Wang Tuo, Dai Liankui
State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China.
Appl Spectrosc. 2017 Jun;71(6):1169-1179. doi: 10.1177/0003702816670915. Epub 2016 Sep 30.
In this paper, a novel background subtraction algorithm is presented that can automatically recover Raman signal. This algorithm is based on an iterative polynomial smoothing method that highly reduces the need for experience and a priori knowledge. First, a polynomial filter is applied to smooth the input spectrum (the input spectrum is just an original spectrum at the first iteration). The output curve of the filter divides the original spectrum into two parts, top and bottom. Second, a proportion is calculated between the lowest point of the signal in the bottom part and the highest point of the signal in the top part. The proportion is a key index that decides whether to go into a new iteration. If a new iteration is needed, the minimum value between the output curve and the original spectrum forms a new curve that goes into the same filter in the first step and continues as another iteration until no more iteration is needed to finally get the background of the original spectrum. Results from the simulation experiments not only show that the iterative polynomial smoothing algorithm achieves good performance, processing time, cost, and accuracy of recovery, but also prove that the algorithm adapts to different background types and a large signal-to-noise ratio range. Furthermore, real measured Raman spectra of organic mixtures and non-organic samples are used to demonstrate the application of the algorithm.
本文提出了一种能够自动恢复拉曼信号的新型背景扣除算法。该算法基于迭代多项式平滑方法，极大地减少了对经验和先验知识的需求。首先，应用多项式滤波器对输入光谱进行平滑处理（在第一次迭代时，输入光谱即为原始光谱）。滤波器的输出曲线将原始光谱分为上下两部分。其次，计算下部信号最低点与上部信号最高点之间的比例。该比例是决定是否进入新迭代的关键指标。如果需要新的迭代，输出曲线与原始光谱之间的最小值形成一条新曲线，该新曲线进入第一步中的同一滤波器，并继续作为另一次迭代，直到不再需要迭代，最终得到原始光谱的背景。模拟实验结果不仅表明迭代多项式平滑算法在处理时间、成本和恢复精度方面具有良好性能，而且证明该算法适用于不同的背景类型和较大的信噪比范围。此外，还使用了有机混合物和无机样品的实际测量拉曼光谱来演示该算法的应用。
