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| Chromatographic Signal Baseline Correction Based on Piecewise Cubic Curve and Data Updating |
| CHEN Jinlin1,2,WU Yiquan1,YUAN Yubin1 |
1. College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing,Jiangsu 211106, China
2. College of Finance and Mathematics, Huainan Normal University, Huainan, Anhui 232038, China |
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Abstract In the process of using polynomial curves to piecewise fitting the baseline of measurement signals, there are frequent defects where the piecewise points affect the fitting accuracy and operational speed. A new baseline correction algorithm for chromatographic signals based on piecewise cubic curves and data updating was proposedto solve the problems.Firstly, a piecewise cubic function is constructed based on five consecutive points to fit the measured signal.This piecewise cubic function has the advantage of low order smoothness and can effectively overcome the defect of piecewisepoints affecting fitting accuracy.Then, in order to reduce the number of iterations of the baseline correction algorithm and increase computational speed, an improved data updating method is proposed.In each iteration process, the peak values of the original measured signals above the baseline are reversed in a certain proportion to update the baseline, while others remain unchanged.The calculation time ratio of the algorithm before and after improvement is approximately 71. Finally, the new algorithm proposed is compared with five existing related algorithms. The experimentalresults show that the baseline correction algorithm based on segmented cubic curves and data updating has high approximation to the baseline and is fast.The new baseline correction algorithm in this paper has been applied in practical applications in liquid chromatography. It proves that this algorithm can effectively remove the baseline from the source chromatographic signals.
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Received: 15 August 2023
Published: 04 July 2024
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|
|
|
| ［21］ |
HAO R L, DAI J S, PAN T H, et al. Sparse Bayesian learning approach for baseline correction［J］. Chemometrics and Intelligent Laboratory Systems, 2020, 204:104088.
|
| ［15］ |
王昕, 康哲铭, 刘龙, 等. 基于中值滤波和非均匀B样条的拉曼光谱基线校正算法［J］.物理学报, 2020, 69(20):201-208.
|
| ［2］ |
叶国阳, 徐科军. 气相色谱仪数据处理方法研究和软件研制［J］. 计量学报, 2017,38(5):532-537.
|
| ［7］ |
高鹏飞, 杨蕊. 拉曼光谱的局域动态移动平均全自动基线校准算法［J］.光谱学与光谱分析,2015,35(5):1281-1285.
|
| ［9］ |
尤晓艳, 武岩波, 朱敏. 基于虚拟移动长基线的载人潜水器声学定位算法［J］.仪器仪表学报, 2019, 40(11):146-152.
|
| ［16］ |
姚泽楷, 蔡耀仪, 李诗文, 等. 基于平滑样条曲线结合离散状态转移算法的拉曼光谱基线校正方法［J］. 中国激光, 2022, 49(18): 160-168.
|
|
LU Z, ZHUANG W, WU Y, et al. Research on spectral baseline correction of FBG sensor network based on the improved EMD method［J］. Chinese Journal of Scientific Instrument,2022, 43 (1):190-197.
|
|
YEI G Y, XU K J. Development of method and software for data processing in gas chromatograph［J］. Acta Metrologica Sinica, 2017, 38(5):532-537.
|
| ［4］ |
HAN G, LIN B, XU Z. Electrocardiogram signal denoising based on empirical mode decomposition technique: an overview［J］. Journal of Instrumentation,2017,12(3): 03010.
|
| ［5］ |
赵奉奎, 徐晓美, 吕立亚. 基于迭代小波变换的光谱信号本底扣除方法研究［J］. 分析测试学报, 2019, 38(10): 1275-1279.
|
|
ZHAO F K, XU X M, L L Y. A background removing method for spectrum signal based on iterative wavelet transform［J］. Journal of Instrumental Analysis,2019,38(10): 1275-1279.
|
| ［6］ |
XIE X, LIU H, SHU M, et al. A multi-stage denoising framework for ambulatory ECG signal based on domain knowledge and motion artifact detection［J］. FutureGeneration Computer Systems,2021,116(3):103-116.
|
| ［8］ |
LIU X, ZAHNG Z, LAIGN Y, et al. Baseline correction of high resolution spectral profile data based on exponential smoothing［J］. Chemometrics and Intelligent Laboratory Systems, 2014, 139:97-108.
