光谱学与光谱分析, 2017, 37 (8): 2632, 网络出版: 2017-08-30   

基于自动线性拟合的快速拉曼基线校正算法

A Fast Raman Baseline Correction Algorithm Based on Automatic Linear Fitting
作者单位
西安交通大学生命科学与技术学院, 陕西 西安 710049
摘要
近年来拉曼光谱以其无创、 灵敏度高等众多优点在化学表征、 生物医药、 材料等领域引起广泛关注, 而基线漂移的存在为后续的定性定量分析带来严重困扰, 因此设计高性能的基线校准算法以提高分析结果的有效性及准确性具有重要意义。 针对传统算法在批量拉曼光谱数据基线校正方面的不足, 基于自动线性拟合算法提出一种快速基线校正算法以校正具有相似背景的批量拉曼光谱数据并详细阐述了该算法的核心思想以及算法实现流程。 该算法首先从批量拉曼光谱数据中自动选择一条拉曼光谱数据作为基准光谱, 使用自动线性拟合算法对其进行基线校准得到其基线以及分段标记点, 然后利用标记点快速计算出组内其他与基准光谱具有较高相关性的拉曼光谱数据的基线, 对于组内与基准光谱相关性不满足阈值要求的拉曼光谱则使用自动线性拟合算法对其进行单独基线校正, 这使得算法具有具有较强的鲁棒性, 可以适应复杂的拉曼光谱基线校正情形。 分别使用快速基线校正算法与单独基线校正算法对多组实际拉曼光谱数据进行基线校正以对比分析算法基线校正效果, 结果表明该算法可以实现对批量拉曼光谱数据的快速校正, 基线校正效果良好, 并且相较于单独进行基线校正算法耗时减少了30%以上, 算法无参, 简单易行, 无需额外人工干预, 是一种切实可行的批量拉曼数据自动基线校正算法。
Abstract
Raman spectroscopy occupies an important position in modern spectroscopy technique due to its numerous advantages such as non-invasive, high-sensitivity, etc. Meanwhile baseline correction is one of the key technologies of Raman qualitative and quantitative analysis, thus it is meaningful to develop high performance algorithms for baseline correction for the purpose to improve the effectiveness and accuracy of analytical results. Because of the defect of traditional algorithms for the problem to correct baselines of a group of Raman spectra which have a similar background, this paper proposed a fast Raman baseline correction algorithm (FRBCA) based on automatic linear fitting for this problem and demonstrated its fundamental ideas and the implementation process of this algorithm. In the FRBCA algorithm, firstly one of the spectra was selected automatically as a reference spectrum and estimated its baseline and the mask points by the automatic linear fitting algorithm, then the baselines of other spectra which have a high relativity to the selected spectrum were estimated quickly based on the mask points of reference spectrum. Separate treatment was called for those spectra which does not satisfy the condition. This innovation makes the algorithm has strong robustness and can be suitable for the complex Raman spectrum baseline correction scenario. In addition, some actual Raman spectral data were used to test performance of the algorithm and make a comparison between the proposed algorithm and the traditional algorithm. The results show that fast Raman baseline correction algorithm proposed in this article allows a fast Roman baseline correction for a number of Raman spectral data. It reduces the consuming time more than 30% while has a similar performance at the correction result no worse than the algorithm correcting the Raman spectroscopy individually. The method presented in this article is conceptually simple, easy to implement, fully automated and doesn’t need additional parameters, making it suitable for the fully automated baseline correction of large numbers of spectra which have a similar background.

张万里, 朱键, 李剑君, 赵军武. 基于自动线性拟合的快速拉曼基线校正算法[J]. 光谱学与光谱分析, 2017, 37(8): 2632. ZHANG Wan-li, ZHU Jian, LI Jian-jun, ZHAO Jun-wu. A Fast Raman Baseline Correction Algorithm Based on Automatic Linear Fitting[J]. Spectroscopy and Spectral Analysis, 2017, 37(8): 2632.

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