• Proceedings of the Korean Society for Food Science of Animal Resources Conference
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• 2000.11a
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• pp.1-14
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• 2000

The Near Infrared region of the energy spectrum was first discovered by Hershel in the year 1800. The principles of NIR is based on light absorption of specific organic chemical bonds. The absorption at each wavelength is measured and a spectre is obtained. The spectre is then treated mathematically and with the absorption data is converted to absolute units via a calibration. In the last two decades it has developed dramatically. With the invention of computers and the ability to treat a large amount of data in a very short time the use of NIR for many different purposes has developed very fast. During the last decade with the aid of very powerful PC's the application of NIR technology has become even more widespread. Now or days development of very robust calibrations can be done in a relatively short time with a minimum of resources. The use of Near Infrared Spectroscopy (NIR) in the Meat industry is relatively new. The first installations were taken into operation in the 80ties. The Meat Industry in often referred to as rather conservative and slow to embrace new technologies, they stay with the old and proven methods. The first NIR instruments used by the Meat Industry, and most other industries, were multipurpose build, which means that the sample presentation was not well suited to this particular application, or many other applications for that sake. As the Meat Industry grows and develops to meet the demands of the modern markets, they realise the need for better control of processes and final products. From the early 90 ties and onward the demand for 'rear time' rapid results starts growing, and some suppliers of NIR instruments (and instruments based on other technologies, like X-ray) start to develop and manufacture instrumentation dedicated to the particular needs of the Meat Industry. Today it is estimated that there are approximately 2000 rapid instruments placed in the Meat industry world-wide. By far most of these are used as at-line or laboratory installations, but the trend and need is moving towards real on-line or in-line solutions. NIR is the most cost effective and reproducible analytical procedure available for the twenty first century.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.2
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• pp.76.2-76.2
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• 2017

We present Subaru Near-Infrared (NIR) photometry for globular clusters (GCs) in the giant elliptical galaxy NGC 4649 (M60) belonging to the Virgo cluster. NIR data are obtained in Ks-band with the Subaru/MOIRCS, and matching HST/ACS optical data available in literature are used to explore the origin of GC color bimodality. A clear bimodal color distribution is observed in the optical color (g-z), in which the ratio between blue and red GCs is 4:6. By contrast, the more metallicity-sensitive optical-NIR colors (g-Ks, z-Ks) show a considerably weakened bimodality in their distributions. The color-color relation of the optical and NIR colors for the GC system shows a nonlinear feature, supporting that the optical color bimodality observed in NGC 4649 GC system is caused by nonlinear color-metallicity relations (CMRs).

• The Bulletin of The Korean Astronomical Society
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• v.35 no.1
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• pp.75.1-75.1
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• 2010

The first near-infrared (NIR) spectroscopic survey of SDSS-selected blue early-type galaxies (BEGs) has been conducted using the AKARI/IRC. The NIR spectra of 36 BEGs are successfully secured, which are well balanced in their SF/Seyfert/LINER type composition. For high signal-to-noise ratio, we stack the BEG spectra all and in bins of several properties: color, specific star formation rate and optically-determined spectral type. We estimate the NIR continuum slope and the 3.3 micron PAH emission equivalent width in the stacked BEG spectra, and compare them with those of SSP model galaxies and known ULIRGs. We first report the NIR spectral features of BEGs and discuss the nature of BEGs based on the comparison with other objects.

• Bulletin of the Korean Chemical Society
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• v.22 no.1
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• pp.37-42
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• 2001

Ridge regression is compared with multiple linear regression (MLR) for determination of Research Octane Number (RON) when the baseline and signal-to-noise ratio are varied. MLR analysis of near-infrared (NIR) spectroscopic data usually encounters a collinearity problem, which adversely affects long-term prediction performance. The collinearity problem can be eliminated or greatly improved by using ridge regression, which is a biased estimation method. To evaluate the robustness of each calibration, the calibration models developed by both calibration methods were used to predict RONs of gasoline spectra in which the baseline and signal-to-noise ratio were varied. The prediction results of a ridge calibration model showed more stable prediction performance as compared to that of MLR, especially when the spectral baselines were varied. . In conclusion, ridge regression is shown to be a viable method for calibration of RON with the NIR data when only a few wavelengths are available such as hand-carry device using a few diodes.

• Bulletin of the Korean Chemical Society
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• v.20 no.9
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• pp.1021-1025
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• 1999

Near-infrared (NIR) spectroscopy has been successfully utilized for the rapid identification of six typical petroleum products such as light straight-run (LSR), naphtha, kerosine, light gas oil (LGO), gasoline, and diesel. The spectral features of each product were reasonably differentiated in the NIR region, and the spectral differences provided enough qualitative spectral information for discrimination. For discrimination, principal component analysis (PCA) combined with Mahalanobis distance was used to identify each petroleum product from NIR spectra. The results showed that each product was accurately identified with an accuracy over 95%. Most noticeably, LSR, kerosine, gasoline, and diesel samples were predicted with identification accuracy of 99%. The overall results ensure that a portable NIR instrument combined with a multivariate qualitative discrimination method can be efficiently utilized for rapid and simple identification of petroleum products. This is especially important when local at-site measurements are necessary, such as accidental petroleum leakage and regulation of illegal product blending.

