목적 : 근적외선 차단렌즈의 색 왜곡을 평가하기 위하여 객관적 및 주관적 측색 결과를 분석하였다. 방법 : Colorchecker Classic의 15번(Red), 16번(Yellow) 및 14번(Green) 색표를 가상신호등으로 설정하여 객관적 측색을 실시하였다. 객관적 평가를 위하여 디지털 카메라에 각 렌즈를 장착한 후 가상신호등을 촬영하였 다. 본래 색상과 렌즈 장착 후 측정된 색을 CIE 1976 L*a*b* 색도좌표로 표시하였고, 좌표간 거리 값인 ΔE* ab를 산출하여 색 왜곡도를 비교 분석하였다. 32명을 대상으로 주관적 평가를 실시하였다. 교통신호등의 관찰된 색을 한국 색채 표준 디지털팔레트에서 선택하도록 하여 본래 색상과 비교하였다. 결과 : 객관적 평가에서, 황색 및 녹색 가상신호등의 색왜곡은 근적외선 차단렌즈 착용 시 가장 작은 것으로 나 타났다. 근적외선 차단렌즈에 의한 적색 가상신호등의 색왜곡은 녹색렌즈보다 크지만 갈색, 회색 및 청색렌즈에 비교하여 더 적은 것으로 나타났다. 주관적 평가에서, 적색 신호등을 주시했을 때 근적외선 차단렌즈에 의한 색왜곡은 갈색렌즈와 비교하여 더 많았으며, 청색 및 녹색렌즈 착용과 유사하였고, 회색렌즈 착용보다 적은 것으로 나타났다. 결론 : 근적외선 차단렌즈의 색왜곡은 다른 렌즈와 비교하여 객관적 평가에서 가장 낮았으나, 주관적 평가에서는 유의한 차이가 없었다. 근적외선 차단렌즈의 주관적 색 재현성을 정량적으로 조사할 수 있는 추가적인 연구가 필요할 것으로 생각된다.

목적 : 근적외선 흡수렌즈의 광학적 특성과 단열특성을 분석하였다. 방법 : 선명도를 분석하기 위해 화상테스트 차트를 카메라로 촬영하였고, 이미지 분석 프로그램을 이용하여 분석하였다. 32명을 대상으로 시력을 측정하였다. 단열효과를 분석하기 위해 돼지피부와 안검 피부에 근적외선을 조사하고 디지털온도계와 열화상카메라를 사용하여 온도를 측정했다. 렌즈 표면에 근적외선을 조사하여 표면온도를 측정하였고, 열에 의한 렌즈손상을 관찰하였다. 근적외선 흡수안경과 착색안경을 착용하고 선명도, 눈부심, 열감, 광량에 대한 주관적 만족도를 평가하였다. 결과 : 근적외선 흡수렌즈와 착색렌즈에 의한 선명도와 시력은 유의한 차이가 없었다. 근적외선 흡수렌즈를 착용 했을 때 돼지피부와 사람 안검의 온도변화는 착색렌즈를 착용했을 때보다 더 낮았다. 근적외선 조사에 의해 근적외선 흡수렌즈가 착색렌즈보다 표면의 온도가 더 높았고, 더 빨리 손상되었다. 설문조사에서 근적외선 흡수렌즈에 의한 선명도(p=0.040)와 눈부심(p=0.000)에 관한 만족도는 모두 청색렌즈보다 더 높았지만 나머지 착색렌즈들과 유의한 차이가 없었다. 근적외선 흡수렌즈에 의한 열감과 광량에 관한 주관적 만족도는 착색렌즈보다 더 높았다. 결론 : 근적외선 흡수렌즈와 착색렌즈에 의한 선명도와 시력은 유의한 차이가 없었으며, 근적외선 흡수렌즈의 단열효과는 착색렌즈보다 더 좋았다. 본 연구는 근적외선 흡수렌즈의 단열효과에 대한 기초자료를 제시하였다고 생각된다.

