• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.5
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• pp.2393-2399
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• 2013

In this paper, we proposed a method to screening of Alzheimer's disease (AD) from Raman spectra of platelet with synthesis of basis spectra using singular value decomposition (SVD). Raman spectra of platelet from AD transgenic mice are preprocessed with denoising, removal background and normalization method. The column vectors of each data matrix consist of Raman spectrum of AD and normal (NR). The matrix is factorized using SVD algorithm and then the basis spectra of AD and NR are determined by 12 column vectors of each matrix. The classification process is completed by select the class that minimized the root-mean-square error between the validation spectrum and the linear synthesized spectrum of the basis spectra. According to the experiments involving 278 Raman spectra, the proposed method gave about 97.6% classification rate, which is better performance about 6.1% than multi-layer perceptron (MLP) with extracted features using principle components analysis (PCA). The results show that the basis spectra using SVD is well suited for the diagnosis of AD by Raman spectra from platelet.

• Proceedings of the Korean Ceranic Society Conference
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• 2000.04a
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• pp.17.2-17.2
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• 2000

• Proceedings of the Korean Ceranic Society Conference
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• 2000.04a
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• pp.93.1-93.1
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• 2000

• Proceedings of the Korean Ceranic Society Conference
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• 2000.10a
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• pp.184.2-184
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• 2000

• Proceedings of the Materials Research Society of Korea Conference
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• 1997.10a
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• pp.126-126
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• 1997

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.461-462
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• 2006

• Polymer Science and Technology
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• v.25 no.5
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• pp.434-439
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• 2014

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.11
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• pp.3054-3059
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• 2009

Natural black diamonds of single crystal, polycrystalline, and agglomerated roughs become important for their industrial and gem stone application. We performed the conventional gemological tests of thermal diffusion, apparent density, scratch test, and magnification test as well as the advanced tests of Raman spectroscopy, X-ray diffraction test and Lang topography. We conclude that scratch test with SiC paper was the most efficient method in view point of speed and cost. Raman spectroscopy and XRD were useful for identification of diamond while Lang topography offered a good visualization method of the grain structure of polycrystalline black diamond roughs.

• Journal of the Mineralogical Society of Korea
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• v.24 no.4
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• pp.289-300
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• 2011

Atomistic origins of carbon solubility into silicates are essential to understand the effect of carbon on the properties of silicates and evolution of the Earth system through igneous and volcanic processes. Here, we investigate the atomic structure and NMR properties of dissolved carbon in enstatite using Raman spectroscopy and quantum chemical calculations. Raman spectrum for enstatite synthesized with 2.4. wt% of amorphous carbon at 1.5 GPa and $1,400^{\circ}C$ shows vibrational modes of enstatite, but does not show any vibrational modes of $CO_2$ or ${CO_3}^{2-}$. The result indicates low solubility of carbon into enstatite at a given pressure and temperature conditions. Because $^{13}C$ NMR chemical shift is sensitive to local atomic structure around carbon and we calculated $^{13}C$ NMR chemical shielding tensors for C substituted enstatite cluster as well as molecular $CO_2$ using quantum chemical calculations to give insights into $^{13}C$ NMR chemical shifts of carbon in enstatite. The result shows that $^{13}C$ NMR chemical shift of $CO_2$ is 125 ppm, consistent with previous studies. Calculated $^{13}C$ NMR chemical shift of C is ~254 ppm. The current calculation will alllow us to assign potential $^{13}C$ NMR spectra for the enstatite dissolved with carbon and thus may be useful in exploring the atomic environment of carbon.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.1
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• pp.16-22
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• 2012

In this paper, we proposed a method of baseline correction for analysis of Raman spectra of platelets from Alzheimer's disease (AD) transgenic mice. Measured Raman spectra include the meaningful information and unnecessary noise which is composed of baseline and additive noise. The Raman spectrum is divided into the local region including several peaks and the spectrum of the region is modeled by curve fitting using Gaussian model. The additive noise is clearly removed from the process of replacing the original spectrum with the fitted model. The baseline correction after interpolating the local minima of the fitted model with linear, piecewise cubic Hermite and cubic spline algorithm. The baseline corrected models extract the feature with principal component analysis (PCA). The classification result of support vector machine (SVM) and maximum $a$ posteriori probability (MAP) using linear interpolation method showed the good performance about overall number of principal components, especially SVM gave the best performance which is about 97.3% true classification average rate in case of piecewise cubic Hermite algorithm and 5 principal components. In addition, it confirmed that the proposed baseline correction method compared with the previous research result could be effectively applied in the analysis of the Raman spectra of platelet.