• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1256-1256
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• 2001

The physico-chemical and texture characteristics of meat determine the nutritional, technological and sensory quality. However, the analysis of meat quality requires expensive, laborious and time consuming analytical methods. The objective of this study was to evaluate NIR spectroscopy using transmittance for determining the moisture, fat, protein and total pigment content, the water holding capacity (WHC) and the toughness of beef meat. A total of 318 spectra were recorded from ground beef samples by a Feed Analyzer 1265 of Infratec. The samples were obtained from the Longissimus muscle of the 10$^{th}$ rib of yearling bulls, ground with an electrical chopper, vacuum packaged, aged during 7 days and frozen at -24$^{\circ}C$ until the analyses were done. Moisture content was measured by oven drying at 10$0^{\circ}C$, fat content was determined by Soxhlet extraction and protein content was estimated from nitrogen content using the Kjeldahl analysis. The total pigment content was determined by the method of Hornsey and the WHC using the method of filter paper press. The instrumental evaluation of texture (maximum load WB, maximum stress MS and toughness) was conducted in an Instron equipment with a Warner-Bratzler shearing device. This analysis was performed on a chop of 3.5 cm obtained from the longissimus of the 8$^{th}$ rib, aged during 7 days, kept frozen at -24$^{\circ}C$ and cooked before the analysis. Near infrared spectra were recorded as log 1/T (T=transmittance) at 2 nm intervals from 850 to 1050 nm using a Feed Analyzer 1265 of Infratec. Calibrations were performed with the WinISI software (vs. 1.02) using the MPLS method. To examine the effect of scatter correction o. derivation of spectra on the calibration performance, calibrations were calculated with the crude spectra or pretreated with different mathematical treatments (inverse MSC, SNVD) and/or second derivative operation. For chemical composition, the use of the scatter corrections improved the calibration statistics, in terms of lower SECV and higher $r^2$. In most of the variables, the use of the 2$^{nd}$ derivative improved the predictions, mainly when combined with the SNVD treatment. However, for predicting the texture traits, the best estimation was obtained from the crude spectrum. These results showed that the equations obtained for predicting moisture, fat and total pigments were very accurate, with $r^2$ being higher that 0.9. However, the prediction of the texture traits (WB, MS, toughness) from ground meat was poor.

• KOREAN JOURNAL OF CROP SCIENCE
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• 제50권6호
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• pp.394-399
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• 2005

Near Infrared Spectroscopy (NIRS) has been used as a tool for the rapid, accurate and nondestructive assay of the fresh rice leaf in nitrogen content. NIRS used in this study was visible and near infrared spectroscopy type instrument, Foss model 6500. To obtain a useful calibration equation, standard regression between the data was analyzed by chemical analysis and by NIRS method. Accuracy of calibration equation for nitrogen content on fresh leaf of rice were 0.879, 0.858 and 0.819, respectively. Accuracy of calibration equation after outlier treatment increased as 0.017, 0.02 and 0.061 improved each with 0.896, 0.878 and 0.880, respectively. Calibration equation combined using merge function after accuracy of calibration equation more increased by 0.911. Difference analysis value between calibration equation and lab value by kjeldahl showed $0.001\%$. With this as same result is the possibility of closing the deterioration of the sample in order to omit a construction and pulverization process it is judged with the fact that the nitrogen content measurement of the fresh rice leaf which the possibility of reducing an hour and an expense is by a near infrared spectroscopy technique will be possible.

• The Bulletin of The Korean Astronomical Society
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• 제44권1호
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• pp.66.3-66.3
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• 2019

We present near-infrared spectroscopic results for three nearby class I sources, IRAS 03445+3242, IRAS 04239+2436 and ESO $H{\alpha}$ 279a. We detected many molecular and atomic line emissions, e.g., $H_2$, [Fe II], Hydrogen Bracket series recombination, Ca I, Na I & CO overtone band, from these sources using the high-resolution Immersion GRating INfrared Spectrometer (IGRINS; R~45,000). Previous studies showed that all the three sources posses actively accreting Keplerian disks. We performed spectral analysis to understand the origin of Hydrogen Bracket series recombination lines. We also estimated the accretion properties and mass loss rates of circumstellar disks for all the three sources.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1530-1530
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• 2001

