magazinelogo

Journal of Applied Mathematics and Computation

ISSN Print: 2576-0645 Downloads: 153774 Total View: 1839432
Frequency: quarterly ISSN Online: 2576-0653 CODEN: JAMCEZ
Email: jamc@hillpublisher.com
Article Open Access http://dx.doi.org/10.26855/jamc.2024.12.010

Baseline Correction of Fourier Transform Infrared Spectroscopy Signals Based on Cubic Splines

Miaomiao Zhu1, Bo Yu1,*, Zhiwen Yao2

1College of Science, China Three Gorges University, Yichang 443002, Hubei, China.

2China Shipbuilding Industry Group Co. Ltd Alphapec Instrument (Hubei), Yichang 443005, Hubei, China.

*Corresponding author: Bo Yu

Published: January 21,2025

Abstract

In order to solve the problem of baseline drift caused by the influence of environment, temperature, noise, and other factors in Fourier infrared spectrum data, we propose a baseline correction method based on the cubic spline, which is compared with other existing baseline correction techniques. Since baseline correction is a pre-processing step for subsequent substance composition identification, we can compare the baseline correction effect with the accuracy of substance composition identification. In order to exclude the influence of other factors on the accuracy of substance component identification, we will use the same method in subsequent noise reduction treatment and substance composition identification to effectively compare various baseline correction techniques. Furthermore, we compare different kinds of baseline correction method from two other aspects of the spectrum and root mean square error. The numerical results show that the proposed method has strong comparability with the existing methods in the spectral map and root mean square error, keeping the best performance in substance composition identification.

References

[1] Peng JT, Peng SL, Xie Q, et al. Baseline correction combined partial least squares algorithm and its application in on-line Fourier transform infrared quantitative analysis. Analytica Chimica Acta. 2011;690(2):162-168.

[2] Chen NP. Review on Identification of Wine by Fourier Transform Infrared Spectroscopy. Modern Chemical Research. 2019;(06):22-24.

[3] Chen YY, Zou CN, Mastalerz M, et al. Applications of Micro-Fourier Transform Infrared Spectroscopy (FTIR) in the Geological Sciences—A Review. International Journal of Molecular Sciences. 2015;16(12):30223-30250.

[4] Jiang A. Research on the Qualitative Analysis Method of Complex Mixtures Based on Infrared Spectroscopy. Shanghai: Chinese Academy of Sciences. 2012.

[5] Wang L, Li DM, Qian HJ, et al. Baseline drift and calibration methods in NIR analysis. Analysis Laboratory. 2016;35(10):1203-1208.

[6] Hu YG, Zhao ZY, Wang G. Baseline correction and background elimination using wavelet transforms. Journal of Huazhong University of Science and Technology (Nature Science Edition). 2011;39(06):36-40.

[7] Ma H, Wang ZB, Zhang JL. et al. Infrared Spectrum Baseline Correction Method Based on Improved Iterative Polynomial Fitting. Laser Technology. 2013;37(02):223-226.

[8] Jiang XY, Li FS, Wang QY. et al. Comparison of the Spectral Baseline Correction Based on the Penalized least squares algorithm. Transactions of the Chinese Society for Agricultural Machinery. 2021;52(08):205-212.

[9] Zhang ZM, Chen S, Liang YZ. et al. Baseline correction using adaptive iteratively reweighted penalized least squares. Analyst. 2010;135(5):1138-1146.

[10] Peng J, Peng S, Xie Q, et al. Baseline correction combined partial least squares algorithm and its application in on-line Fourier transform infrared quantitative analysis. Analytica Chimica Acta. 2011;690(2):162-168.

[11] Baek SJ, Park A, Ahn YJ. et al. Baseline correction using asymmetrically reweighted penalized least squares smootning. Analyst. 2015;140(1):250-257.

[12] Zhang F, Tang X, Tong A, et al. Baseline correction for infrared spectra using adaptive smoothness parameter penalized least squares method. Spectroscopy Letters. 2020;53(3):222-233.

[13] Zhao FK, Xu XM, Lv LY. et al. A Background Removing Method for Spectrum Signal Based on Iterative Wavelet Transform. Journal of Instrumental Analysis. 2019;38(10):1275-1279.

[14] Ning ZQ, Liu JX, Wu Y, et al. Infrared Spectrum BASELINE Correction Method Based on Improved Iterative Polynomial Fitting. Laser & Optoelectronics. 2020;57(03):255-261.

[15] Gan F, Ruan G, Mo J. Baseline correction by improved iterative polynomial fitting with automatic threshold. Chemometrics and Intelligent Laboratory Systems. 2006;82(1):59-65.

[16] Ma Z, Ma E, Xiong FB. et al. Dynamic Moving Savitzky-Golay Smoothing Algorithm for the Raman Spectrum Automatic Baseline Correction. Applied Laser. 2017;37(04):614-618.

[17] Zhao H, Chen YX, Xu XD, et al. Baseline Correction for Raman Spectra Based on Locally Symmetric Reweighted Penalized Least Squares. Chinese Journal of Lasers. 2018;45(12):280-291.

[18] Guo Y, Wang W. Baseline correction for Raman spectra using a spectral estimation-based asymmetrically reweighted penalized least squares method. Applied Optics. 2023;62(18):4766-4776.

[19] Nie L, Tao M, Zhai ZS, et al. Undersampled modified algorithm for cylindrical near-field measurement based on cubic spline interpolation. Journal of Hubei University of Technology. 2017;32(01):63-67.

[20] Chat T, Draxler RR. Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific Model Development. 2014;7(3):1247-1250.

How to cite this paper

Baseline Correction of Fourier Transform Infrared Spectroscopy Signals Based on Cubic Splines

How to cite this paper: Miaomiao Zhu, Bo Yu, Zhiwen Yao. (2024) Baseline Correction of Fourier Transform Infrared Spectroscopy Signals Based on Cubic SplinesJournal of Applied Mathematics and Computation8(4), 342-350.

DOI: http://dx.doi.org/10.26855/jamc.2024.12.010