The aim of the present study was to update the calibration that is used for the measurement of the total nitrogen content in the rice plant samples by using the visible and near infrared spectrum. Before the equation merge, correlation coefficient of calibration equation for nitrogen content on each rice parts was 0.945 (Leaf), 0.928 (Stem), and 0.864 (Whole plant), respectively. In the calibration models created by each part in the rice plant under the various regression method, the calibration model for the leaf was recorded with relatively high accuracy. Among of those, the calibration equation developed by Partial least squares (PLS) method was more accurate than the Multiple linear regression (MLR) method. The calibration equation was sensitive based on variety and location variations. However, we have merged and enlarged various of the samples that made not only to measure the nitrogen content more accurately, but also later sampling populations became more diversified. After merging, R2 value becomes more accurate and significantly to 0.950 (L.), 0.974 (S.), 0.940 (W.). Also, after removal of outlier, R2 values increased into 0.998, 0.995, and 0.997. In view of the results so far achieved, Standard error of prediction (SEP) and SEP (C) were reduced in the stem and whole plant. Biases were reduced in the leaf, stem as well as whole plant. Slopes were high in the stem. Standard deviation reduced in the stem but R2 was high in the stem and whole plant. Result was indicated that calibration equation make update, and updating robust calibration equation from merge function and multi-variate calibration.

녹차 품질평가의 한 요인이 되는 색도 평가 시 기존 평가 방법인 육안평가 혹은 색차 분석에 의존하고 있는 현행 분석방법을 신속, 간편하며 재현성이 높고, 녹차 품질관련 기타 성분과 동시분석이 가능한 녹차 색차 분석용 NIRS 검량식을 작성한 결과를 요약하면 다음과 같다. 1. 공시된 녹차 시료를 대상으로 색차계를 이용하여 색도 값(L, a, b)을 조사한 결과 검량식 작성용 시료는 L값이 평균 53.37(48.52~57.72 ), a값이 평균 -7.55(-10.02~-4.63 ), b 값이 평균 18.07(14.00~22.02 )을 나타내었고, 작성 검량식의 평가용으로 이용된 예견치 분석용 시료와 거의 동일한 범위를 나타내었다. 2. 녹차의 색차 분석용 NIRS 검량식을 검토한 결과 색차 중 명도에 해당하는 L 값은 원시 스펙트럼에 2차 미분(2nd derivative, 8 nm gap, 6 points smoothing, 1 point second smoothing)을 수행한 조건에서 R2 = 0.936으로 가장 우수한 양상을 나타내었고, 적색에 해당되는 색차 a값과 황색에 해당하는 b값은 1차 미분(1st derivative, 4 nm gap, 4 points smoothing, 1 point second smoothing)조건에서 R2 가 각각 0.991 및 0.958로 가장 우수한 결과를 나타내었다. 3. 최적의 녹차 색차 분석용으로 작성된 각각의 NIRS 검량식을 미지시료에 적용하여 정확성을 평가한 결과 색도값 L, a 및 b의 결정계수는 각각 0.905, 0.986 및 0.931로 매우 높은 상관을 보였으며, 이들 검량식은 향후 NIRS를 이용한 녹차 관련 연구 및 녹차 산업현장에서 품질관리를 위한 효율적 분석방법으로 활용이 가능할 것으로 판단된다.

NIR spectroscopy combined with multivariate analysis after the appropriate spectral data pre-treatment has been proved to be a very powerful tool for judgment of the relative pattern of the objects that have very similar properties. In this study, 500 GMO soybean seeds and, 500 non-GMO ones were measured in NIR reflectance mode. Principal component analysis (PCA), and discriminant analysis (DA) were applied to classify soybean with different genes into two groups (GMO and non-GMO). Calibrations were developed using DA regression with the cross-validation technique. The results show that differences between GMO and non-GMO soybeans do exist and excellent classification can be obtained after optimizing spectral pre-treatment. The raw spectra with DA model after the second derivative pre-treatment had the best satisfactory calibration and prediction abilities, with 97% accuracy. The results in the present study show NIR spectroscopy together with chemometrics techniques could be used to differentiate GMO soybean, which offers the benefit of avoiding time-consuming, costly and laborious chemical and sensory analysis.

