We study CO2/H2O ice abundance ratios in nearby galaxies using AKARI near-infrared slit spec- troscopy. Past studies of the ices intensively examined CO2/H2O ratios mainly in our Galaxy, and found that there were considerable variations in the CO2/H2O ratios from object to object. The cause of the variations is, however, still under debate. As a result of the analysis of our sample that includes 1031 regions in 158 galaxies, the CO2/H2O ratios are in a range of 0.05-0.30. In the dataset, we nd that the CO2/H2O ratios positively correlate with the Brα/PAH 3.3 μm ratios which re ect the massive star formation activity. Furthermore, we find that the CO2/H2O ratios positively correlate with the specific star formation rates of the galaxies where the ices are detected, that re ect the evolutionary stage of a galaxy. These results suggest that the CO2/H2O ratios are enhanced in active star-forming regions and young galaxies.

This study was conducted to determine the effect of mathematical transformation on near infrared spectroscopy (NIRS) calibrations for the prediction of chemical composition and fermentation parameters in corn silage. Corn silage samples (n=407) were collected from cattle farms and feed companies in Korea between 2014 and 2015. Samples of silage were scanned at 1 nm intervals over the wavelength range of 680~2,500 nm. The optical data were recorded as log 1/Reflectance (log 1/R) and 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 several spectral math treatments to reduce the effect of extraneous noise. The optimum calibrations were selected based on the highest coefficients of determination in cross validation (R2 cv) and the lowest standard error of cross validation (SECV). Results of this study revealed that the NIRS method could be used to predict chemical constituents accurately (correlation coefficient of cross validation, R2 cv, ranging from 0.77 to 0.91). The best mathematical treatment for moisture and crude protein (CP) was first-order derivatives (1, 16, 16, and 1, 4, 4), whereas the best mathematical treatment for neutral detergent fiber (NDF) and acid detergent fiber (ADF) was 2, 16, 16. The calibration models for fermentation parameters had lower predictive accuracy than chemical constituents. However, pH and lactic acids were predicted with considerable accuracy (R2 cv 0.74 to 0.77). The best mathematical treatment for them was 1, 8, 8 and 2, 16, 16, respectively. Results of this experiment demonstrate that it is possible to use NIRS method to predict the chemical composition and fermentation quality of fresh corn silages as a routine analysis method for feeding value evaluation to give advice to farmers.

This study was conducted to assess the potential of using NIRS to accurately determine the chemical composition and fermentation parameters in fresh coarse sorghum and sudangrass silage. Near Infrared Spectroscopy (NIRS) has been increasingly used as a rapid and accurate method to analyze the quality of cereals and dried animal forage. However, silage analysis by NIRS has a limitation in analyzing dried and ground samples in farm-scale applications because the fermentative products are lost during the drying process. Fresh coarse silage samples were scanned at 1 nm intervals over the wavelength range of 680~2500 nm, and the optical data were obtained as log 1/Reflectance (log 1/R). The spectral data were regressed, using partial least squares (PLS) multivariate analysis in conjunction with first and second order derivatization, with a scatter correction procedure (standard normal variate and detrend (SNV&D)) to reduce the effect of extraneous noise. The optimum calibrations were selected on the basis of minimizing the standard error of cross validation (SECV). The results of this study showed that NIRS predicted the chemical constituents with a high degree of accuracy (i.e. the correlation coefficient of cross validation (R²cv) ranged from 0.86~0.96), except for crude ash which had an R² cv of 0.68. Comparison of the mathematical treatments for raw spectra showed that the second-order derivatization procedure produced the best result for all the treatments, except for neutral detergent fiber (NDF). The best mathematical treatment for moisture, acid detergent fiber (ADF), crude protein (CP) and pH was 2,16,16 respectively while the best mathematical treatment for crude ash, lactic acid and total acid was 2,8,8 respectively. The calibrations of fermentation products produced poorer calibrations (RPD < 2.5) with acetic and butyric acid. The pH, lactic acid and total acids were predicted with considerable accuracy at R²cv 0.72~0.77. This study indicated that NIRS calibrations based on fresh coarse sorghum and sudangrass silage spectra have the capability of assessing the forage quality control

Young Galactic supernova remnants (SNRs) are where we can observe closely supernova (SN) ejecta and their interaction with the circumstellar/interstellar medium. They also provide an opportunity to explore the explosion and the final stage of the evolution of massive stars. Near-infrared (NIR) emission lines in SNRs mostly originate from shocked dense material. In shocked SN ejecta, forbidden lines from heavy ions are prominent, while in shocked circumstellar/interstellar medium, [Fe II] and H2 lines are prominent. [Fe II] lines are strong in both media, and therefore [Fe II] line images provide a good starting point for the NIR study of SNRs. There are about twenty SNRs detected in [Fe II] lines, some of which have been studied in NIR spectroscopy. We will review the NIR [Fe II] observations of SNRs and introduce our recent NIR spectroscopic study of the young core-collapse SNR Cas A where we detected strong [P II] lines.

