• 한국작물학회지
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• 제57권3호
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• pp.296-300
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• 2012

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.

• Asian-Australasian Journal of Animal Sciences
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• 제18권7호
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• pp.1036-1040
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• 2005

Near-infrared reflectance spectroscopic (NIRS) calibrations were developed for accurate and fast prediction of whole broiler chicken carcass composition. The Feed and Forage Foss systems Model 5000 Reflectance Transport Model 5000 with near-infrared reflectance spectroscopy (NIRS)-WinISI II windows software was used for this purpose. One equation was developed for the prediction of each carcass component. One hundred and fifty freeze dried broiler whole carcass samples were ground in a Cyclotech 1,093 sample mill and analyzed for dry matter, protein, fat, calcium and phosphate. Samples were divided into two sets: a calibration set from which equations were derived and a prediction set used to validate these equations. The chemical analysis values (mean${\pm}$SD) were calculated based on dry matter basis as follows: dry matter: 33.41${\pm}$2.78 (range: 26.41-43.47), protein: 54.04${\pm}$6.63 (range: 36.20-76.09), fat 35.44${\pm}$8.34 (range: 7.50-55.03), calcium 2.55${\pm}$0.65 (range: 0.99-4.41), phosphorus 1.38${\pm}$0.26 (range: 0.60-2.28). One hundred and three samples were used to calibrate the equations and prediction values. The software used was modified to obtain partial least square regression statistics, as it is the most suitable for natural products analysis. The coefficients of determination ($R^2$) and the standard errors of prediction were 0.82 and 1.83 for the dry matter, 0.96 and 1.98 for protein, 0.99 and 1.07 for fat, 0.90 and 0.30 for calcium and 0.91 and 0.11 for phosphorus, respectively. The present study indicated that NIRS can be calibrated to predict the whole broiler carcass chemical composition, including minerals in a rapid, accurate, and cost effective manner. It neither requires skilled operators nor generates hazardous waste. These findings may have practical importance to improve instrumental procedures for quick evaluation of broiler carcass composition.

• 한국근적외분광분석학회:학술대회논문집
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1258-1258
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• 2001

Estimation of sugar and reducing sugar content in molasses is very important task in sugar refineries. Conventional methods of determination of sugar content in molasses samples are highly time consuming and employ hazardous chemicals. Due to the physical properties of molasses, probability of error in conventional analytical techniques is high. These methods have proven to be inefficient for a process control in any sugar industry. Hence development of a rapid, inexpensive, physical and also accurate method for sugar determination in molasses will be highly useful. Near Infrared spectroscopy is being widely used worldwide as an analytical technique in food industry. The technique offers the advantage of being non-destructive and rapid. The present paper highlights the potential of near infrared reflectance spectroscopy as a rapid and automated analytical technique for determination of sugar and reducing sugar content in molasses. A number of molasses samples were collected during and after the sugar season from Havana Sugar Industry, Havana. The samples were chosen so as to obtain a wide range of concentration of sugar and reducing sugars. This was done in order to achieve a good calibration curve with widely spread data points. These samples were scanned in the region of 1100 - 2500 nm in diffuse reflectance mode. An indigenous ELICO NIR spectrophotometer, modified according to the requirements of sugar industry was used for this purpose. Each sample was also analyzed simultaneously by standard chemical methods. Chemical values were taken as reference for near infrared analysis. In order to obtain the most accurate calibration for the set of samples, various mathematical treatments were employed. Partial Least Square method was found to be most suitable for the analysis. A comparison is made between the actual values (chemical values) and the predicted values (NIR values). The actual values agree very well with the predicted values showing the accuracy of the technique. The validity of the technique is checked by predicting the concentration of sugar in unknown molasses samples using the calibration curve. The present investigation assesses the feasibility of the technique for on-line monitoring of sugars present in molasses in sugar industries.

• Asian-Australasian Journal of Animal Sciences
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• 제18권5호
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• pp.643-648
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• 2005

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid, accurate method of evaluating some chemical constituents in cereal grains and forages. If samples could be analyzed without drying and grinding, then sample preparation time and costs may be reduced. This study was conducted to develop robust NIRS equations to predict fermentation quality of corn (Zea mays) silage and to select acceptable sample preparation methods for prediction of fermentation products in corn silage by NIRS. Prior to analysis, samples (n = 112) were either oven-dried and ground (OD), frozen in liquid nitrogen and ground (LN) and intact fresh (IF). Samples were scanned from 400 to 2,500 nm with an NIRS 6,500 monochromator. The samples were divided into calibration and validation sets. The spectral data were regressed on a range of dry matter (DM), pH and short chain organic acids using modified multivariate partial least squares (MPLS) analysis that used first and second order derivatives. All chemical analyses were conducted with fresh samples. From these treatments, calibration equations were developed successfully for concentrations of all constituents except butyric acid. Prediction accuracy, represented by standard error of prediction (SEP) and $R^2_{v}$ (variance accounted for in validation set), was slightly better with the LN treatment ($R^2$ 0.75-0.90) than for OD ($R^2$ 0.43-0.81) or IF ($R^2$ 0.62-0.79) treatments. Fermentation characteristics could be successfully predicted by NIRS analysis either with dry or fresh silage. Although statistical results for the OD and IF treatments were the lower than those of LN treatment, intact fresh (IF) treatment may be acceptable when processing is costly or when possible component alterations are expected.

