• Polymer(Korea)
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• v.26 no.1
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• pp.80-87
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• 2002

A comparative study using electron-beam(EB) and thermal curing techniques was carried out to determine the effect of cure behavior and thermal stability of epoxy resins. In this work, benzylquinoxalinium hexafluoroantimonate(BQH) was used as a latent cationic catalyst for an epoxy resin. According to the thermogravimetric analysis(TGA), the decomposed activation energy based on Coats-Redfern method was higher in the case of thermal curing technique. This could be interpreted in terms of slow thermal diffusion rate resulted from high crosslink density of the thermally cured epoxy resin. However, the increase of hydroxyl group in the epoxy resin cured by EB technique was observed in near-infrared spectroscopy(NIRS) measurements, resulting in improving the stable short aromatic chain structure, integral procedural decomposition temperature, and finally ductile properties for high impact strengths.

• YAKHAK HOEJI
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• v.42 no.4
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• pp.359-363
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• 1998

Near infrared reflectance spectroscopy (NIRS) was applied for discrimination according to geographical origin (Korea, China) of herb drugs. Herbal medicine has an important role in clinical therapy in Asian countries such as Korea and China. The objective of this study is to provide a convenient and accurate method to determinate geographical origin (Korea, China) of herbal medicine for quality control whose quality is generally different according to geographical origin. A rapid, nondestructive and accurate discrimination was achieved by NIRS. Second derivative spectra of herb drugs were subjected to discriminant analysis. Herbal medicine drugs such as Cassia Semen, Ganoderma and Smilacis Rhizoma was discriminated accurately according to geographical origin using PLS regression method.

• Proceedings of the Korean Institute of Landscape Architecture Conference
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• 2007.10b
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• pp.214-219
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• 2007

Psychological and physiological effects of plants were studied by investigating human responses while observing plants. Eighteen healthy adult male(aged between $19{\sim}25$ years) participated in this study. Semantic differential method(SD method) and multi-channel near-infrared spectroscopy(NIRS) were used to survey verbal and non-verbal response, respectively. Cerebral hemodynamics as a new evaluation index of brain activity was recorded for right brain hemisphere where visual information is mainly delivered. Thirty seconds of cerebral blood flow in forty seven channels were calculated when watching five types of picture images with different rates of hedge against gray block wall; 0:10, 3:7, 5:5, 7:3, 10:0. In the SD results, similar evaluations were found in all subjects. However, the change of cerebral hemodynamics as a non-verbal response varied among subjects. Largely two patterns of hemodynamics change were found with increasing plants rate in picture images; group A showed significant decreases of blood flow volume in many cortical regions, Group B had significant increase of blood flow volume in the occipital region for the scenes seen comparatively more plant. Our findings on the cerebral hemodynamics may indicate that there are two patterns of brain activity during observation of plants; group A in which brain areas associated with visual information and thinking work simultaneously to the visual stimuli; group B in which brain areas associated only with visual information work.

• Journal of Korea Multimedia Society
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• v.24 no.2
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• pp.295-304
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• 2021

Learning assistance system that continuously measures user's concentration will be helpful to grasp the concentration pattern and adjust the learning method accordingly to improve the learning efficiency. Although a lot of various learning aids have been proposed, there have been few studies on the concentration monitoring system in real time. Therefore, in this study, we developed an Android-based mobile application that can measure concentration during study by using functional near-infrared spectroscopy, which is used to measure brain activity. First, the task accuracy was predicted at a maximum level of 93.75% from the prefrontal oxygenation characteristics measured while performing the visual Q&A task on 11 college students, and a concentration calculation formula based on a linear regression model was derived. Then, a survey on the usability of the mobile application was conducted, overall high satisfaction and positive opinions were obtained. From these findings, this application can be used as a customized learning aid application for users, and further, it can help educators improve the quality of classes based on the level of concentration of learners.

• Journal of The Korean Society of Grassland and Forage Science
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• v.26 no.1
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• pp.53-62
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• 2006

Near infrared reflectance spectroscopy (NIRS) has been increasingly used as a rapid, accurate method of evaluating some chemical compositions in forages. Analysis of forage quality by NIRS usually involves dry ground samples. Costs might be reduced if samples could be analyzed without drying or grinding. The objective of this study was to investigate effect of sample preparations and spectral math treatments on prediction ability of chemical composition for corn silage by NIRS. A population of 112 corn silage representing a wide range in chemical parameters were used in this investigation. Samples of com silage were scanned at 2nm intervals over the wavelength range 400-2500nm and the optical data recorded as log l/Reflectance(log l/R) and scanned in overt-dried grinding(ODG), liquid nitrogen grinding(LNG) or intact fresh(IF) condition. Samples were analysed for neutral detergent fiber(NDF), acid detergent fiber(ADF), acid detergent lignin(ADL), crude protein(CP) and crude ash content were expressed on a dry-matter(DM) basis. The spectral data were regressed against a range of chemical parameters using modified partial least squares(MPLS) multivariate analysis in conjunction with four spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation(SECV). The results of this study show that NIRS predicted the chemical parameters with very high degree of accuracy(the correlation coefficient of cross validation$(R^2cv)$ range from $0.70{\sim}0.95$) in ODG. The optimum equations were selected on the basis of minimizing the standard error of prediction(SEP). The Optimum sample preparation methods and spectral math treatment were for ADF, the ODG method using 2,10,5 math treatment(SEP = 0.99, $R^2v=0.93$), and for CP, the ODG method using 1,4,4 math treatment(SEP = 0.29. $R^2v=0.91$).

