• 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.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1191-1191
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• 2001

Generation of precise, accurate, and robust calibration models for spectroscopic methods of analysis can be time-consuming, expensive, and sometimes difficult to achieve. For these reasons, efforts have been made to find ways in which the calibration from one instrument can be moved to another with minimal performance reduction. A slight shift in nomenclature from the common term calibration transfer to the term calibration transport is used here to help resolve the subtle difference between two means of moving a calibration from one instrument to another. The former term denotes a transfer procedure that includes mathematical manipulation of the calibration data via some determined transfer function, whereas the latter term does not. Todays generation of process and laboratory FTNIR analyzers is capable of not only achieving calibration transfer, but also calibration transport often without the need of slope or bias adjustments. Several studies are used to examine the boundaries of the extent to which calibration transport is achieved in the refining industry. Data collected on multiple on-line and laboratory FTNIR analyzers located in multiple countries are considered, and the ultimate limitations discussed.

• Proceedings of the Korean Society of Dyers and Finishers Conference
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• 2009.03a
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• pp.185-186
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• 2009

• Journal of The Korean Astronomical Society
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• v.41 no.5
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• pp.121-137
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• 2008

We present optical and near-infrared imaging and long-slit spectroscopy for the blue compact dwarf galaxy (BCD) Mrk 49 in the Virgo Cluster. The surface brightness distribution analysis shows that Mrk 49 consists of an off-centered blue bright compact core of r = 10" and a red faint outer exponential envelope. The $H_{\alpha}$ image and color difference suggest that these two components have different stellar populations: a high surface brightness population of massive young stars and an underlying low surface brightness population of older stars. The redder near-infrared colors of the inner most region suggest that the near-infrared flux of Mrk 49 originates from evolved massive stars associated with the current star-forming activity. The total apparent magnitude is $B_T\;=\;14.32$ mag and the mean effective surface brightness is ${\mu}_{eff}(B)\;=\;21.56$ mag $arcsec^{-2}$. Long-slit spectroscopy shows that Mrk 49 rotates apparently as a solid body within r = 10" in a plane at position angle 55 degrees with an amplitude of about $20\;km\;sec^{-1}$. The measured radial velocity of Mrk 49 was derived as $1,535\;km\;sec^{-1}$; and the total mass of stars and gases is in the range of 3 to $6\;{\times}\;10^9\;M_{\odot}$. The mass-to-light ratios for the central region of Mrk 49 in I and B band are estimated 1.0 and 0.5, respectively. The upper limit of the dark matter to visible matter ratio seems to be < 5. The oxygen abundance is $12\;+\;\log(O/H)\;=\;8.21\;{\pm}\; 0.1$ which is about one quarter of the solar value while the relative helium abundance appears to be similar to that of the sun.

• The Bulletin of The Korean Astronomical Society
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• v.42 no.1
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• pp.54.2-54.2
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• 2017

We present the results of high-resolution optical (R ~ 30,000) and near-infrared (R ~ 45,000) spectroscopic monitoring observations of a new FU Orionis-like young stellar object, 2MASS J06593158-0405277. FU Orionis objects (FUors) are well-studied examples of episodic accretion because of their outburst phenomenon. Recently, 2MASS J06593158-0405277 exhibited an outburst and was identified as an FUor. It provides an important opportunity to investigate the whole FUors phenomenon from its pre-outburst to its post-outburst phase. We observed 2MASS J06593158-0405277 with the Bohyunsan Optical Echelle Spectrograph (BOES) of the Bohyunsan Optical Astronomy Observatory (BOAO) and the Immersion GRating INfrared Spectrograph (IGRINS) of Harlan J. Smith Telescope (HJST) at the McDonald observatory since December 24, 2014. We detected a number of lines and present here our analysis for time variations of those spectral lines.

• The Bulletin of The Korean Astronomical Society
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• v.38 no.1
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• pp.49.1-49.1
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• 2013

We have been carrying out near-infrared (NIR) spectroscopy as well as [Fe II] narrow band imaging observations of Cassiopeia A supernova remnant (SNR). In this presentation, we describe the spectral classification of the iron knots around the SNR. From eight long-slit spectroscopic observations for the iron-bright shell, we identified a total of 61 iron knots making use of a clump-finding algorithm, and performed principal component analysis in an attempt to spectrally classify the iron knots. Three major components have emerged from the analysis; (1) Iron-rich, (2) Helium-rich, and (3) Sulfur-rich groups. The Helium-rich knots have low radial velocities (${\mid}v_r{\mid}$ < 100 km/s) and radiate strong He I and [Fe II] lines, that match well with Quasi-Stationary Flocculi (QSFs) of circumstellar medium, while the Sulfur-rich knots show strong lines of oxygen burning materials with large radial velocity up to +2000 km/s, which imply that they are supernova ejecta (i.e. Fast-Moving Knots). The Iron-rich knots have intermediate characteristics; large velocity with QSF-like spectra. We suggest that the Iron-rich knots are missing "pure" iron materials ejected from the inner most region of the progenitor and now encountering the reverse shock.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.36 no.2
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• pp.88-93
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• 2003

