拉曼光谱基线校正的一维活动轮廓模型
One-dimensional Active Contour Models for Raman Spectrum Baseline Correction
摘要：
拉曼光谱是一种强大的非侵入性方法，用于分析化学品和检测未知物质。然而，拉曼信号是如此微弱，以至于背景噪声可以使实际拉曼信号失真。拉曼光谱中存在的这些基线偏移可能会使分析结果恶化。本文提出了一种改进的一维活动轮廓模型，用于拉曼光谱的基线校正。我们的技术，灵感来自物理原理和启发式优化方法，迭代变形的初始化曲线向所需的基线。该算法的性能进行了评估，并与类似的技术，使用模拟拉曼光谱进行比较。结果表明，一维主动轮廓模型优于许多迭代基线校正方法。将该算法成功应用于拉曼光谱实验数据，结果表明，该算法可以自动扣除拉曼光谱的基线。
英文摘要：
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.
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