• Korean Journal of Plant Resources
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• v.31 no.5
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• pp.498-514
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• 2018

A statistical analysis for 3651 genetic resources collected from China (1,542), Japan (1,409), Korea (413), and India (287) was conducted using normal distribution, variability index value (VIV), analysis of variation (ANOVA) and Ducan's multiple range test (DMRT) based on a data obtained from NIRS analysis. In normal distribution, the average protein content was 8.0%, whereas waxy type amylose and common rice amylose were found to be 8.7% and 22.7%, respectively. The protein contents ranged from 5.4 to 10.6% at the level of 95%. The waxy amylose and common rice amylose ranged from 5.9 to 11.5%, and from 16.9 to 28.5% at 95% confidence level, respectively. The VIV was 0.59 for protein, 0.64 for low amylose, and 0.81 for high amylose contents. The average amylose contents were 18.85% in Japanese, 19.99% in Korean, 20.27% in Chinese, and 25.46% in Indian resources, while the average protein contents were found to be 7.23% in Korean, 7.73% in Japanese, 8.01% in Chinese, and 8.17% in Indian resources. The ANOVA of amylose and protein content showed significant differences at the level of 0.01. The F-test for amylose content was 158.34, and for protein content 53.95 compared to critical value 3.78. The DMRT of amylose and protein content showed significant difference (p<0.01) between resources of different countries. Japanese resources had the lowest level of amylose contents, whereas, the lowest level of protein content was found in Korean resources compared to other origins. Indian resources showed the highest level of amylose and protein contents. It is recommended these results should be helpful to future breeding experiments.

• 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.

• Korean Journal of Food Science and Technology
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• v.35 no.5
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• pp.818-824
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• 2003

Capillary electrophoresis (CE) and near-infrared spectroscopy (NIRS) were performed to discriminate astragalus roots (Astragalus membranaceus) according to geographical origin (domestic or foreign). Two-hundred-and-four astragalus roots were extracted with 30% methanol in 0.1 M phosphate buffer (pH 2.5) and separated in a uncoated fused-silica $(50\;{\mu}m{\times}27\;cm)$ capillary. Conditions for optimal analysis included: temperature $-45^{\circ}C$, voltage -14 kV, and pressure injection time -8 sec. The optimal separation buffer was 0.1 M phosphate buffer (pH 2.5) containing 40 mM hexane sulfonic acid with 20% 2-methoxy ethanol. Raw NIR spectra were obtained using NIRS, and modified partial least square regression was used to develop the prediction model. The correlation coefficient and standard error of prediction were 0.915 and 14.3%, respectively. Under the optimal conditions established for CE and NIRS, the geographical origins of the astragalus roots were correctly identified in 80 and 97%, respectively. Astragalus roots that were not discriminated by NIRS were correctly discriminated by CE. Hence, CE and NIRS are potential methods for discriminating the geographical origins of astragalus roots that complement one another.

• Journal of The Korean Society of Grassland and Forage Science
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• v.32 no.3
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• pp.301-308
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• 2012

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of Italian ryegrass silages. A population of 267 Italian ryegrass silages representing a wide range in chemical parameters and fermentative characteristics was used in this investigation. Samples of silage were scanned at 2 nm intervals over the wavelength range 680~2,500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and 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 on the basis of the highest coefficients of determination in cross validation ($R^2$) 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 $R^2$ and SECV were 0.98 (SECV 1.27%) for moisture, 0.88 (SECV 1.26%) for ADF, 0.84 (SECV 2.0%), 0.93 (SECV 0.96%) for CP and 0.78 (SECV 0.56), 0.81 (SECV 0.31%), 0.88 (SECV 1.26%) and 0.82 (SECV 4.46) for pH, lactic acid, TDN and RFV on a dry matter (%), respectively. Results of this experiment showed the possibility of NIRS method to predict the chemical composition and fermentation quality of Italian ryegrass silages as routine analysis method in feeding value evaluation and for farmer advice.

• Korean Journal of Plant Resources
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• v.32 no.2
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• pp.124-143
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• 2019

This study was carried out to build a database system for amylose and protein contents of rice germplasm based on NIRS (Near-Infrared Reflectance Spectroscopy) analysis data. The average waxy type amylose contents was 8.7% in landrace, variety and weed type, whereas 10.3% in breeding line. In common rice, the average amylose contents was 22.3% for landrace, 22.7% for variety, 23.6% for weed type and 24.2% for breeding line. Waxy type resources comprised of 5% of the total germplasm collections, whereas low, intermediate and high amylose content resources share 5.5%, 20.5% and 69.0% of total germplasm collections, respectively. The average percent of protein contents was 8.2 for landrace, 8.0 for variety, and 7.9 for weed type and breeding line. The average Variability Index Value was 0.62 in waxy rice, 0.80 in common rice, and 0.51 in protein contents. The accession ratio in arbitrary ranges of landrace was 0.45 in amylose contents ranging from 6.4 to 8.7%, and 0.26 in protein ranging from 7.3 to 8.2%. In the variety, it was 0.32 in amylose ranging from 20.1 to 22.7%, and 0.51 in protein ranging from 6.1 to 8.3%. And also, weed type was 0.67 in amylose ranging from 6.6 to 9.7%, and 0.33 in protein ranging from 7.0 to 7.9%, whereas, in breeding line it was 0.47 in amylose ranging from 10.0 to 12.0%, and 0.26 in protein ranging from 7.0 to 7.9%. These results could be helpful to build database programming system for germplasm management.