본 연구의 목적은 기능적 근적외선 분광법인 fNIRS(functional Near-Infrared Spectroscopy)를 활용하여 미술표현에 따른 뇌 활성화를 측정하는 것이다. fNIRS는 소형의 크기에, 휴대성이 용이하고, 근적외선이 연구참여자의 전두엽에 비침습적으로 침투하여 대뇌의 혈류량을 파악하고, 이를 토대로 뇌의 활성화 상태를 측정하는 장비이다. K대학 소재의 연구참여자 4명을 대상으로 fNIRS 를 착용 후 1분 30초씩, 총 3번에 걸쳐서 미술표현 활동을 진행하였고, 개별 뇌 활성 이미지와 4명 의 평균 뇌 활성 이미지 및 그래프를 추출하였다. 분석결과 연구참여자 4명의 모든 미술표현 활동에서 시각적 작업기억을 담당하는 우측의 DLPFC영역, 메타인지와 평가 및 성찰을 담당하는 FPC 영역, 그리고 가치 부여 및 판단을 담당하는 OFC영역이 활성화 되었음을 확인하였다. 분석결과를 토대로 미술교육의 시사점 및 나아가야 할 방향을 제안하였다. 첫째, 미술활동은 우뇌와 좌뇌의 활성이 함께 일어나지만 비교적 우뇌가 더 많이 활성화 된다. 둘째, 미술표현활동은 주의집중과 목표지향적 행동, 동기부여를 유발하는 교과이다. 셋째, 미술표현 활동에서 자기평가와 성찰을 위해 표현 후 결과물에 대해 발표 및 소감문을 작성해 보는 시간이 필요하다. 넷째, 미술할동에서 주제를 제시할 때 막연하고 추상적인 주제가 아니라 구체적인 언어와 단어로써 제시해야 한다. 본 연구를 토대로 앞으로 미술교과와 관련된 과학적이고 객관적, 실증적인 연구가 많이 진행되어 미술교과의 위상을 높이고, 더 좋은 후속연구들이 이루어지기를 기대한다.

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.

This study was conducted to characterize the amylose and protein contents of 4,948 rice landrace germplasm using the NIRS model developed in the previous study. The average amylose content of the germplasm was 20.39% and ranged between 3.97 and 37.13%. The amylose contents in the standard rice were 4.99, 18.63 and 20.55% in Sinseonchal, Chucheong and Goami, respectively. The average protein content was 8.17% and ranged from 5.20 to 17.45%. Protein contents in Sinseonchal, Chucheong and Goami were 6.824, 6.869 and 7.839%, respectively. A total of 62% germplasm were distributed between 20.06% and 27.02% in amylose content. Germplasm of 81.60% represented protein content of 6.78-9.75%. The distinguishable ranges of amylose contents according to origin were 16.58-20.06% in Korea, 20.06-23.25% in Japan, 23.25-27.02% in North Korea, and 27.02-37.13% in China. In the protein content, approximately 30% of Chinese resources ranged from 9.75 to 17.45%, whereas less than 10% were detected in other origin accessions. Fifty resources were selected with low and high amylose ranging from 3.97-6.66% and 30.41-37.13%, respectively. Similarly, fifty resources were selected with low and high protein ranging from 5.20-6.09% and 13.21-17.45%, respectively. Landraces with higher protein could be adapted to practical utilization of food sources.

