|
|
|
|
|
|
| Improved Num-Local Piecewise Polynomial Fitting Algorithm for
Accurate Correction of Raman Spectroscopy Baselines |
| TIAN Chao-fan, LI Jian-jun*, WENG Guo-jun, ZHU Jian, ZHAO Jun-wu* |
School of Life Science and Technology, Xi'an Jiaotong University, Key Laboratory of Biomedical Information Engineering of Ministry of Education, Xi'an 710049, China
|
|
|
|
|
Abstract Baseline correction, one of the extremely critical steps in Raman spectroscopy pre-processing, is of great significance for further Raman spectroscopy data analysis, Raman imaging, etc. Currently, the most common baseline correction algorithm is based on polynomial fitting; due to its manual or semi-manual form, manual experience, a high level of user expertise, and a tedious processing process are required, leading to large differences in processing results. At the same time, the polynomial order and the moving segmentation window are difficult to select in the process, so the processed results are often under-fitted or over-fitted. This paper improves the Numlocal Piecewise Polynomial Fitting (NPPF) algorithm for accurately calibrating Raman spectral baselines. Firstly, an improved segmentation-based local optimum algorithm is used to select the approximate lateral width of the bottom contour of the widest peak in the spectrum as the background point window width; the minimum and second minimum values within the window, in turn, are selected as the background baseline points to be fitted, avoiding the difficulty of selecting background points, and achieving more accurate selection of each background contour baseline point. Then, the three fitted curve functions are obtained by iterative coverage of each window three times, and each point in the selected window corresponds to three curve function values, which are calculated with the previous fitted absolute value separately. The curve function value with the minimum absolute value is taken as the fitted curve value at this point. Thismethod better avoids the underfitting and overfitting phenomenon of the Piecewise Polynomial Fitting(PPF) algorithm and also determines the order and segmentation window in the fitting process. In this paper, two Raman spectra with different background types are simulated, and the NPPF and PPF algorithms are compared to process the two simulated spectra separately. The Root Mean Square Error (RMSE) of NPPF processing results is found to be smaller, which confirms the superiority of NPPF over PPF. Finally, the Raman spectra of the actual samples (alizarin and rhodamine 6G) are processed by comparing NPPF and PPF, and it is found that the fitted baseline of NPPF is more accurate, which confirms that the NPPF algorithm in this paper has wide practical application value and prospect in the baseline correction pretreatment of Raman spectra.
|
|
Received: 2022-10-02
Accepted: 2022-11-25
|
|
|
|
Corresponding Authors:
LI Jian-jun, ZHAO Jun-wu
E-mail: jjunli@mail.xjtu.edu.cn; nanoptzhao@163.com
|
|
[1] Orlando A, Franceschini F, Muscas C, et al. Chemosensors, 2021, 9(9): 262.
[2] Scotter C N G. Trends in Food Science & Technology, 1997, 8(9): 285.
[3] Liland K H, Kohler A, Afseth N K. Journal of Raman Spectroscopy, 2016, 47(6): 643.
[4] Emry J R, Marshall A O, Marshall C P. Geostandards and Geoanalytical Research, 2016, 40(1): 29.
[5] Afseth N K, Segtnan V H, Wold J P. Applied Spectroscopy, 2006, 60(12): 1358.
[6] Zhang Zhimin, Chen Shan, Liang Yizeng. Analyst, 2010, 135(5): 1138.
[7] Lieber C A, Mahadevan-Jansen A. Applied Spectroscopy, 2003, 57(11): 1363.
[8] Schulze G, Jirasek A, Yu M M L, et al. Applied Spectroscopy, 2005, 59(5): 545.
[9] Chen Hao, Xu Weiliang, Broderick N G R. Applied Spectroscopy, 2019, 73(3): 284.
[10] Wang Xin, Fan Xianguang, Xu Yingjie, et al. Measurement Science and Technology, 2015, 26 (11): 115503.
[11] LIU Long, FAN Xian-guang, KANG Zhe-ming, et al(刘 龙，范贤光，康哲铭，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(1): 111.
[12] Liland K H, Almoy T, Mevik B H. Applied Spectroscopy, 2010, 64(9): 1007.
[13] Kandjani A E, Griffin M J, Ramanathan R, et al. Journal of Raman Spectroscopy, 2013, 44(4): 608.