Near infrared (NIR) spectroscopy was used to classify normal and artificially aged nonviable corn (Zea mays L., cv. 'Suwon19') seeds. The spectra at 1100-2500nm were scanned with normal and artificially aged single seeds and analyzed by principle component analysis (PCA). To discriminate normal seeds from artificially aged seeds, a calibration modeling set was developed with a discriminant partial least square 2 (PLS 2) method. The calibration model derived from PLS 2 resulted in 100% classification accuracy of normal and artificially aged (aged) seeds from the raw, the 1st and 2nd derivative spectra. The prediction accuracy of the unknown normal seeds was 88, 100 and 97% from the raw, the 1st and 2nd derivative spectra, and that of the unknown aged seeds was 100% from all the raw, the 1st and 2nd derivative spectra, respectively. The results showed a possibility to separate corn seeds into viable and non-viable using NIR spectroscopy.

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.

To investigate seed non destructive and fast determination technique utilizing near infrared reflectance spectroscopy (NIRs) for screening ultra high oleic (C18:1) and linoleic (C18:2) fatty acid content sesame varieties among genetic resources and lines of pedigree generations of cross and mutation breeding were carried out in National Institute of Crop Science (NICS). 150 among 378 landraces and introduced cultivars were released to analyse fatty acids by NIRs and gas chromatography (GC). Average content of each fatty acid was 9.64% in palmitic acid (C16:0), 4.73% in stearic acid (C18:0), 42.26% in oleic acid and 43.38% in linoleic acid by GC. The content range of each fatty acid was from 7.29 to 12.27% in palmitic, 6.49% from 2.39 to 8.88% in stearic, 12.59% of wider range compared to that of stearic and palmitic from 37.36 to 49.95% in oleic and of the widest from 30.60 to 47.40% in linoleic acid. Spectrums analyzed by NIRs were distributed from 400 to 2,500 nm wavelengths and varietal distribution of fatty acids were appeared as regular distribution. Varietal differences of oleic acid content good for food processing and human health by NIRs was 14.08% of which 1.49% wider range than that of GC from 38.31 to 52.39%. Varietal differences of linoleic acid content by NIRs was 16.41% of which 0.39% narrower range than that of GC from 30.60 to 47.01%. Varietal differences of oleic and linoleic acid content in NIRs analysis were appeared relatively similar inclination compared with those of GC. Partial least square regression (PLSR) among multiple variant regression (MVR) in NIRs calibration statistics was carried out in spectrum characteristics on the wavelength from 700 to 2,500 nm with oleic and linoleic acids. Correlation coefficient of root square (RSQ) in oleic acid content was 0.724 of which 72.4 percent of sample varieties among all distributed in the range of 0.570 percent of standard error when calibrated (SEC) which were considerably acceptable in statistic confidence significantly for analysis between NIRs and GC. Standard error of cross validation (SECV) of oleic acid was 0.725 of which distributed in the range of 0.725 percent standard error among the samples of mother population between analyzed value by NIRs analysis and analyzed value by GC. RSQ of linoleic acid content was 0.735 of which 73.5 percent of sample varieties among all distributed in the range of 0.643 percent of SEC. SECV of linoleic acid was 0.711 of which distributed in the range of 0.711 percent standard error among the samples of mother population between NIRs analysis and GC analysis. Consequently, adoption NIR analysis for fatty acids of oleic and linoleic instead that of GC was recognized statistically significant between NIRs and GC analysis through not only majority of samples distributed in the range of negligible SEC but also SECV. For enlarging and increasing statistic significance of NIRs analysis, wider range of fatty acids contented sesame germplasm should be kept on releasing additionally for increasing correlation coefficient of RSQ and reducing SEC and SECV in the future.