본 연구에서는 전통 재래식 콩된장의 제조공정을 과학적으로 해석하기 위한 연구의 일환으로 생메주의 건조 및 발효과정에 있어서 수분함량과 단백질 분해에 의한 아미노산 생성과정을 근적외 분광법으로 쉽게 판단 할 수 있는지 그 가능성을 조사하였다. 생메주를 일정한 크기(3×4×3 cm)로 잘라서 수분이 30%가 되도록 25℃ 항온기에 3주간 발효시킨 모델계 발효메주를 만들어 가시광-근적외 스펙트럼을 측정하여 해석하였다. 발효가 진행됨에 따라 수분이 감소되면서 수분과 관련이 있는 1,430 nm대의 흡광도가 감소 및 시프트 되는 것이 관찰되었고, 발효기간이 길어질수록 아미노산과 관련이 있는 2,050 nm대의 흡광도가 증가하는 것을 관찰 할 수 있었다. 전통 재래식 발효메주를 속부분과 겉부분으로 분리하여 1,000-2,250 nm에서의 가시광 스펙트럼과 근적외 스펙트럼 데이터로 판별분석한 결과, 겉부분과 속부분이 잘 판별되었고, 가시광 보다는 근적외 영역에서 잘 구분되었다. 이들 전통메주중의 아미노산 농도를 비파괴 측정하기 위해 PLSR한 결과 R²은 0.9 이상이며, RMSECv 0.23%로 나타났으며, 전통메주중의 수분함량도 R² 0.81, RMSECv 0.83%이어서 근적외 분광법으로 전통메주의 품질을 비파괴 측정 가능할 것으로 판단되었다.

The objective of this research was to develop Near-Infrared Reflectance Spectroscopy (NIRS) model for amylose and protein contents analysis of large accessions of rice germplasm. A total of 511 accessions of rice germplasm were obtained from National Agrobiodiversity Center to make calibration equation. The accessions were measured by NIRS for both brown and milled brown rice which was additionally assayed by iodine and Kjeldahl method for amylose and crude protein contents. The range of amylose and protein content in milled brown rice were 6.15-32.25% and 4.72-14.81%, respectively. The correlation coefficient (R 2 ), standard error of calibration (SEC) and slope of brown rice were 0.906, 1.741, 0.995 in amylose and 0.941, 0.276, 1.011 in protein, respectively, whereas R 2 , SEC and slope of milled brown rice values were 0.956, 1.159, 1.001 in amylose and 0.982, 0.164, 1.003 in protein, respectively. Validation results of this NIRS equation showed a high coefficient determination in prediction for amylose (0.962) and protein (0.986), and also low standard error in prediction (SEP) for amylose (2.349) and protein (0.415). These results suggest that NIRS equation model should be practically applied for determination of amylose and crude protein contents in large accessions of rice germplasm.

Near infrared reflectance spectroscopy (NIRS) has been used as a rapid analysis tool to many components in cereal grains. This study was to investigate the potential NIRS application for determination of components in Korean wheat. Main components of wheat quality are protein content, moisture content, SDS-sedimentation volume and ash content. Wheat has screened for quality, hardness of seed by NIRS in CIMMYT (International Maize and Wheat Improvement Center). NIRS calibration was used as a rapid and simultaneous analysis method to determine the wheat quality components. A total of 282 wheat samples, collected from a wide range of Korean wheat cultivation region for 2 years, were analyzed by NIRS. NIRS calibration of individual components were developed using first derivation, second derivation and modified partial least-squares regression and internal cross validation method. As a result, calibration formula of protein was y=0.937x+0.786, calibration formula of moisture was y=0.922x+0.911, calibration formula of ash was y=0.933x+0.08, calibration formula of SDS-sedimentation volume was y=0.947x+2.150. NIRS calibration for wheat quality may be useful for determining protein(R2 = 93.6), moisture(R2 = 91.6), SDS-sedimentation volume(R2 = 94.3), and ash(R2 = 93.4). This study shows that 4 calibrations of NIRS is a useful application in the accurate and rapid determination of wheat quality. Therefore, NIRS could be used to rapidly determine the quality contents of wheat for grade evaluation in a purchasing of wheat cultivation region.