|
|
YOU X Y, WU Y B, ZHU M. Acoustic localization algorithm of human occupied vehicle based on virtual moving long baseline［J］. Chinese Journal of Scientific Instrument,2019, 40 (11): 146-152.
|
| ［1］ |
吕峥, 庄炜, 吴越, 等. 基于改进EMD方法的FBG传感网络光谱基线校正研究［J］. 仪器仪表学报, 2022, 43 (1):190-197.
|
| ［3］ |
XU X, HUO X, QIAN X, et al. Data-driven and coarse-to-fine baseline correction for signals of analytical instruments［J］. Analytica Chimica Acta, 2021, 1157(3): 338386.
|
|
GAO P F, YANG R. Local dynamic moving average fullyautomatic baseline calibration algorithm for Raman spectroscopy［J］. Spectroscopy and Spectral Analysis, 2015, 35 (5): 1281-1285.
|
| ［10］ |
朱锋, 安军社, 施海亮, 等. 基于低秩约束惩罚最小二乘的干涉图基线校正方法［J］.光学学报, 2022, 42(14): 216-222.
|
| ［11］ |
WANG X, CHEN X. Baseline correction based on a search algorithm from artificial intelligence［J］. Applied Spectroscopy: Society for Applied Spectroscopy, 2021, 75(5):531-544.
|
|
LIAN X Q, CHEN Y M, WANG Y Q, et al. Research on ICP-AES spectral baseline correction method based on DE algorithm and NURBS curve［J］. Spectroscopy and Spectral Analysis,2023,43(1):260-267.
|
| ［14］ |
HU H B, BAI J, XIA G. Improved baseline correction method based on polynomial fitting for Raman spectroscopy［J］. Photonic Sensors, 2018, 8(4): 332-340.
|
|
YAO Z K, CAI Y Y, LI S W, et al. Baseline correction for Raman spectroscopy using cubic splinesmoothing combined with discrete state transformation algorithm［J］.Chinese Journal of Lasers,2022, 49(18): 160-168.
|
|
LIU L, FAN X G, KANG Z M, et al. Baseline correction algorithm for Raman spectroscopy based on adaptive window spline fitting［J］. Spectroscopy and Spectral Analysis, 2021,41(1):111-115.
|
| ［19］ |
FENG X, ZHU Z, SHEN M, et al. The method of baseline drift correction of Raman spectrum based on polynomial fitting［J］. Computers and Applied Chemistry, 2009,26(6):756-762.
|
|
ZHU F, AN J S, SHI H L, et al. Interferogram baseline correction method based on low-rank constraint and penalized least squares［J］. Acta Optica Sinica, 2022, 42(14): 216-222.
|
| ［12］ |
QIAN F, WU Y, HAO P. A fully automated algorithm of baseline correction based on wavelet feature points andsegment interpolation［J］. Optics Laser Technology, 2017, 96:202-207.
|
| ［13］ |
廉小亲, 陈彦铭, 王宇乔, 等. 基于DE算法和NURBS曲线的ICP-AES光谱基线校正方法研究［J］.光谱学与光谱分析, 2023, 43(1):260-267.
|
|
WANG X, KANG Z M, LIU L, et al. Baseline correction algorithm for Raman spectra based on median filtering and uniform B-spline［J］. Acta Phys. Sin, 2020, 69(20):201-208.
|
| ［17］ |
刘龙, 范贤光, 康哲铭, 等. 基于自适应加窗spline曲线拟合的拉曼光谱去基线方法［J］.光谱学与光谱分析,2021, 41(1):111-115.
|
| ［18］ |
覃赵军, 陶站华, 刘军贤, 等. 分段式线性拟合校正拉曼光谱基线漂移［J］.光谱学与光谱分析,2013,33(2): 383-386.
|
|
TAN Z J, TAO Z H, LIU J X, et al. Correction of Raman spectral baseline drift by piecewise linear fitting［J］. Spectroscopy and Spectral Analysis, 2013,33(2): 383-386.
|
| ［20］ |
MAXIME Y, FRANCK F, MASSIOT D, et al. Iterative baseline correction algorithm for dead time truncated one-dimensional solid-state MAS NMR spectra［J］. Solid State Nuclear Magnetic Resonance, 2020, 110:101699.
|
|
|
|
2024, Vol. 45 