The objectives of this study were to develop models to predict quality parameters of Korean bee-honeys by visible and NIR spectroscopic technique. Two kinds of bee-honey fronl acacia and polyflower sources were tested in this study. The honeys were harvested in the spring of 2000 and stored in the storage facility at 20$^{\circ}C$ during experiments. Total of 394 samples of honey were analyzed. Reflectance spectra, moisture contents, ash, invert sugar, sucrose, F/G (fructose/glucose) ratio, HMF (hydroxymethyl furfural), and C12/C13 ratio of honeys were measured. The average values for the tested honeys were 19.9% of moisture contents, 0.12% of ash, 68.4% of invert sugar, 5.7% of sucrose, 1.27 of F/G(fructose/glucose) ratio, 14.4 mg/kg of HMF, and -19.1 of C12/C13 ratio. A spectrophotometer, equipped with a single-beam scanning monochromator (NIR Systems, Model 6500, USA) and a horizontal setup module, was used to collect reflectance data from honey. The reflectance spectra were measured in wavelength ranges of 400∼2,498 nm. with 2 nm of interval. Thirty-two repetitive scans were averaged, transformed to log(1/Reflectance), and then were stored in a microcomputer file, forming one spectrum per measurement. A sample cell and reflectance plate were made to hold honey samples constantly. Spectra of honey samples were divided into a calibration set and a validation set. The calibration set was used during model development, and the validation set was used to predict quality parameters from unknown spectra. The PLS(Partial Least Square) models were developed to predict the quality parameters of honeys. The first and the second derivatives of raw spectra were also used to develop the models with proper smoothing gap. The MSC (multiplicative scatter correction) and the SNV & Dtr.(standard normal variate and detranding) preprocessing were applied to all spectra to minimize sample-to-sample light scatter differences. The PLS models showed good relationships between predicted and measured quality parameters of honeys in the wavelength range of 1100∼2200 nm. However, the PLS analysis was not good enough to predict HMF of honeys.

• Journal of The Korean Society of Grassland and Forage Science
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• 제35권2호
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• pp.125-130
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• 2015

The objective of this study was to investigate the feasibility of using near infrared spectroscopy (NIRS) to discriminate between grass spices. A combination of NIRS and chemometrics was used to discriminate between Italian ryegrass, perennial ryegrass, and tall fescue seeds. A total of 240 samples were used to develop the best discriminant equation, whereby three spectra range (visible, NIR, and full range) were applied within a 680 nm to 2500 nm wavelength. The calibration equation for the discriminant analysis was developed using partial least square (PLS) regression and discrimination equation (DE) analysis. A PLS discriminant analysis model for the three spectra range that was developed with the mathematic pretreatment "1,8,8,1" successfully discriminated between Italian ryegrass, perennial ryegrass, and tall fescue. An external validation indicated that all of the samples were discriminated correctly. The discriminant accuracy was shown as 68%, 78%, and 73% for Italian ryegrass, perennial ryegrass, and tall fescue, respectively, with the NIR full-range spectra. The results demonstrate the usefulness of the NIRS-chemometrics combination as a rapid method for the discrimination of grass species by seed.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 한국근적외분광분석학회 2001년도 NIR-2001
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• pp.1061-1061
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• 2001

The scope of precision agriculture is to reach the put up cultivation goals by adjusting inputs as precise as possible after what is required by the soil and crop potentials, on a high spatial resolution. Consequently, precision agriculture is also often called site specific agriculture. Regulation of field inputs “on the run” has been made possible by the GPS (Geographical Position System)-technology, which gives the farmer his exact real time positioning in the field. The general goal with precision agriculture is to apply inputs where they best fill their purpose. Thus, resources could be saved, and nutrient losses as well as the impact on the environment could be minimized without lowering total yields or putting product quality at risk. As already indicated the technology exists to regulate the input based on beforehand decisions. However, the real challenge is to provide a reliable basis for decision-making. To support high spatial resolution, extensive sampling and analysis is required for many soil and plant characteristics. The potential of the NIR-technology to provide rapid, low cost analyses with a minimum of sample preparation for a multitude of characteristics therefore constitutes a far to irresistible opportunity to be un-scrutinized. In our work we have concentrated on soil-analysis. The instrument we have used is a Bran Lubbe InfraAlyzer 500 (1300-2500 nm). Clay- and organic matter-contents are soil constituents with major implications for most properties and processes in the soil system. For these constituents we had a 3000-sample material provided. High performance models for the agricultural areas in Sweden have been constructed for clay-content, but a rather large reference material is required, probably due to the large variability of Swedish soils. By subdividing Sweden into six areas the total performance was improved. Unfortunately organic matter was not as easy to get at. Reliable models for larger areas could not be constructed. However, through keeping the mineral fraction of the soil at minimal variation good performance could be achieved locally. The influence of a highly variable mineral fraction is probably one of the reasons for the contradictory results found in the literature regarding organic matter content. Tentative studies have also been performed to elucidate the potential performance in contexts with direct operational implications: lime requirement and prediction of plant uptake of soil nitrogen. In both cases there is no definite reference method, but there are numerous indirect, or indicator, methods suggested. In our study, field experiments where used as references and NIR was compared with methods normally used in Sweden. The NIR-models performed equally or slightly better as the standard methods in both situations. However, whether this is good enough is open for evaluation.