친환경적이면서 신속한 비파괴 분석방법인 FT-NIR를 이용하여 백미의 총식이섬유(TDF)함량 예측모델을 개발하였다. 백미는 국내산으로 전남지방에서 재배된 47개 품종과, 시중 유통 중인 13개 브랜드 미에 대해서 AOAC 방법에 준한 효소법에 의해 TDF 함량을 분석하였다. 습식 분석된 TDF함량의 범위는 이었다. FT-NIR로 측정된 스펙트럼의 검량식은 빛의 산란 효과를 최소화하기 위해 수학적 처리를 하였고, 몇 개의 특정 파장이 아닌 전 파

벼 영양진단에서 중요한 성분인 생체잎의 질소함량을 NIRS를 이용하여 신속하고 정확하게 분석하기 위해 최적의 검량식 작성에 관한 일련의 시험을 실시한 결과는 다음과 같다. 벼 생체엽 질소함량 검량식의 결정계수는 익산, 정읍, 부안지역이 각각 0.879, 0.858, 0.819였다. Outlier를 제거한 후 검량식을 다시 작성한 결과 0.896, 0.878, 0.88~% 로 각각 0.017, 0.02, 0.061씩 향상되었다. Merge 기능을 이용하여 검량식을 합병한 후 검량식을 다시 작성한 결과 0.971로 정확도가 더욱 향상되었다. 벼 생체엽의 질소함량 검량식에 의한 분석값과 습식분석 평균값의 차이는 0.001~% 를 나타냈다. 이와 같은 결과로서 건조와 분쇄과정을 생략하기 때문에 시료의 변질을 막을 수 있고 시간과 비용을 줄일 수 있는 벼 생잎의 질소농도 측정이 근적외분석기술에 의해 가능할 것으로 판단되었다.

In order to find out an alternative way of analysis of food waste compost, the Near Infrared Reflectance Spectroscopy(NIRS) was used for the compost assessment because the technics has been known as non-detructive, cost-effective and rapid method. One hundred thirty six compost samples were collected from Incheon food waste compost factory at Namdong Indurial Complex. The samples were analyzed for nitrogen, organic matter (OM), ash, P, and K using Kjedahl, ignition method, and acid extraction with spectrophotometer, respectively. The samples were scanned using FOSS NIRSystem of Model 6500 scanning monochromator with wavelength from 400~2,400㎚ at 2nm interval. Modified partial Least Squares(MPLS) was applied to develop the most reliable calibration model between NIR spectra and sample components such as nitrogen, ash, OM, P, and K. The regression was validated using validation set(n=30). Multiple correlation coefficient(R²) and standard error of prediction(SEP) for nitrogen, ash, organic matter, OM/N ratio, P and K were 0.87, 0.06, 0.72, 1.07, 0.68, 1.05, 0.89, 0.31, 0.77, 0.06, and 0.64, 0.07, respectively. The results of this experiment indicates that NIRS is reliable analytical method to assess some components of feed waste compost, also suggests that feasibility of NIRS can be justified in case of various sample collection around the year.

삼지구엽초에 함유되어 있는 icariin 함량을 신속하게 추정하기 위하여 NIRS(근적외선 분광분석기)를 이용한 분석 방법을 검토하였다. HPLC를 이용하여 분석된 삼지구엽초 유전자원 150계통에 대한 이카린 함량치를 NIRS 스펙트럼에 적용시켜 42개의 calibration set 와 26개의 valilion set를 구분하였다. NIRS의 검량식을 몇가지 방법에 의하여 비교분석한 결과 2차미분된 스텍트럼을 MPLS(Modified Partial Least Squares)를 이용한 회귀식에 이용하는 것이 가장 적합하였다. HPLC를 이용한 유전자원들의 이카린 함량은 평균 0.424%(0.12~0.67%)이었으며, NIRS에서 도출된 검량식과의 상관계수는 0.951을 나타내었다. 따라서 삼지구엽초의 이카린 함량은 NIRS를 이용하여 신속 편리하게 분석할 수 있음이 인정되었다.

