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Automated decomposition algorithm for Raman spectra based on a Voigt line profile model

机译：基于Voigt线轮廓模型的拉曼光谱自动分解算法

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摘要

Raman spectra measured by spectrometers usually suffer from band overlap and random noise. In this paper, an automated decomposition algorithm based on a Voigt line profile model for Raman spectra is proposed to solve this problem. To decompose a measured Raman spectrum, a Voigt line profile model is introduced to parameterize the measured spectrum, and a Gaussian function is used as the instrumental broadening function. Hence, the issue of spectral decomposition is transformed into a multiparameter optimization problem of the Voigt line profile model parameters. The algorithm can eliminate instrumental broadening, obtain a recovered Raman spectrum, resolve overlapping bands, and suppress random noise simultaneously. Moreover, the recovered spectrum can be decomposed to a group of Lorentzian functions. Experimental results on simulated Raman spectra show that the performance of this algorithm is much better than a commonly used blind deconvolution method. The algorithm has also been tested on the industrial Raman spectra of ortho-xylene and proved to be effective. (C) 2016 Optical Society of America
机译：由光谱仪测量的拉曼光谱通常遭受频带重叠和随机噪声的困扰。提出了一种基于Voigt线轮廓模型的拉曼光谱自动分解算法。为了分解测得的拉曼光谱，引入了Voigt线轮廓模型以对测得的光谱进行参数化，并使用高斯函数作为仪器加宽函数。因此，将频谱分解问题转换为Voigt线轮廓模型参数的多参数优化问题。该算法可以消除仪器加宽，获得恢复的拉曼光谱，分辨重叠带并同时抑制随机噪声。而且，恢复的光谱可以分解为一组洛伦兹函数。在模拟拉曼光谱上的实验结果表明，该算法的性能比常用的盲反卷积方法要好得多。该算法还在邻二甲苯的工业拉曼光谱上进行了测试，并证明是有效的。 （C）2016美国眼镜学会

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