본 연구는 근적외선분광법을 이용하여 우리나라에서 재배되고 있는 목초류 중 외형적 특성이 유사한 이탈리안 라이그라스, 페레니얼 라이그라스와 톨 페스큐 종자의 초종 판별 가능성을 검토하고자 수행되었다. 근적외선분광기를 이용하여 목초류 종자를 가시파장 대역대(680~1,099 nm), NIRS 파장 대역대(1,100-2,500 nm) 및 NIRS 전체 파장 대역대(680-2,500 nm)로 구분하여 스펙트라를 얻은 후 1차 미분과 8 nm gap으로 수 처리를 수행하였으며 부분최소자승(PLS) 회귀분석법을 통해 초종판별 검량식을 개발하고 판별 정확성을 검증하였다. 목초류의 초종판별 정확성은 가시파장대역에서 SECV 1.732, R2CV 0.96으로 가장 판별 정확성이 낮았으며 NIRS 전체 파장대역에서 SECV 1.182, R2CV 0.98로 가장 높은 판별 정확성을 나타내었다. 파장대역별 예측 정확성은 NIR 파장대역(1,100-2,500 nm)에서 교차검증오차(SECV) 1.319에서 예측 오차(SEP) 1.288로 낮아졌으며 가시영역대(680~1,099)는 SECV 1.732에서 SEP 1.749로 약간 높아졌다. Discrimination equation 분석법에 의한 NIRS 전체 파장대역별 목초류 초종의 판별 결과는 초종간에 판별 정확성의 차이가 크게 나타났으며 이탈리안 라이그라스의 ‘Hits’는 68%로 가장 낮았으며 페레니얼 라이그라스가 78%의 정확성으로 가장 높게 나타났다. 따라서 NIRS를 이용한 목초류 초종의 판별분석이 가능할 것으로 판단되었다.

Nutritive value analysis of feed is very important for the growth of livestock, and ensures the efficiency of feeds as well as economic status. However, general laboratory analyses require considerable time and high cost. Near-infrared reflectance spectroscopy (NIRS) is a spectroscopic technique used to analyze the nutritive values of seeds. It is very effective and less costly than the conventional method. The sample used in this study was a corn kernel and the partial least square regression method was used for evaluating nutrient composition, digestibility, and energy value based on the calibration equation. The evaluation methods employed were the coefficient of determination (R2) and the root mean squared error of prediction (RMSEP). The results showed the moisture content (R2val=0.97, RMSEP=0.109), crude protein content (R2val=0.94, RMSEP=0.212), neutral detergent fiber content (R2val=0.96, RMSEP=0.763), acid detergent fiber content (R2val=0.96, RMSEP=0.142), gross energy (R2val=0.82, RMSEP=23.249), in vitro dry matter digestibility (R2val=0.68, RMSEP=1.69), and metabolizable energy (approximately R2val >0.80). This study confirmed that the nutritive components of corn kernels can be predicted using near-infrared reflectance spectroscopy.

This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical and fermentation parameters of whole crop winter rye silages. A representative population of 216 fresh winter rye silages was used as database for studying the possibilities of NIRS to predict chemical composition and fermentation parameters. Samples of silage were scanned at 1 nm intervals over the wavelength range 680~2,500 nm and the optical data recorded as log 1/Reflectance (log 1/R) and scanned in fresh condition. NIRS calibrations were developed by means of partial least-squares (PLS) regression. NIRS analysis of fresh winter rye silages provided accurate predictions of moisture, acid detergent fiber (ADF), neutral detergent fiber (NDF), crude protein (CP) and pH as well as lactic acid content with correlation coefficients of cross-validation (R2cv) of 0.96, 0.86, 0.79, 0.85, 0.82 and 0.78 respectively and standard error of cross-validation (SECV) of 1.89, 2.02, 2.79, 1.14, 1.47 and 0.46 % DM respectively. Results of this experiment showed the possibility of NIRS method to predict the chemical parameters of winter rye silages as routine analysis method in feeding value evaluation and for farmer advice.

본 논문에서는 주성분 회귀법과 부분최소자승 회귀법을 비교하여 보여준다. 이 비교의 목적은 선형형태를 보유한 근적외선 분광 데이터의 분석에 사용할 수 있는 적합한 예측 방법을 찾기 위해서이다. 두 가지 데이터 마이닝 방법 론인 주성분 회귀법과 부분최소자승 회귀법이 비교되어 질 것이다. 본 논문에서는 부분최소자승 회귀법은 주성분 회귀법과 비교했을 때 약간 나은 예측능력을 가진 결과를 보여준다. 주성분 회귀법에서 50개의 주성분이 모델을 생 성하기 위해서 사용지만 부분최소자승 회귀법에서는 12개의 잠재요소가 사용되었다. 평균제곱오차가 예측능력을 측 정하는 도구로 사용되었다. 본 논문의 근적외선 분광데이터 분석에 따르면 부분최소자승회귀법이 선형경향을 가진 데이터의 예측에 가장 적합한 모델로 판명되었다.