The applicability of non-destructive near infrared reflectance spectroscopic (NIRS) method was tested to determine the protein and oil contents of intact soybean [Glycine max (L.) Merr.] seeds. A total of 198 soybean calibration samples and 101 validation samples were used for NIRS equation development and validation, respectively. In the developed non-destructive NIRS equation for analysis of protein and oil contents, the most accurate equation was obtained at 2, 8, 6, 1(2nd derivative, 8 nm gap, 6 points smoothing, and 1 point second smoothing) and 2, 1, 20, 10 math treatment conditions with Standard Normal Variate and Detrend (SNVD) scatter correction method and entire spectrum (400-2500 nm) by using Modified Partial Least Squares (MPLS) regression, respectively. Validation of these non-destructive NIRS equations showed very low bias (protein: 0.060%, oil: -0.017%) and standard error of prediction (SEP, protein: 0.568 %, oil : 0.451 %) as well as high coefficient of determination (R2 , protein: 0.927, oil: 0.906). Therefore, these non-destructive NIRS equations can be applicable and reliable for determination of protein and oil content of intact soybean seeds, and non-destructive NIRS method could be used as a mass screening technique for selection of high protein and oil soybean in breeding programs

The applicability of near infrared reflectance spectroscopy(NIRS) was tested to determine the protein and oil contents in ground soybean [Glycine max (L.) Merr.] seeds. A total of 189 soybean calibration samples and 103 validation samples were used for NIRS equation development and validation, respectively. In the NIRS equation of protein, the most accurate equation was obtained at 2, 8, 6, 1(2nd derivative, 8 nm gap, 6 points smoothing and 1 point second smoothing) math treatment condition with SNV-D (Standard Normal Variate and Detrend) scatter correction method and entire spectrum by using MPLS (Modified Partial Least Squares) regression. In the case of oil, the best equation was obtained at 1, 4, 4, 1 condition with SNV-D scatter correction method and near infrared (1100-2500nm) region by using MPLS regression. Validation of these NIRS equations showed very low bias (protein:-0.016%, oil : -0.011 %) and standard error of prediction (SEP, protein: 0.437%, oil: 0.377%) and very high coefficient of determination (R2 , protein: 0.985, oil : 0.965). Therefore, these NIRS equation seems reliable for determining the protein and oil content, and NIRS method could be used as a mass screening method of soybean seed

Near-infrared reflectance spectroscopy (NIRS) was used to estimate the lipid and protein contents in ground seed samples of perilla (Perilla frutescens Brit.) and peanut (Arachis hypogaea L.). A total of 46 perilla and 80 peanut calibration samples and 23 perilla and 46 pea. nut NIRS validation samples were used for NIRS equation development and validation, respectively. Validation of these NIRS equations showed a range of very low bias (-0.05 to 0.13 %) and standard error of prediction corrected for bias (0.224 to 0.803%) and very high coefficient of determination (R2 ) (0.962 to 0.985). It was concluded that NIRS could be adapted as a mass screening method for lipid and protein contents in perilla and peanut seed.

Apple fruit grading is largely dependant on skin color degree. This work reports about the possibility of nondestructive assessment of apple fruit color using infrared(NIR) reflectance spectroscopy. NIR spectra of apple fruit were collected in wavelength range of 1100~2500nm using an InfraAlyzer 500C(Bran+Luebbe). Calibration as calculated by the standard analysis procedures MLR(multiple linear regression) and stepwise, was performed by allowing the IDAS software to select the best regression equations using raw spectra of sample. Color degree of apple skin was expressed as 2 factors, anthocyanin content by purification and a-value by colorimeter. A total of 90 fruits was used for the calibration set(54) and prediction set(36). For determining a-value, the calibration model composed 6 wavelengths(2076, 2120, 2276, 2488, 2072 and 1492nm) provided the highest accuracy : correlation coefficient is 0.913 and standard error of prediction is 4.94. But, the accuracy of prediction result for anthocyanin content determining was rather low(R of 0.761).