[14] WANG Tuo, DAI Lian-kui. Applied Spectroscopy, 2017, 71(6): 1169.
[15] HU Hai-bin, BAI Jing, GUO Xia, et al. Photonic Sensors, 2018, 8(4): 332.
|
| [1] |
MA Jun-jie, LI Yan, WU Fu-rong, HE Kang, WANG Feng-ping*. Spectroscopic Study on the Pigments of the Architectural Colored
Paintings of the Altar of Agriculture in Beijing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 983-990. |
| [2] |
LIU Zong-yi1, 2, ZHANG Cai-hong1, 2, JIANG Jian-kang1, 2, SHEN Bin-guo3, DING Yan-fei1, 2, ZHANG Lei-lei1, 2*, ZHU Cheng1, 2*. Rapid Determination of Total Flavonoids in Dendrobium Officinale Based on Raman Spectroscopy and CNN-LSTM Deep Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 1018-1024. |
| [3] |
ZHOU Ming-hao1, CHEN Xiao-gang1*, CUI Ji-feng1, BIAN Kai2, HU Feng2. Study on Recognition Method of Mine Water Source Based on Raman Spectrum Combined With WOA Characteristic Screening[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(04): 1039-1044. |
| [4] |
GE Xue-feng, SHI Bin, TANG Meng-yuan, JI Kang, ZHANG Yin-ping, GU Min-fen*. Spectral Detection of Desloratadine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 751-755. |
| [5] |
WANG Mao-cheng1, LI Gan1, CHENG Hao2, JIANG Wei1, CHEN Guang1, LI Hai-bo1*. Double-Wavelength NIR Raman Spectroscopy and the Application on
Corrosion Products[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 756-761. |
| [6] |
ZHANG Juan1, LI Ke-xin2, QIN Dong-mei1, BAO De-qing1, 2, WANG Chao-wen2*. Spectroscopic Characteristics and Color Genesis of Yellowish-Green
Montebrasite[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(03): 777-783. |
| [7] |
MENG Xing-zhi, LIU Ya-qiu*, LIU Li-na. Raman Spectroscopy Combined With the WGANGP-ResNet Algorithm to Identify Pathogenic Species[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 542-547. |
| [8] |
WU Fu-rong, LI Yan, MA Jun-jie, WANG Yu-hang, WANG Feng-ping*. The Effect of Different Irradiance on the Degradation Time of Realgar[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(02): 325-330. |
| [9] |
XING Hai-bo1, ZHENG Bo-wen1, LI Xin-yue1, HUANG Bo-tao2, XIANG Xiao2, HU Xiao-jun1*. Colorimetric and SERS Dual-Channel Sensing Detection of Pyrene in
Water[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 95-102. |
| [10] |
WANG Xin-qiang1, 3, CHU Pei-zhu1, 3, XIONG Wei2, 4, YE Song1, 3, GAN Yong-ying1, 3, ZHANG Wen-tao1, 3, LI Shu1, 3, WANG Fang-yuan1, 3*. Study on Monomer Simulation of Cellulose Raman Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 164-168. |
| [11] |
WANG Fang-yuan1, 2, HAN Sen1, 2, YE Song1, 2, YIN Shan1, 2, LI Shu1, 2, WANG Xin-qiang1, 2*. A DFT Method to Study the Structure and Raman Spectra of Lignin
Monomer and Dimer[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 76-81. |
| [12] |
WANG Lan-hua1, 2, CHEN Yi-lin1*, FU Xue-hai1, JIAN Kuo3, YANG Tian-yu1, 2, ZHANG Bo1, 4, HONG Yong1, WANG Wen-feng1. Comparative Study on Maceral Composition and Raman Spectroscopy of Jet From Fushun City, Liaoning Province and Jimsar County, Xinjiang Province[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 292-300. |
| [13] |
LI Jie, ZHOU Qu*, JIA Lu-fen, CUI Xiao-sen. Comparative Study on Detection Methods of Furfural in Transformer Oil Based on IR and Raman Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 125-133. |
| [14] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
| [15] |
WANG Zhi-qiang1, CHENG Yan-xin1, ZHANG Rui-ting1, MA Lin1, GAO Peng1, LIN Ke1, 2*. Rapid Detection and Analysis of Chinese Liquor Quality by Raman
Spectroscopy Combined With Fluorescence Background[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3770-3774. |
|
|
|
|
