This study was conducted to find out an alternative way of rapid and accurate analysis of chemical composition of permanent pastures in hilly grazing area. Near reflectance infrared spectroscopy (NIRS) was used to evaluate the potential for predicting proximate analysis of permanent pastures in a vegetative stage. 386 pasture samples obtained from hilly grazing area in 2015 and 2016 were scanned for their visible–NIR spectra from 400~2,400nm. 163 samples with different spectral characteristics were selected and analysed for moisture, crude protein (CP), crude ash (CA), acid detergent fiber (ADF) and neutral detergent fiber (NDF). Multiple linear regression was used with wet analysis data and spectra for developing the calibration and validation mode1. Wavelength of 400 to 2500nm and near infrared range with different critical T outlier value 2.5 and 1.5 were used for developing the most suitable equation. The important index in this experiment was SEC and SEP. The R2 value for moisture, CP, CA, CF, Ash, ADF, NDF in calibration set was 0.86, 0.94, 0.91, 0.88, 0.48 and 0.93, respectively. The value in validation set was 0.66, 0.86, 0.83, 0.71, 0.35 and 0.88, respectively. The results of this experiment indicate that NIRS is a reliable analytical method to assess forage quality for CP, CF, NDF except ADF and moisture in permanent pastures when proper samples incorporated into the equation development.

Near Infra-red (NIR) analytical method was applied to quantify fat, moisture, protein, and salt in meat products. To ensure an accurate analysis, the NIR analysis method was compared with Korean food standard codex method. Correlation coefficients were 0.961-0.997, and the ratio of standard error of prediction (SEP) with standard error of laboratory (SEL) were 1.01-1.32. Z-score and Q score were −0.02-0.95 and −0.27-0.15, respectively. A control chart was conducted for three meat products. Each control chart data consisted of fat, moisture, protein, and salt, and it was produced by NIR analysis. The control chart of meat products was used for quality control.

근적외선 분광 분석법을 이용하여 참깨의 지방함량의 신속측정 가능성에 대해서 연구하였다. 검량식의 기본 모형은 다중 회귀 모형으로 하였으며, 회귀 모형의 독립 변수의 선택은 step-wise법을 이용하였다. 1. 참깨의 121점 중 지방산 함량 분포는 palmitic acid(IT028899-7.2%, IT169359-10.0%), stearic acid(IT028897-4.5%, IT028910-7.0%), oleic acid(IT170020-32.2%, IT156371-46.5%), linoleic acid(IT156348-36.5, IT169926-49.6%), 그리고 linolenic acid(IT169316-0.2%, IT156367-0.6%)로 나타났다. 2. 지방함량을 예측하기 위해서 검량선 작성시 종래의 화학적 방법에 의한 분석치와 NIRS 분석치 와의 상관계수는 oleic acid(r2-0.834) and linoleic acid(r2-0.925)로 판단되어 linoleic acid의 이용은 가능할 것으로 판단되었다.

Red pepper powder (Capsicum annum L.) is an important seasoning as a kimchi ingredient in korea and most korean consumer tend to eat the korean red pepper powder as the better than other oriental country such as China. Near infrared reflectance spectroscopy (NIRS) was applied for discrimination according to geographical origin (Korea, China) of red pepper powder. The objective of this study is to determine if NIR technique could be used to discriminate between the korean red pepper powder and non-korean red pepper powder according to seed content and maxing ratio in red pepper powder by using the new method. Rapid, precise and nondestructive analysis method for determination of the geographical origin of red pepper powder by near infrared spectroscopy and chemometrics were performed. It has been observed discriminant analysis with PLS is adequate to determinate the geographical origin of red pepper powder. It tend to difficult the discrimination of geographical origin according to increase the seed content of red pepper powder. The accuracy of discrimination in mixed red pepper powder was range from 95.2% to 100%.

Sesame seed (Sesamum indicum L.) is an important seasoning in Korea and most korean consumer tend to eat the korean sesame seed as the best than other ones produced in oriental countries such as China and Japan. Near infrared reflectance spectroscopy (NIRS) was applied for discrimination according to geographical origin (Korea, China and so on) of sesame seeds. Near-infrared spectroscopy among the many kinds of techniques could provide a rapid screening, low cost solution to discriminate geographical origin of sesame seed. The objective of this study is to determine if NIR technique could be used to discriminate between the korean sesame seed and non-korean sesame seed by using the new method. Rapid, precise and nondestructive analysis method for determination of the geographic origin of sesame seeds were discriminated relative accurately according to geographical origin using PLS regression method.

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.

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.

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.

NIRS는 농산물 구성성분의 정량 및 정성 분석뿐만 아니라 최근 원산지 판별에도 이용하고 있다. 본 연구에서 는 NIRS를 이용하여 콩의 원산지 판별을 하였으며, 공시재료는 국내산 콩 92점과 수입산 콩 43점을 사용하였 다. 방법은 시료를 마쇄한 후 NIRS로 400～2,500 nm 파장범위를 스캔하였으며 SNV 와 detarend로 입도차이에 서 오는 산란을 보정하였다. 또한 물리적 영향에 의해 야기되는 바탕선 변화 등의 오차를 줄이고 겹쳐 있는 파장을 분리하기 위해 2차 미분법을 통한 수처리를 하고 검량식 및 판별식을 작성하였다. 수처리 후 MPLS 회귀분석법에 의한 검량식 작성시 R2가 0.98인 검량식을 얻을 수 있었으며, 이때 SEC는 6.54이었다. 한편, 판별의 정확도를 위해 전체 135 시료를 두 set로 나눈 후 validation equation을 확인한 바, 1-VR 및 SECV 값이 각각 0.91, 13.98로 나타나 NIRS를 이용한 콩의 원산지 판별이 가능한 것으로 판단되었다.

본 시험은 종자은행이 보유하고 있는 다양한 벼 유전자원의 활용을 촉진하기 위하여 비파괴적인 분석방법의 하나인 근적외선 분광분석법을 이용한 유용유전자원의 대량 선발체계 구축을 위해 실시하였다. 1. NIR스펙트럼은 700 nm 이하의 가시광선 범위에서 다양한 범위의 spectrum을 보였으며, 700 nm에서 2500 nm의 근적외선 파장에서도 spectrum의 차이가 크게 나타났고, 1400 nm에서 전체 spectrum의 정점을 나타냈으며 그 이상의 spectrum에서는 포화현상을 나타내었다. 2. NIR 검량식 작성에 이용된 144점의 선발자원이 가지는 단백질함량의 범위는 6.5~9.5% 였으며, 134점의 선발자원이 가지는 아밀로스함량의 범위는 18.1~21.7% 이었다. 3. 단백질함량과 아밀로스함량의 실험치(Lab data)와 NIR 데이터의 모집단 분포의 해석과 상관관계에 관한 통계분석 결과, 단백질함량과 아밀로스함량의 R2 (RSQ) 값은 각각 0.786과 0.865로 높게 보였으며, 검량식 표준오차(SEC)는 각각 0.442와 2.078로 유의한 값을 보였고, 또한 검량식 검정 표준오차(SECV)도 각각 0.541과 3.106으로 유의한 값을 보였지만 검증시 상관정도(1-VR)는 0.68과 0.70로 검량식 작성시보다 낮은 유의성을 보였다.