This study was investigated to develop mass evaluation system for the contents of crude protein, oil and fatty acid in soybean germplasm using NIRS. NIRS equations were created with 345 soybeans, multiple correlation coefficients of crude protein, oil, palmitic, stearic, oleic, linoleic and linolenic acid between data obtained from NIRS and quantitative analysis were 0.983, 0.969, 0.592, 0.514, 0.978, 0.961 and 0.957, respectively. Equation statistics indicated that contents of crude protein, oil and unsaturated fatty acid except palmitic and stearic acid in soybean seed were suitable for determination by NIRS. Those NIRS equations were applied to examine crude protein, oil and unsaturated fatty acid of 854 soybean landraces from Korea. The average contents and ranges of crude protein and oil were 39.2% with a range of 33.7-47.0% and 15.0% with a range of 9.8-20.3%, individually. In addition, those of oleic, linoleic and linolenic acid were 21.4% with a range of 12.1-30.2%, 55.6% with a 47.8-62.3% and 8.1% with a range of 5.9-10.7% respectively. We conducted quantitative analysis to reconfirm with IT154552 (45.1%) and IT023955(46.9%) above 45% of crude protein, the results were similar from NIRS (45.2%, 47.0%). NIRS data for protein from this study made no difference with lab data, which would be useful for mass evaluation. There was negative correlation (-0.203) between crude protein and oil, positive correlation (0.379) between crude oil and oleic acid, and significantly negative correlation (-0.879) between oleic and linoleic acid.

This study was conducted to develop a fast and efficient screening method to determine the quantity of fatty acid in peanut oil for high oleate breeding program. A total of 329 peanut samples were used in this study, 227 of which were considered in the calibration equation development and 102 were utilized for validation, using near infrared reflectance spectroscopy (NIRS). The NIRS equations for all the seven fatty acids had low standard error of calibration (SEC) values, while high R2 values of 0.983 and 0.991 were obtained for oleic and linoleic acids, respectively in the calibration equation. Furthermore, the predicted means of the two main fatty acids in the calibration equation were very similar to the means based on gas chromatography (GC) analysis, ranging from 36.7 to 77.1% for oleic acid and 7.1 to 42.7% for linoleic acid. Based on the standard error of prediction (SEP), bias values, and R 2 statistics, the NIRS fatty acid equations were accurately predicted the concentrations of oleic and linoleic acids of the validation sample set. These results suggest that NIRS equations of oleic and linoleic acid can be used as a rapid mass screening method for fatty acid content analysis in peanut breeding program.

Soybeans have been the favored livestock forage for centuries. However, little studies have been succeed in estimating forage quality of soybean by near-infrared reflectance spectroscopy (NIRS). To establish NIR equations for soybean forage quality, 353 forage soybean samples, including an 181 recombinant inbred line population derived from PI 483463 (G. soja) ´ Hutcheson (G. max), 104 cultivated soybeans (G. max) and 68 wild soybeans (G. soja) were used to develop NIR for four quality parameters: crude protein (CP), crude fat (CF), neutral detergent fiber (NDF), and acid detergent fiber (ADF). Two NIR spectroscopy equations developed for CP and CF (2,5,5,1; multiple scatter correction [MSC]) and for NDF and ADF (1,4,4,1; MSC) were the best prediction equations for estimating these parameters. The coefficients of determination in external validation set (r2) were 0.934 for CF, 0.909 for CP, 0.767 for NDF, and 0.748 for ADF. The relative predictive determinant (RPD) ratios for MSC (2,5,5,1) calibration indicate that the CP (3.34) and CF (3.45) equations were acceptable for quantitative prediction of soybean forage quality, whereas the NDF (2.34) and ADF (1.97) equations were useful for screening purposes. The NIR calibration equations developed in this study will be useful in predicting the contents of forage qualities and in breeding soybean for forage

Brown rice grain pigments of black rice have a higher content of bioactive substances such as anti-mutagenic substance than the non-pigmented rice grain. The major anthocyanin pigment contained in black rice was cyanidin-3-glucoside. This study was conducted to establish a rapid analysis method for determining cyanidin-3-glucoside contents in flour and whole rice seeds of black rice using VIS/NIRS technique. A total of 60 black rice samples were used for VIS/NIRS equation model development and validation. The value of coefficient of determination of external validation (r 2 ) and standard error of performance (SEP) in whole rice seed sample were 0.653 and 97.2, respectively. Therefore, the value of it seemed to be difficult to analyze cyanidin-3-glucoside content in whole rice seed samples using VIS/NIRS. However, in rice flour sample, the best accurate equation model was obtained from the partial least square regression (PLS) method. The value of r 2 , SEP and bias were 22.5, 0.922 and -1.45 in the calibration transformed to the N-point smooth of log 1/R signal, 5 factors, respectively. Therefore, the results of our study clearly demonstrate that the VIS/NIRS method would be applicable only for rapid determination of cyanidin-3-glucose content in black rice flour samples.