• The Korean Journal of Food And Nutrition
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• 제23권4호
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• pp.582-587
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• 2010

본 연구에서는 녹두 유전자원의 지방산 함량을 신속 대량 검정하는 기술을 개발하여 유전자원 활용 및 육종 촉진에 기여하고자 하였다. 유전자원 평가에 적합한 신속하고 비파괴적인 지방산 함량 평가기술을 개발하기 위해 공시자원 1,125점의 녹두 종자를 종실상태와 분쇄한 분말상태로 근적외선분광분석기(NIR)를 이용하여 1,104~2,494 nm에서의 스펙트럼을 얻고 이들 중 스펙트럼이 중복되지 않는 원산지가 다양한 대표자원 106점을 선발하여 일반적인 방법으로 지방산 함량을 분석하고, 이 값과 NIR 스펙트럼 흡광도값 간의 상관분석을 위한 calibration set로 활용하였다. 그 결과 palmitic acid, stearic acid, oleic acid, linoleic acid, linolenic acid 및 total fatty acid에 대한 NIR 흡광도와의 상관계수 $R^2$이 각각 0.74, 0.18, 0.12, 0.72, 0.48 및 0.78로 나타났고, 이들 중 $R^2$가 높은 검량식을 미지의 시료 10점으로 검증한 결과, palmitic, linoleic 및 total fatty acid에 대한 검증 상관계수 $R^2$이 0.96, 0.74, 0.81로 나타나, 다양한 녹두 유전자원의 지방산함량 신속 대량 예측에 유효하게 활용될 수 있는 것으로 나타났다. 한편, 공시된 녹두 유전자원 115점 중에서 자원번호 IT208075 자원은 저 지방산 자원($14.24\;mg\;g^{-1}$)으로 선발되었고, IT163279 자원은 고 지방산 자원($18.43\;mg\;g^{-1}$)으로 선발되어 향후 녹두작물의 성분육종에 유용할 것으로 생각된다.

• Smart Media Journal
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• 제7권4호
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• pp.37-43
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• 2018

Most of fruit quality classification has been done by time consuming, inaccurate and intensive manual labor. This study proposed an automated fruit grading system based on appearances and internal flavors. In this study, image processing technique and a weight checker were used to measure the value of appearance features and the near infrared spectroscopy analysis method was used to estimate the value of internal flavors. Additionally, I suggested 8x8x5x5 ANN based fruit quality classifier model to grade fruits quality. The proposed automated fruit quality classification system is expected to be very beneficial for many farms where heavy manual labor is usually needed for fruit quality classification.

• The Bulletin of The Korean Astronomical Society
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• 제44권1호
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• pp.67.3-67.3
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• 2019

Massive stars play an important role in terms of their feedback, but their formation process is poorly understood. Direct observational evidence for the formation of massive stars through accretion disks is rare. Hence the detection of disks in massive young stellar objects (MYSOs), if any, could be important to constrain the formation process of massive stars. The inner gaseous disk can be observed by the high-resolution near-infrared spectroscopy. We observed a MYSO, Min 2-62, using IGRINS and detected a double peak feature, which could be an evidence of a rotating disk, in the Bracket and Pfund series lines. We report the preliminary observational results of Min 2-62 with IGRINS.

• Journal of The Korean Society of Grassland and Forage Science
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• 제39권4호
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• pp.292-297
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• 2019

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.