NIR spectroscopic analysis was used for the measurement of deproteinization and deacetylation to apply the merits of NIR spectroscopic analysis to the quality management in the process of chitin and chitosan production. In measuring squid pen and red snow crab shell, which are raw materials of chitin and chitosan by NIR there were typical peaks in 1200 nm, 1510 nm, 2050 nm and 2180 nm. Squid pen had somewhat higher peak than red snow crab shell. In producing chitin, amount of protein was decreased. Measuring it by NIR, reduction of protein caused by deproteinization was identified in producing chitin. Chitosan is a derivative material made from chitin by processing the deacetylation. During this processing, acetyl groups were removed and amide bends were appeared. From NIR spectra, peaks at 1530 nm and 2030 nm indicated amide II peak of chitosan, and these peaks were used for identifying the differences of structure between chitin and chitosan. The error in measurement of nonidentified sample was below $1\%$ and the error in the standard curve was below 0.006. These errors were very low and the accuracy of NIR was considered to be superior to the existing methods.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.48-54
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• 2013

Purpose: This study evaluated the feasibility of using a discrete wavelet transform (DWT) method as a preprocessing tool for visible/near-infrared spectroscopy (VIS/NIRS) with a spectroscopic transmittance dataset for predicting the internal quality of cherry tomatoes. Methods: VIS/NIRS was used to acquire transmittance spectrum data, to which a DWT was applied to generate new variables in the wavelet domain, which replaced the original spectral signal for subsequent partial least squares (PLS) regression analysis and prediction modeling. The DWT concept and its importance are described with emphasis on the properties that make the DWT a suitable transform for analyzing spectroscopic data. Results: The $R^2$ values and root mean squared errors (RMSEs) of calibration and prediction models for the firmness, sugar content, and titratable acidity of cherry tomatoes obtained by applying the DWT to a PLS regression with a set of spectra showed more enhanced results than those of each model obtained from raw data and mean normalization preprocessing through PLS regression. Conclusions: The developed DWT-incorporated PLS models using the db5 wavelet base and selected approximation coefficients indicate their feasibility as good preprocessing tools by improving the prediction of firmness and titratable acidity for cherry tomatoes with respect to $R^2$ values and RMSEs.

• Fire Science and Engineering
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• v.24 no.3
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• pp.20-24
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• 2010

When the fire occurred in building, the fire-resistance-structure has to be constructed to prevent collapse of building and to have a time for evacuation of peoples. because of the features of the fireresistance test is similar with real scale, there is no way to confirm quality of fire-resistive-structure in building construction site. Therefore the purpose of this study, a study by spectroscopic analysis using near-infrared spectroscopy (NIR), is to suggest of useful and scientific testing-methods in building construction site by identification-analysis-study for fire resistive paint.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1131-1131
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• 2001

To develop a nondestructive quality evaluation technique of fruits, a K-mean algorism is applied to near infrared (NIR) spectroscopy of apples. The K-mean algorism is one of neural network partition methods and the goal is to partition the set of objects O into K disjoint clusters, where K is assumed to be known a priori. The algorism introduced by Macqueen draws an initial partition of the objects at random. It then computes the cluster centroids, assigns objects to the closest of them and iterates until a local minimum is obtained. The advantage of using neural network is that the spectra at the wavelengths having absorptions against chemical bonds including C-H and O-H types can be selected directly as input data. In conventional multiple regression approaches, the first wavelength is selected manually around the absorbance wavelengths as showing a high correlation coefficient between the NIR $2^{nd}$ derivative spectrum and Brix value with a single regression. After that, the second and following wavelengths are selected statistically as the calibration equation shows a high correlation. Therefore, the second and following wavelengths are selected not in a NIR spectroscopic way but in a statistical way. In this research, the spectra at the six wavelengths including 900, 904, 914, 990, 1000 and 1016nm are selected as input data for K-mean analysis. 904nm is selected because the wavelength shows the highest correlation coefficients and is regarded as the absorbance wavelength. The others are selected because they show relatively high correlation coefficients and are revealed as the absorbance wavelengths against the chemical structures by B. G. Osborne. The experiment was performed with two phases. In first phase, a reflectance was acquired using fiber optics. The reflectance was calculated by comparing near infrared energy reflected from a Teflon sphere as a standard reference, and the $2^{nd}$ derivative spectra were used for K-mean analysis. Samples are intact 67 apples which are called Fuji and cultivated in Aomori prefecture in Japan. In second phase, the Brix values were measured with a commercially available refractometer in order to estimate the result of K-mean approach. The result shows a partition of the spectral data sets of 67 samples into eight clusters, and the apples are classified into samples having high Brix value and low Brix value. Consequently, the K-mean analysis realized the classification of apples on the basis of the Brix values.