국내산 콩과 수입콩의 판별에 NIRS를 도입함으로써 보다 빠르고 정확한 식별분석을 하고자 실험을 수행하였다. NIRS를 사용하여 400~2,500 nm 범위에서 콩 분말의 파장을 측정하였으며, 측정된 spectrum은 WINISI II program 을 이용하여 수처리와 회귀분석을 하였다. 검량식 작성을 위한 수처리는 spectrum을 1차미분 및 4 nm gap으로 조정한 것이 가장 적합하였으며, 회귀식은 변형부분최소자승회귀법(Modified partial least squares regression)이 우수하였다. MPLS 회귀분석시 원산지 판별을 위해 loading value를 국내산 콩은 '100', 수입콩은 '1'로 처리하여 검량식을 작성하고 그 적합성을 검증한 결과 factor가 10일 때 도출된 calibration equation의 상관값이 0.98, 교차검증의 상관값이 0.94를 나타내어 상관도가 높음을 알 수 있었다. 따라서 NIRS를 이용한 국내산 콩과 수입콩의 판별분석이 가능할 것으로 판단되었다.

This experiment was carried out to find suitable sample type for a more accurate prediction and non-destructive way in the application of NIRS technique for estimation amino acid composition of soybean by comparing three different sample types, single seed, whole seeds, and milled seeds’ powder. The coefficient of determination in calibration and 1-VR of cross-validation for 17 amino acids analyzed by NIR using milled powder were highest, followed by single seed, and then whole seeds were the lowest. The R2 coefficients of determination in calibration for single bean perimeters were higher than those of whole beans and showed higher R2 coefficient than 0.8 with the exception of seven amino acids. On the other hand, calibration equation development for only glutamic acid analyzed NIRS data scanned with whole seeds showed higher R2 coefficient than 0.8. Eleven different amino acids, such as aspartic acid, threonine, serine, glutamine, glycine, alanine, valine, isoleucine, leucine, arginine and proline, of soybean seeds in the powder had higher R2 coefficient of determination in calibration than 0.8 and could be estimated for the most accurate prediction. However, judging from the results of single bean samples, in consideration on NIR application for more nondestructive and faster prediction, these amino acids using single bean sample could be estimated without grinding the seeds.

The objective of this study was to rapidly evaluate fatty acids in a collection of millet (Panicum miliaceum subsp. miliaceum) of different origins so that this information could be disseminated to breeders to advance germplasm use and breeding. To develop the calibration equations for rapid and nondestructive evaluation of fatty acid content, near-infrared reflectance spectroscopy (NIRs) spectra (1104-2494 nm) of samples ground into flour (n =100) were obtained using a dispersive spectrometer. A modified partial least-squares model was developed to predict each component. For foxtail millet germplasm, our models returned coefficients of determination (R2 ) of 0.89, 0.89, 0.89, and 0.92 for palmitic acid, oleic acid, linoleic acid, and total fatty acids, respectively. The prediction of the external validation set (n=10) showed significant correlation between references values and NIRs values (r2 =0.64, 0.90, 0.79, and 0.89 for palmitic acid, oleic acid, linoleic acid, and total fatty acids, respectively). Standard deviation/standard errors of cross-validation (SD/SECV) values were close to 3 (2.62, 2.40, 1.85, and 2.23 for palmitic acid, oleic acid, linoleic acid, and total fatty acids, respectively). These results indicate that these NIRs equations are functional for the mass screening and rapid quantification of the oleic and total fatty acids characterizing millet germplasm. Among the samples, IT153514 showed an especially high content of fatty acids (48.14mg~;g-1 ), whereas IT123909 had a very low content (34.44mg~;g-1 ).

