“红碧石”的宝石矿物学特征

近年来,珠宝市场上出现了一种商业名称为"红碧石"的玉石,因其外观,特别是红色的体色与南红相近,常被作为南红的仿制品进行出售。本文采用常规宝石学测试、红外光谱、激光拉曼光谱、X射线粉晶衍射、紫外-可见、近红外光谱等方法,对"红碧石"样品的宝石矿物学特征进行研究。结果表明,"红碧石"整体呈现弱-中等玻璃光泽,折射率范围为1.53~1.54,摩氏硬度为6~7,与石英质玉石基本一致。红外光谱、激光拉曼光谱、X射线粉晶衍射的测试结果表明:"红碧石"的主要组成矿物为石英,次要矿物为赤铁矿和钙铁榴石。近红外光谱中5166~5200 cm^(-1)和4322 cm^(-1)附近的吸收指示了其中分子水及-OH的存在。对紫外-可见-近红外光谱进行一阶求导分析,"红碧石"样品均显示576 nm处赤铁矿的主峰,表明"红碧石"为赤铁矿致色。

Terahertz spectrum clustering of traditional Chinese medicine based on first derivative characteristics

In order to deal with the unclear absorption peak caused by the absorption peak overlap of traditional Chinese medicine(TCM)and other mixtures,a method of three unsupervised clustering algorithms as K-means,K-medoids and Fuzzy C-means(FCM)combined with the first derivative characteristics of terahertz absorption spectrum,is proposed to perform the terahertz spectra clustering of Sanchi and other three kinds of TCM compared with their easily-confused products(ECPs).These three unsupervised clustering methods complement the scope of the supervised learning classification method.The first derivative of the spectrum could amplify the difference in the absorption coefficient with different substances,so that the obvious absorption peak can be revealed.Experiments shows that these three clustering algorithms can achieve good results by combining the origin absorption coefficient with its first-order derivative as the characteristic data,and among which K-means does the best with the accuracy of95.32%.Compared with pure absorption coefficient data clustering,the accuracy in this study has been significantly improved,especially for the non-absorption-peak TCM classification.And the accuracy of K-means algorithm is improved by5.38%.Besides,clustering algorithms in this study have strong anti-interference ability to the error data.

Simultaneous determination of brilliant blue and indigotine by derivative fluorescence spectrometry combined with WT-RBFNN

The mixed solutions of brilliant blue and indigotine are prepared and the fluorescence spectra of them are experimentally measured. The serious overlapping spectra of brilliant blue and indigotine are solved by means of the first-derivative fluorescence spectrometry. The wavelet coefficients, obtained by compressing the spectral data using wavelet transformation （WT）, are taken as inputs to establish the radial basis function neural network （RBFNN）. The neural network model can realize simultaneous determination of brilliant bFue and indigotine, and the mean relative errors of both compounds are 1.84% and 1.26%, respectively