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利用拉曼光谱对天然蓝宝石和铍扩散蓝宝石进行无损鉴别的可行性。

Feasibility for non-destructive discrimination of natural and beryllium-diffused sapphires using Raman spectroscopy.

作者信息

Chang Kyeol, Lee Sanguk, Park Jimin, Chung Hoeil

机构信息

Analytical Spectroscopy Lab, Department of Chemistry and Research Institute for Convergence of Basic Sciences, Hanyang University, Seoul 133-791, Korea.

Analytical Spectroscopy Lab, Department of Chemistry and Research Institute for Convergence of Basic Sciences, Hanyang University, Seoul 133-791, Korea.

出版信息

Talanta. 2016 Mar;149:335-340. doi: 10.1016/j.talanta.2015.11.064. Epub 2015 Nov 26.

DOI:10.1016/j.talanta.2015.11.064
PMID:26717849
Abstract

Raman spectroscopy based non-destructive discrimination between natural and beryllium-diffused (Be-diffused) sapphires has been attempted. The initial examination of Raman image acquired on a sapphire revealed that microscopic structural and compositional heterogeneity was apparent in the sample, so acquisition of spectra able to represent a whole body of sapphire rather than a localized area was necessary for a reliable discrimination. For this purpose, a wide area illumination (WAI) scheme (illumination area: 28.3mm(2)) providing a large sampling volume was employed to collect representative Raman spectra of sapphires. Upon the diffusion of Be into a sapphire, the band shift originated from varied lattice structure by substitution of Be at cation sites was observed and utilized as a valuable spectral signature for the discrimination. In the domain of principal component (PC) scores, the groups of natural and Be-diffused sapphires were identifiable with minor overlapping and the cross-validated discrimination error was 7.3% when k-Nearest Neighbor (k-NN) was used as a classifier.

摘要

基于拉曼光谱的天然蓝宝石与铍扩散(Be扩散)蓝宝石之间的无损鉴别已被尝试。对蓝宝石上获取的拉曼图像的初步检查表明,样品中微观结构和成分的不均匀性很明显,因此为了进行可靠的鉴别,获取能够代表整个蓝宝石体而不是局部区域的光谱是必要的。为此,采用了一种提供大采样体积的广域照明(WAI)方案(照明面积:28.3mm²)来收集蓝宝石的代表性拉曼光谱。当铍扩散到蓝宝石中时,观察到由于阳离子位点上的铍替代而导致的晶格结构变化引起的谱带位移,并将其用作鉴别用的有价值的光谱特征。在主成分(PC)得分域中,天然蓝宝石和Be扩散蓝宝石组可以识别,有少量重叠,当使用k近邻(k-NN)作为分类器时,交叉验证的鉴别误差为7.3%。

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