• Journal of Chest Surgery
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• v.52 no.4
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• pp.205-220
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• 2019

Near-infrared (NIR) fluorescence imaging provides a safe and cost-efficient method for immediate data acquisition and visualization of tissues, with technical advantages including minimal autofluorescence, reduced photon absorption, and low scattering in tissue. In this review, we introduce recent advances in NIR fluorescence imaging systems for thoracic surgery that improve the identification of vital tissues and facilitate the resection of tumorous tissues. When coupled with appropriate NIR fluorophores, NIR fluorescence imaging may transform current intraoperative thoracic surgery methods by enhancing the precision of surgical procedures and augmenting postoperative outcomes through improvements in diagnostic accuracy and reductions in the remission rate.

• Journal of Sensor Science and Technology
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• v.26 no.2
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• pp.91-95
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• 2017

Recently, infrared (IR) spectrometers have been required in various fields such as environment, safety, mobile, automotive, and military. This IR dispersive sensor detection method of substances is widely used. In this study, we fabricated a silicon (Si) prism-based near infrared (NIR) spectrometer utilizing micro electro mechanical system (MEMS) technology. Si prism-based NIR spectrometer utilizing MEMS technology consists of upper, middle, and lower substrates. The upper substrate passes through the incident IR ray selectively. The middle substrate, acting as a prism, disperses and separates the incident IR beam. The lower substrate has an amorphous Si (a-Si)-based bolometer array to detect the IR spectrum. The fabricated Si prism-based NIR spectrometer utilizing MEMS technology has the advantage of a simple structure, easy fabrication steps, and a wide NIR region operating range.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1998.06b
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• pp.171-177
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• 1998

The part Ⅰ was for developing regression models to predict the moisture content, protein content and viscosity of brown and milled rice using Near Unfrared (NIR) Reflectance analyzer. The purpose of this study(part Ⅱ) is to measure fundamental data required for the prediction of rice quality , and to develop regression models to predict the protein content of brown and milled rice, brown rice yield from undreid paddy powder by using Near Infrared (NIR) Reflectance analyzer. The results of this study were summarized as follows . The predicted values of protein contents obtained from the undried paddy powder were will correlated to the measured values from brown and milled rice. The predicted yields of brown rice from undried paddy powder were not well correlated to be lab measured values from dried paddy. Continuous study in wavelength selection and of constituent relationship is necessary for practical application.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3122-3122
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• 2001

Documented quality control plays a vital role I the production of technical “Depth filter” sheets used in industries such as Beverage and pharmaceutical. Depth filter sheets which can be up to several millimeters thick are stacker in plate and frame filter systems. They are the core of stainless steel filter systems which can be up to several meters high. FT-NIR Spectroscopy has many potential applications in the whole production line of filter sheets. Raw materials such as different types of cellulose pads, white powdery fillers (e.g. Kieelgur, Perlite) or liquid chemicals such as wet-strength agents we, with the help of NIR, easy to identify. NIR can also determine physical parameters such as particle size, essential for the filtration behavior. FT-NIR can be used for the quality parameters of the final product. Moisture and permeability can be determined, and with the help of the speed of this NIR method it is possible to correct possible faults quickly in the production process. Waste production can be minimized which is good for both the product profitability and the environment. Further tests have shown that it is also possible to use NIR on-line in the production area, to check the concentrations and the homogeneity of the paper suspension consisting of cellulose fibres, fillers and additives.

• Journal of Pharmaceutical Investigation
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• v.37 no.6
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• pp.377-395
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• 2007

Near infrared(NIR) spectral data have been used for the noninvasive analysis of various biological samples. Nonetheless, absorption bands of NIR region are overlapped extensively. It is very difficult to select the proper wavelengths of spectral data, which give the best PCR(principal component regression) models for the analysis of constituents of biological samples. The NIR data were used after polynomial smoothing and differentiation of 1st order, using Savitzky-Golay filters. To find the best PCR models, all-possible combinations of available principal components from the given NIR spectral data were derived by in-house programs written in MATLAB codes. All of the extensively generated PCR models were compared in terms of SEC(standard error of calibration), $R^2$, SEP(standard error of prediction) and SECP(standard error of calibration and prediction) to find the best combination of principal components of the initial PCR models. The initial PCR models were found by SEC or Malinowski's indicator function and a priori selection of spectral points were examined in terms of correlation coefficients between NIR data at each wavelength and corresponding concentrations. For the test of the developed program, aqueous solutions of BSA(bovine serum albumin) and glucose were prepared and analyzed. As a result, the best PCR models were found using a priori selection of spectral points and the final model selection by SEP or SECP.