• 한국작물학회지
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• 제57권1호
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• pp.29-34
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• 2012

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 ($R^2$) 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 ($r^2$=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 ($34.44mg\;g^{-1}$).

• 한국육종학회지
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• 제42권5호
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• pp.448-454
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• 2010

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 ($R^2$) 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 ($r^2=0.97$, 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 ($84.06mg\;g^{-1}$), whereas IT188096 had a very low content ($29.92mg\;g^{-1}$).

• 한국작물학회지
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• 제46권2호
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• pp.106-111
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• 2001

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 ($R^2$, 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.

• 한국초지조사료학회지
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• 제34권3호
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• pp.214-218
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• 2014

This study was conducted to assess the feasibility of near-infrared reflectance spectroscopy (NIRS) as a rapid and reliable method for the estimation of crude protein (CP) fractions in forage legume mixtures (sudangrass and pea mixture, and kidney bean and potato mixture). A total of 178 samples were collected and their spectral reflectance obtained in the range of 400~2,500 nm. Of these, 50 samples were selected for calibration and validation, and 35 samples were used for calibration of the data set, and the modified partial least square regression (MPLSR) analysis was performed. The correlation coefficient ($r^2$) and the standard error of cross-validation (SECV) of the calibration models in the CP fractions, A, B1, B2, B3, and C, were 0.94 (1.05), 0.92 (0.74), 0.96 (0.95), 0.91 (0.42), and 0.83 (0.38), respectively. Fifteen samples were used for equation validation, and the $r^2$ and the standard error of prediction (SEP) were 0.87 (1.45), 0.91 (0.49), 0.94 (1.13), 0.36 (0.96), and 0.74 (0.67), respectively. This study showed that NIRS could be an effective tool for the rapid and precise estimation of CP fractions in forage legume mixtures.

• 한국정보디스플레이학회:학술대회논문집
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• 한국정보디스플레이학회 2009년도 9th International Meeting on Information Display
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• pp.360-363
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• 2009

In this paper, we describe new approach of ink particle fabrication method for electrophoretic display(EPD) with low voltage and rapid response time. Nano-size ink particles which fabricated using non-aqueous base modified emulsion process and micron-scale particles by non-solvent particle fabrication process are discussed. Finally, specially designed particles and panel structure fabricated considering the interactions between particle/particle, particle/media or particle/electrode dramatically reduce the driving voltages to ${\pm}$ 10V and improve the response time of less than 100msec and white reflectance of 58% for EPD using dielectric fluid as a medium. In case of EPD adapting micron-sized electrophoretic particles and a medium of air, the saturation voltage could be reduced to ${\pm}$ 40V and having white reflectance of 45% without scarification of electrophoretic mobility of the particles.

• 대한원격탐사학회지
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• 제29권2호
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• pp.173-182
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• 2013

천리안 위성 해양탑재체(Geostationary Ocean Color Imager, GOCI) 대기보정의 근간이 되는 Sea-viewing Wide Field-of-view Sensor(SeaWiFS) 초기 대기보정 기법은 근적외선 파장대의 해수 반사도를 0으로 가정한다. 이러한 가정에 근거하여 근적외선 파장에서 탐지되는 모든 신호는 에어로졸 산란에 의한 반사도로 간주된다. 그러나 이러한 가정은 탁한 해역에서 해수 반사도를 과소 추정하는 문제점을 야기시킨다. 이를 해결하기 위하여 Management Unit of the North Sea Mathematical Models(MUMM) 대기보정 알고리즘이 개발되었다. 이 알고리즘은 근적외선 파장에서 탐지되는 해수 반사도 비율인 ${\alpha}$를 도입하였다. ${\alpha}$는 통계적 방법에 의하여 결정되며 영상 내의 모든 픽셀에 고정적인 값으로 사용된다. 이 알고리즘은 근적외선 해수 반사도가 0.01보다 작은 중간 탁도의 해역에서는 잘 맞는 반면 매우 탁한 해역에서는 ${\alpha}$가 탁도에 따라 변하기 때문에 오차율이 다시 증가한다. 본 연구에서는 매우 탁한 해역 해수 반사도의 정확도를 향상시키고자 ${\alpha}$를 고정하지 않고, 반복계산을 통해 탁도에 적합한 ${\alpha}$를 계산하도록 MUMM 알고리즘을 수정 보완하였다. 그 결과 MUMM 알고리즘의 모든 밴드의 평균 Root Mean Square Error(RMSE)는 0.0048인 반면 수정된 MUMM 알고리즘은 0.002로 개선된 결과를 얻었다.