拉曼光谱是一种无损快速检测技术, 可以提供材料的定性和定量信息, 因而在医药、 化工等诸多领域得到了广泛的应用。 但是, 由于样品荧光背景噪声的影响, 造成拉曼光谱信号出现基线漂移现象, 这给拉曼光谱的特征峰识别和拉曼成像带来十分严重的影响。 目前, 改进实验方法和数值处理是解决该问题的两种重要手段。 改进实验方法上, 有偏振调制法和高频调制法等, 但存在实验设备复杂, 检测技术难度大等缺点; 数值处理上, 有多项式拟合和小波变换等, 但容易出现欠拟合和过拟合等现象。 本文在不改换高精密设备的前提下, 针对传统基线校正的方法进行了改进, 提出一种基于自适应加窗spline曲线拟合的拉曼光谱去基线方法。 首先, 基于谱峰识别算法和初始搜索步长求得谷值的最优搜索间距, 并利用谱谷识别算法完成谷值曲线的拟合; 其次, 利用最优搜索间距和谱峰识别算法, 求得谷值曲线峰值位置, 并在该位置处对称添加自适应矩形窗函数去除峰值, 重新划分整个区间, 拟合谷值曲线; 再次, 逐点比较拟合曲线与原拉曼光谱信号, 取较小值, 拟合曲线; 最后, 重复加窗去除峰值操作, 直至自适应窗函数宽度低于阈值, 完成拉曼光谱信号的基线拟合。 在实验中, 选用乙酸丁酯、 聚甲基丙烯酸甲酯(polymethyl methacrylate, PMMA)作为实验样品, 利用该方法对其拉曼光谱信号进行了基线校正, 观察并比较该方法和传统方法的校正结果。 实验结果表明, 该方法能够有效地消除拉曼光谱信号的基线漂移, 较好的保留一些较弱的拉曼特征峰, 且不易出现欠拟合和过拟合的现象, 获得了良好的基线校正效果, 为进一步分析光谱数据和实现拉曼成像提供准确可靠的信息。

Raman spectroscopy is a non-destructive and rapid detection technology that can provide qualitative and quantitative information of the material. Therefore, it has been widely used in many fields such as medicine and chemical industry. However, the Raman spectrum suffers from the baseline drift due to the background fluorescence of the sample. Moreover, it has a serious impact on the identification of characteristic peaks of Raman spectra and the Raman imaging. At present, there are two methods to solve this problem, that is, improve the experimental methods and numerical processing. The improve the experimental methods include polarization modulation method and high frequency modulation method. However, they suffer from the disadvantages of complicated experimental equipment and difficult detection technology. The numerical processing includes polynomial fitting and wavelet transform. However, it is prone to suffer from the over and under-fitting. In order to solve this problem, we propose the baseline correction algorithm for Raman spectroscopy based on adaptive window spline fitting, which based on the existing equipment and the traditional baseline correction algorithm. Firstly, the optimal search interval of the trough value is obtained based on the peak recognition algorithm and the initial search step, and then the trough recognition algorithm is used to complete the fitting of the trough curve. Secondly, the peak position of the trough curve is obtained based on the optimal search interval and the peak recognition algorithm. Then, the adaptive rectangular window is symmetrically added at this position, in order to delete the peak, and fitting the trough curve. Thirdly, the fitting trough curve is compared with the original Raman spectrum, point by point, and taking the smaller value to fit a new trough curve. Finally, the operation above will continue until the width of the adaptive window is lower than the threshold. Afterwards, the baseline fitting of the Raman spectrum is completed. And then the baseline correction of the sample is obtained based on our algorithm and the traditional methods. It can be seen that our algorithm can effectively eliminate the baseline drift, and some weaker Raman characteristic peaks can be better remaining. Simultaneously, the over and under-fitting is avoided, and the result of baseline correction is good. Therefore, it provides reliable information on the further analysis of the Raman spectrum and the realization of the Raman imaging.