In this study, whole crop rice samples were used to develop near-infrared reflectance (NIR) equations to estimate six forage quality parameters: Moisture, crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), Ash and relative feed value (RFV). A population of 564 whole crop rice representing a wide range in chemical parameters was used in this study. Undried finely chopped whole crop rice samples were scanned at 1 nm intervals over the wavelength range 680–2500 nm and the optical data recorded as log 1/Reflectance (log 1/R). NIRS calibrations were developed by means of partial least-squares (PLS) regression. The correlation coefficients of cross-validation (R2 cv) and standard error of cross-validation (SECV) for whole crop rice calibration were 0.98 (SECV 1.81%) for moisture, 0.89 (SECV 0.50%) for CP, 0.86 (SECV 1.79%) for NDF, 0.89 (SECV 0.86%) for ash, and 0.84 (SECV 5.21%) for RFV on a dry matter (%), respectively. The NIRS calibration equations developed in this study will be useful in predicting whole crop rice quality for these six quality parameters.

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.

This study was carried out to explore the accuracy of near infrared spectroscopy(NIRS) for the prediction of moisture content and chemical parameters on winter annual forage crops. A population of 2454 winter annual forages representing a wide range in chemical parameters was used in this study. Samples of forage were scanned at 1nm 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 calibration model to predict the moisture contents and chemical parameters had very high degree of accuracy except for barely. The R2 and SECV for integrated winter annual forages calibration were 0.99(SECV 1.59%) for moisture, 0.89(SECV 1.15%) for acid detergent fiber, 0.86(SECV 1.43%) for neutral detergent fiber, 0.93(SECV 0.61%) for crude protein, 0.90(SECV 0.45%) for crude ash, and 0.82(SECV 3.76%) 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 winter annual forage for routine analysis method to evaluate the feed value.

LuNbO4:0.2Yb3+,xTm3+ powders were prepared using a solid-state reaction process. The effects of the amount of Tm on up-conversion(UC) and down-conversion(DC) luminescence properties are investigated. X-ray diffraction patterns confirm that Yb3+ and Tm3+ ions are successfully incorporated into Lu sites. Under 980 nm excitation, the UC spectra of the powders predominantly exhibit strong near-infrared emission bands that peak at 805 nm, whereas weak 480 nm emission bands are observed as well. The emission bands are assigned to the 1G4→ 3H6 (480 nm) and 3H4→ 3H6 (805 nm) transitions of the Tm3+ ions via an energy transfer from Yb3+ to Tm3+; two- and three-photon UC processes are responsible for the 805 and 480 nm emissions, respectively. The DC emission spectra exhibit blue emission (1D2→ 3F4) of Tm3+ at 458 nm. The amount of Tm affects the emission intensity with the strongest emissions at x = 0.007 and 0.02 for the UC and DC luminescence, respectively. The results demonstrate that LuNbO4:Yb3+,Tm3+ phosphors are suitable for bio-applications.

Near infrared spectroscopy (NIRS) is a rapid and accurate method for analyzing the quality of cereals, and dried animal forage. However, one limitation of this method is its inability to measure fermentation parameters in dried and ground samples because they are volatile, and therefore, respectively lost during the drying process. In order to overcome this limitation, in this study, fresh coarse haylage was used to test the potential of NIRS to accurately determine chemical composition and fermentation parameters. Fresh coarse Italian ryegrass haylage samples were scanned at 1 nm intervals over a wavelength range of 680 to 2500 nm, and optical data were recorded as log 1/reflectance. Spectral data, together with first- and second-order derivatives, were analyzed using partial least squares (PLS) multivariate regressions; scatter correction procedures (standard normal variate and detrend) were used in order to reduce the effect of extraneous noise. Optimum calibrations were selected based on their low standard error of cross validation (SECV) values. Further, ratio of performance deviation, obtained by dividing the standard deviation of reference values by SECV values, was used to evaluate the reliability of predictive models. Our results showed that the NIRS method can predict chemical constituents accurately (correlation coefficient of cross validation, R2 cv, ranged from 0.76 to 0.97); the exception to this result was crude ash (R2 cv = 0.49 and RPD = 2.09). Comparison of mathematical treatments for raw spectra showed that second-order derivatives yielded better predictions than first-order derivatives. The best mathematical treatment for DM, ADF, and NDF, respectively was 2, 16, 16, whereas the best mathematical treatment for CP and crude ash, respectively was 2, 8, 8. The calibration models for fermentation parameters had low predictive accuracy for acetic, propionic, and butyric acids (RPD < 2.5). However, pH, and lactic and total acids were predicted with considerable accuracy (R2 cv 0.73 to 0.78; RPD values exceeded 2.5), and the best mathematical treatment for them was 1, 8, 8. Our findings show that, when fresh haylage is used, NIRS-based calibrations are reliable for the prediction of haylage characteristics, and therefore useful for the assessment of the forage quality.

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.