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As one of the key steps for data preprocessing of Raman spectra, baseline correction is an effective method to eliminate fluorescence interference. Traditional algorithms such as polynomial fitting and uniform B-spline fitting are simple and easy to implement, but the uncertain fitting order and internal knots limit their flexibility. In addition, the baseline correction results of traditional algorithms often occur over and under fitting phenomena. Therefore, we propose a baseline correction algorithm for Raman spectra based on median filtering and un-uniform B-spline. Firstly, the trough points of the spectral data are filtered by smoothing preprocess, difference calculation and threshold setting, and the internal knots of the un-uniform B-spline are adaptively selected by the trough positions of the spectral data. Then, the median filtering algorithm is used to process the spectral data so that the un-uniform B-spline has a better baseline fitting effect at the position where the signal changes from peak to smooth band. Finally, the un-uniform B-splines is used to fit the baseline by fitting the baseline iteratively. The proposed algorithm overcomes the shortcoming of traditional B-spline algorithm that the internal knots need to be selected manually based on different Raman spectra, and also avoids influencing the baseline fitting by random noise in the spectral data, and thus further improving the spectral baseline correction effect. The original Raman spectra of polymethyl methacrylate and normal octane are used for experimentally evaluating the baseline correction effect. Compared with the results from polynomial fitting, uniform B-spline and adaptive iteratively reweighted penalized least squares algorithms, the experimental results show that the proposed algorithm can well eliminate the Raman signal baseline drift effectively without over or under fitting phenomena, and it can perform better baseline correction for different baseline drift situations. Therefore, the proposed algorithm can provide more accurate and reliable information for the further analysis of spectral data.
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Keywords:
- baseline correction /
- Raman spectrum /
- un-uniform B-spline /
- median filtering /
- adaptive knots
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图 1 波谷点选择策略
Figure 1. Strategy of selection of trough points.
图 2 内节点选择策略
Figure 2. Strategy of selection of internal knots
图 3 原始拉曼光谱和基于MF-UUB算法拟合的基线 (a) N-OCTANE拉曼光谱; (b) PMMA拉曼光谱
Figure 3. Original Raman spectra and fitted baseline by MF-UUB: (a) N-OCTANE Raman spectrum; (b) PMMA Raman spectrum.
图 4 基于MF-UUB算法基线校正后的光谱 (a) N-OCTANE拉曼光谱; (b)PMMA拉曼光谱
Figure 4. Corrected Raman spectra by MF-UUB: (a) N-OCTANE Raman spectrum; (b) PMMA Raman spectrum.
图 5 原始拉曼光谱和基于非均匀B样条算法拟合的基线 (a) N-OCTANE拉曼光谱; (b)PMMA拉曼光谱
Figure 5. Original Raman spectra and fitted baseline by un-uniform B-spline algorithm: (a) N-OCTANE Raman spectrum; (b) PMMA Raman spectrum.
图 6 原始拉曼光谱和基于均匀B样条算法拟合的基线 (a) N-OCTANE原始拉曼光谱; (b) PMMA原始拉曼光谱
Figure 6. Original Raman spectra and fitted baseline by uniform B-spline algorithm: (a) N-OCTANE Raman spectrum; (b) PMMA Raman spectrum.
图 7 原始拉曼光谱和基于多项式拟合算法拟合的基线 (a) N-OCTANE拉曼光谱; (b)PMMA拉曼光谱
Figure 7. Original Raman spectra and fitted baseline by polynomial fitting algorithm: (a) N-OCTANE Raman spectrum; (b) PMMA Raman spectrum.
图 8 原始拉曼光谱和基于airPLS拟合的基线 (a) N-OCTANE拉曼光谱; (b)PMMA拉曼光谱
Figure 8. Original Raman spectra and fitted baseline by airPLS algorithm: (a) N-OCTANE Raman spectrum; (b) PMMA Raman spectrum.
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[1] Raman C V 1928 Nature 121 619
[2] Morris M D 2006 Anal. Chem. 78 33
Google Scholar
[3] Geiman I, Leona M, Lombardi J R 2009 J. Forensic Sci. 54 947
Google Scholar
[4] Liu H, Zhang Z L, Liu S Y, Yan L X, Liu T T, Zhang T X 2015 Appl. Spectrosc. 69 1013
Google Scholar
[5] Cadusch P J, Hlaing M M, Wade S A, Mcarthur S L 2013 J. Raman Spectrosc. 44 1587
Google Scholar
[6] 张锐, 赵学玒, 胡雅君, 郭媛, 王喆, 赵迎, 李子晓, 汪曣 2014 物理学报 63 070702
Google Scholar
Zhang R, Zhao X H, Hu Y J, Guo Y, Wang Z, Zhao Y, Li Z X, Wang Y 2014 Acta Phys. Sin. 63 070702
Google Scholar
[7] 庞宇, 邓璐, 林金朝, 李章勇, 周前能, 李国权, 黄华伟, 张懿, 吴炜 2014 物理学报 63 098701
Google Scholar
Pang Y, Deng L, Lin J Z, Li Z Y, Zhou Q N, Li G Q, Huang H W, Zhang Y, Wu W 2014 Acta Phys. Sin. 63 098701
Google Scholar
[8] Perez-Pueyo R, Soneira M J 2010 Appl. Spectrosc. 64 595
Google Scholar
[9] Shao L M, Griffiths P 2007 Environ. Sci. Technol. 41 7054
Google Scholar
[10] Zhao J H, Lui H, Mclean D I, Zeng H S 2007 Appl. Spectrosc. 61 1225
Google Scholar
[11] Wang W P, Pottmann H, Liu Y 2006 ACM Graphic. 25 214
Google Scholar
[12] Cai Y Y, Yang C H, Xu D G, Gui W H 2018 Anal. Methods. 10 3525
Google Scholar
[13] Zhang Z M, Chen S, Liang Y Z 2010 J. Raman Spectrosc. 41 659
Google Scholar
[14] 范贤光, 王海涛, 王昕, 许英杰, 王秀芬, 阙靖 2016 光谱学与光谱分析 36 724
Fan X G, Wang H T, Wang X, Xu Y J, Wang X F, Que J 2016 Spectrosc. Spec. Anal. 36 724
[15] Martin T, Cohen E, Kirby R M 2009 Comput. Aided Geom. D. 26 648
Google Scholar
[16] Wang X, Fan X G, Xu Y J 2015 Meas. Sci. Technol. 26 115503
Google Scholar
[17] 王昕, 范贤光, 许英杰, 吴景林, 梁骏, 左勇 2014 光谱学与光谱分析 34 2117
Google Scholar
Wang X, Fan X G, Xu Y J, Wu J L, Lian J, Zuo Y 2014 Spectrosc. Spec. Anal. 34 2117
Google Scholar
[18] 卢德俊, 爨凯旋, 张伟峰 2018 光谱学与光谱分析 38 3708
Lu J D, Cuan K X, Zhang W F 2018 Spectrosc. Spec. Anal. 38 3708
[19] Juhola M, Katajainen J, Raita T 1991 IEEE T. on Signal Proces. 39 204
Google Scholar
[20] Zhang Z M, Chen S, Liang Y Z 2010 Analyst 135 1138
Google Scholar
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