This work was conducted to assess the use of Near-infrared reflectance spectroscopy (NIRS) as a technique to analyze nutritional constituents of Distillers dried grain with solubles (DDGS) and corn quickly and accurately, and to apply an NIRS-based indium gallium arsenide array detector, rather than a NIRS-based scanning system, to collect spectra and induce and analyze calibration equations using equipment which is better suited to field application. As a technique to induce calibration equations, Partial Least Squares (PLS) was used, and for better accuracy, various mathematical transformations were applied. A multivariate outlier detection method was applied to induce calibration equations, and, as a result, the way of structuring a calibration set significantly affected prediction accuracy. The prediction of nutritional constituents of distillers dried grains with solubles resulted in the following: moisture (R2=0.80), crude protein (R2=0.71), crude fat (R2=0.80), crude fiber (R2=0.32), and crude ash (R2=0.72). All constituents except crude fiber showed good results. The prediction of nutritional constituents of corn resulted in the following: moisture (R2=0.79), crude protein (R2=0.61), crude fat (R2=0.79), crude fiber (R2=0.63), and crude ash (R2=0.75). Therefore, all constituents except for crude fat and crude fiber were predicted for their chemical composition of DDGS and corn through Near-infrared reflectance spectroscopy.

Near infrared reflectance spectroscopy (NIRS) has become increasingly used as a rapid and accurate method of evaluating some chemical compositions in forages and feedstuff. This study was carried out to explore the accuracy of near infrared spectroscopy (NIRS) for the prediction of chemical parameters of fresh whole crop barley silages. A representative population of 284 fresh whole crop barley silages was used as a database for studying the possibilities of NIRS to predict chemical composition. Samples of silage were scanned at 1 nm intervals over the wavelength range 680~2,500 nm and the optical data were recorded as log 1/Reflectance (log 1/R) and were scanned in fresh condition. NIRS calibrations were developed by means of partial least-squares (PLS) regression. NIRS analysis of fresh whole crop barley silages provided accurate predictions of moisture, acid detergent fiber (ADF), neutral detergent fiber (NDF), crude protein (CP) and pH, as well as lactic acid content with correlation coefficients of cross-validation (R2cv) of 0.96, 0.81, 0.79, 0.84, 0.72 and 0.78, respectively, and standard error of cross-validation (SECV) of 1.26, 2.83, 2.18, 1.19, 0.13 and 0.32% DM, respectively. Results of this experiment showed the possibility of the NIRS method to predict the chemical parameters of fresh whole crop barley silages as a routine analysis method in feeding value evaluation and for farmer advice.

We provide a new physical insight on the hot molecular clouds near the nucleus of the heavily obscured AGN IRAS 01250+2832, based on the results of near-infrared high-resolution spectroscopy of gaseous CO ro-vibrational absorption lines with Subaru/IRCS. The detected CO absorption lines up to highly excited rotational levels reveal that hot dense molecular clouds exist around the AGN under the peculiar physical conditions.

With AKARI, we carried out near-infrared spectroscopy of the nearby barred spiral galaxy, NGC 1097, categorized as Seyfert 1 with a circumnuclear starburst ring. Our observations mapped the galactic center region. As a result, we obtain the spatial distributions of the polycyclic aromatic hydrocarbon 3.3μm and the aliphatic hydrocarbon 3.4−3.6 μm emission. The former is detected from all the observed regions and the latter is enhanced near the bar connecting the ring with the nucleus. In addition, we detect absorption features due to H2O ice and CO/SiO at the ring and the galactic center, while we detect the hydrogen recombination line Brα only from the ring. Hence the observed spectra change dramatically within the central 1 kpc region.

본 연구는 근적외선분광법의 현장 이용성 및 이탈리안 라이그라스 원물 사일리지의 신속한품질평가를 위한 근적외선 검량식을 개발하기 위하여 전국에서 이탈리안 라이그라스 사일리지 약 450여 점을 수집하였다. 각각의 시료는근적외선 분광기를 이용하여 스펙트럼을 측정한 후 측정된 스펙트럼과 실험실 분석값 간에상관관계를 이용한 다변량회귀분석법을 통하여검량식을 유도한 다음 각 성분별로 예측 정확성을 평가하였다. 이탈리안 라이그라스 원물사일리지의 수분 함량에 대한 검량식 개발 결과는 검량식 작성시 표준오차(SEC)가 0.94%(R2=0.99)로 매우 우수한 정확성을 보였으며 개발된 검량식의 상호검증(SECV) 결과는 1.27%(R2=0.98)로 나타났다. ADF, NDF 및 조단백질함량 평가를 위해 개발된 검량식의 상호검증(SECV) 결과는 각각 1.26% (R2=0.88), 2.0%(R2=0.84) 및 0.96% (R2=0.93)으로 나타났으며조회분함량 평가에 대한 검량식 개발 결과는다소 낮은 정확성(SECV 0.72%, R2=0.62)을 나타내었다. 사일리지의 발효품질의 주요 평가항목인 pH와 젖산함량 평가에 대한 검량식 개발결과(SEC)는 각각 0.41 (R2=0.85), 0.18% (R2=0.92)로 나타났으며 개발된 검량식의 상호검증결과(SECV)는 각각 0.56 (R2=0.78), 0.31%(0.81)로 나타났다.