Chen Yunliang, Dai Liankui
Appl Opt. 2016 May 20;55(15):4085-94. doi: 10.1364/AO.55.004085.
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.
用光谱仪测量的拉曼光谱通常存在谱带重叠和随机噪声问题。本文提出一种基于Voigt线型模型的拉曼光谱自动分解算法来解决这一问题。为了分解测量得到的拉曼光谱，引入Voigt线型模型对测量光谱进行参数化，并使用高斯函数作为仪器展宽函数。因此，光谱分解问题被转化为Voigt线型模型参数的多参数优化问题。该算法能够消除仪器展宽，获得恢复后的拉曼光谱，分辨重叠谱带，并同时抑制随机噪声。此外，恢复后的光谱可以分解为一组洛伦兹函数。对模拟拉曼光谱的实验结果表明，该算法的性能远优于常用的盲反卷积方法。该算法也已在邻二甲苯的工业拉曼光谱上进行了测试，证明是有效的。
