rich in phosphoric acids in general. The contents of chlorophyll, tannin, vitamin-c and total catechin were increased as harvest periods getting late but the contents of total nitrogen, free amino acids, theanine, caffeine were decreased on the reverse. The inorganic constituents Mg, Ca and Mn were increased as harvest periods getting late but the Na, K, B contents were decreased on the reverse. The contents of the total nitrogen, chlorophyll, total free amino acid, theanine, caffeine and total catechin and Na, Mg, Ca, B and Se were insignificant differences between Ujeon and Sejag.

• Current Photovoltaic Research
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• v.8 no.1
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• pp.33-38
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• 2020

Single-phased SnS thin films have been prepared by RF magnetron sputtering at various deposition pressures. The effect of deposition pressure on the structural and optical properties of polycrystalline SnS thin films was studied using X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), X-ray photoelectron spectroscopy (XPS) and ultraviolet-visible-near infrared (UV-Vis-NIR) spectrophotometer. The XRD analysis revealed the orthorhombic structure of the SnS thin films oriented along the (111) plane direction. As the deposition pressure was increased from 5 mTorr to 15 mTorr, the intensity of the peak on the (111) plane increased, and the intensity decreased under the condition of 20 mTorr. The binding energy difference at the Sn 3d_5/2 and S 2p_3/2 core levels was about 324.5 eV, indicating that the SnS thin film was prepared as a pure Sn-S phase. The optical properties of the SnS thin films indicate the presence of direct allowed transitions with corresponding energy band gap in the rang 1.47-1.57 eV.

• Korean Journal of Materials Research
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• v.29 no.5
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• pp.282-287
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• 2019

The photovoltaic properties of $TiO_2$ used for the electron transport layer in perovskite solar cells(PSCs) are compared according to the particle size. The PSCs are fabricated and prepared by employing 20 nm and 30 nm $TiO_2$ as well as a 1:1 mixture of these particles. To analyze the microstructure and pores of each $TiO_2$ layer, a field emission scanning electron microscope and the Brunauer-Emmett-Teller(BET) method are used. The absorbance and photovoltaic characteristic of the PSC device are examined over time using ultraviolet-visible-near-infrared spectroscopy and a solar simulator. The microstructural analysis shows that the $TiO_2$ shape and layer thicknesses are all similar, and the BET analysis results demonstrate that the size of $TiO_2$ and in surface pore size is very small. The results of the photovoltaic characterization show that the mean absorbance is similar, in a range of about 400-800 nm. However, the device employing 30 nm $TiO_2$ demonstrates the highest energy conversion efficiency(ECE) of 15.07 %. Furthermore, it is determined that all the ECEs decrease over time for the devices employing the respective types of $TiO_2$. Such differences in ECE based on particle size are due to differences in fill factor, which changes because of changes in interfacial resistance during electron movement owing to differences in the $TiO_2$ particle size, which is explained by a one-dimensional model of the electron path through various $TiO_2$ particles.

• Food Science and Preservation
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• v.19 no.1
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• pp.95-103
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• 2012

The objective of this study was to develop models for the predict of the milk properties (fat, protein, SNF, lactose, MUN) of unhomogenized milk using the visible and near-infrared (NIR) spectroscopic technique. A total of 180 milk samples were collected from dairy farms. To determine optimal measurement temperature, the temperatures of the milk samples were kept at three levels ($5^{\circ}C$, $20^{\circ}C$, and $40^{\circ}C$). A spectrophotometer was used to measure the reflectance spectra of the milk samples. Multilinear-regression (MLR) models with stepwise method were developed for the selection of the optimal wavelength. The preprocessing methods were used to minimize the spectroscopic noise, and the partial-least-square (PLS) models were developed to prediction of the milk properties of the unhomogenized milk. The PLS results showed that there was a good correlation between the predicted and measured milk properties of the samples at $40^{\circ}C$ and at 400~2,500 nm. The optimal-wavelength range of fat and protein were 1,600~1,800 nm, and normalization improved the prediction performance. The SNF and lactose were optimized at 1,600~1,900 nm, and the MUN at 600~800 nm. The best preprocessing method for SNF, lactose, and MUN turned out to be smoothing, MSC, and second derivative. The Correlation coefficients between the predicted and measured fat, protein, SNF, lactose, and MUN were 0.98, 0.90, 0.82, 0.75, and 0.61, respectively. The study results indicate that the models can be used to assess milk quality.

• Resources Recycling
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• v.26 no.6
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• pp.73-83
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• 2017

Used small household appliances have a wide variety of product types and component materials, and contain high percentage of black plastics. However, they are not being recycled efficiently as conventional sensors such as near-infrared ray (NIR), etc. are not able to detect black plastic by types. In the present study, an automatic sorting system was developed based on laser-induced breakdown spectroscopy (LIBS) to promote the recycling of waste plastics. The system we developed mainly consists of sample feeder, automatic position recognition system, LIBS device, separator and control unit. By applying laser pulse on the target sample, characteristic spectral data can be obtained and analyzed by using CCD detectors. The obtained data was then treated by using a classifier, which was developed based on artificial intelligent algorithm. The separation tests on waste plastics also were carried out by using a lab-scale automatic sorting system and the test results will be discussed. The classification rate of the radial basis neural network (RBFNNs) classifier developed in this study was about > 97%. The recognition rate of the black plastic by types with the automatic sorting system was more than 94.0% and the sorting efficiency was more than 80.0%. Automatic sorting system based on LIBS technology is in its infant stage and it has a high potential for utilization in and outside Korea due to its excellent economic efficiency.