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℃, 20℃, and 40℃). 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℃ 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.

This study was conducted to develop a portable quality evaluation system of bee-honey by near infrared spectroscopic technique. Two kinds of bee-honeys from acacia and polyflower sources were tested in this study. The system consists of power supply, tungsten-halogen lamp, detector, and optical fiber probe. Performance of the system was analyzed by comparing the prediction accuracy of the laboratory spectrophotometer. Total of 346 spectra was divided into a calibration set and a prediction set. The PLS (Partial Least Squares) models were developed to predict the quality parameters of bee-honeys. Reflectance spectra, moisture contents, ash, invert sugar, sucrose, F/G ratio, HMF(hydroxy methyl furfural), and ratio of honeys were measured. The PLS models of the laboratory spectrophotometer showed good relationships between predicted and measured quality parameters of honeys in the wavelength range of 1.100~2.200 nm. The PLS analysis of the portable quality evaluation system showed good relationships between predicted and measured quality parameters of honeys in the wavelength range of 1.100~1.300 nm and 1.400~1.700 nm. The results showed the feasibility of the portable quality evaluation system to determine the quality parameters of bee-honey in the field during harvesting.

This experiment was carried out to find suitable sample type for the more accurate prediction and non-destructive way in the application of near infrared reflectance spectroscopy (NIRS) technique for estimation the protein, total amino acids, and total isoflavone of soybean by comparing three different sample types, single seed, whole seeds, and milled seeds powder. The coefficient of determination in calibration (R2 ) and coefficient of determination in cross-validation (1-VR) for three components analyzed using NIRS revealed that milled powder sample type yielded the highest, followed by single seed, and the whole seeds as the lowest. The coefficient of determination in calibration for single seed was moderately low(R2 0.70-0.84), while the calibration equation developed with NIRS data scanned with whole seeds showed the lowest accuracy and reliability compared with other sample groups. The scatter plot for NIRS data versus the reference data of whole seeds showed the widest data cloud, in contrary with the milled powder type which showed flatter data cloud. By comparison of NIRS results for total isoflavone, total amino acids, and protein of soybean seeds with three sample types, the powder sample could be estimated for the most accurate prediction. However, based from the results, the use of single bean samples, without grinding the seeds and in consideration with NIRS application for more nondestructive and faster prediction, is proven to be a promising strategy for soybean component estimation using NIRS.

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.

The objective of this study was to rapidly evaluate fatty acids in a collection of foxtail millet (Setaria italica (L.) P. Beauv) of different origins so that this information could be disseminated to breeders to advance germplasm use and breeding. To develop the calibration equations for rapid and nondestructive evaluation of fatty acid content, near-infrared reflectance spectroscopy (NIRs) spectra (1104-2494 nm) of samples ground into flour (n=100) were obtained using a dispersive spectrometer. A modified partial least-squares model was developed to predict each component. For foxtail millet germplasm, our models returned coefficients of determination (R2) of 0.91, 0.89, 0.98 and 0.98 for strearic acid, oleic acid, linoleic acid, and total fatty acids, respectively. The prediction of the external validation set (n=10) showed significant correlation between references values and NIRs values (r2=0.97, 0.91, 0.99 for oleic, linoleic, and total fatty acids, respectively). Standard deviation/standard error of cross-validation (SD/SECV) values were greater than 3 (3.11, 5.45, and 7.50 for oleic, linoleic, and total fatty acids, respectively). These results indicate that these NIRs equations are functional for the mass screening and rapid quantification of the oleic, linolenic, and total fatty acids characterizing foxtail millet germplasm. Among the samples, IT153491 showed an especially high content of fatty acids (84.06 mg g-1), whereas IT188096 had a very low content (29.92 mg g-1).