Abstract:For measuring antimony sulfide by artificial chemical methods had many problems, such as complex operation and long detection time, a novel method for measuring antimony sulfide content rapidly based on Gaussian-peak fitting of Raman spectroscopy was proposed.The antimony samples were firstly characterized by Raman spectroscopy system to get their Raman spectroscopy, and Raw Raman spectra were pretreated including smoothing, background subtraction,spectrum selection and normalization. The mathematical model of single Gauss peak was established based on Gaussian peaks, peak area, half high width and peak position of the gauss curve.Then the Gaussian-peak fitting algorithm was proposed to fit Pretreated Raman spectra. The state transition algorithm was employed to get the optimization solution.The characteristic parameters were obtained to describe the information of Pretreated Raman spectra. The relational model between the spectra and antimony sulfide content was established by the combination of the Gaussian characteristic parameters and partial least squares regression method, so as to predict antimony sulfide content.It was important to verify this model. In the experiment, the calibration model was established by the training samples and was used to predict the testing samples. In order to test the correctness and extrapolation of the model, the proving samples were randomly selected from the training samples,and then the established model was used to predict the antimony content of them.The experimental results showed that applying Gaussian-peak fitting of Raman spectroscopy to measure antimony sulfide content was feasible, and the measuring process was more simple. It is suitable for rapid analysis of mineral composition.
李原鹰,徐德刚,桂卫华,阳春华,蔡耀仪. 基于拉曼光谱高斯分峰拟合的硫化锑含量检测方法[J]. 光谱学与光谱分析, 2017, 37(12): 3743-3748.
LI Yuan-ying, XU De-gang, GUI Wei-hua, YANG Chun-hua, CAI Yao-yi. A Novel Method for Measuring Antimony Sulfide Content Based on Gaussian-Peak Fitting of Raman Spectroscopy. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2017, 37(12): 3743-3748.
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