Raman spectroscopy is a powerful and non-invasive method for analysis of chemicals and detection of unknown substances. However, Raman signal is so weak that background noise can distort the actual Raman signal. These baseline shifts that exist in the Raman spectrum might deteriorate analytical results. In this paper, a modified version of active contour models in one-dimensional space has been proposed for the baseline correction of Raman spectra. Our technique, inspired by principles of physics and heuristic optimization methods, iteratively deforms an initialized curve toward the desired baseline. The performance of the proposed algorithm was evaluated and compared with similar techniques using simulated Raman spectra. The results showed that the 1D active contour model outperforms many iterative baseline correction methods. The proposed algorithm was successfully applied to experimental Raman spectral data, and the results indicate that the baseline of Raman spectra can be automatically subtracted.

中文翻译：

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用于拉曼光谱基线校正的一维主动轮廓模型

拉曼光谱法是一种用于分析化学物质和检测未知物质的有力且非侵入性的方法。但是，拉曼信号是如此微弱，以至于背景噪声会使实际的拉曼信号失真。拉曼光谱中存在的这些基线变化可能会使分析结果恶化。在本文中，一维空间中主动轮廓模型的修改版本已被提出用于拉曼光谱的基线校正。我们的技术受到物理学原理和启发式优化方法的启发，使初始化曲线朝着所需的基准方向逐渐变形。对所提出算法的性能进行了评估，并与使用模拟拉曼光谱的类似技术进行了比较。结果表明，一维主动轮廓模型的性能优于许多迭代基线校正方法。