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Raman spectroscopy de-noising based on EEMD combined with VS-LMS algorithm

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Abstract

This paper proposes a novel de-noising algorithm based on ensemble empirical mode decomposition (EEMD) and the variable step size least mean square (VS-LMS) adaptive filter. The noise of the high frequency part of spectrum will be removed through EEMD, and then the VS-LMS algorithm is utilized for overall de-noising. The EEMD combined with VS-LMS algorithm can not only preserve the detail and envelope of the effective signal, but also improve the system stability. When the method is used on pure R6G, the signal-to-noise ratio (SNR) of Raman spectrum is lower than 10 dB. The de-noising superiority of the proposed method in Raman spectrum can be verified by three evaluation standards of SNR, root mean square error (RMSE) and the correlation coefficient ρ.

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Authors and Affiliations

  1. School of Information Science and Engineering, Xinjiang University, Urumqi, 830046, China

    Xiao Yu  (俞潇), Jia-qing Mo  (莫家庆) & Xiao-yi Lü  (吕小毅)

  2. School of Electrical Engineering, Tianjin University of Technology, Tianjin, 300384, China

    Liang Xu  (许亮)

  3. Institute of Health and Environmental Medicine, Academy of Military Medical Science, Tianjin, 300050, China

    Xiao-yi Lü  (吕小毅)

Authors
  1. Xiao Yu  (俞潇)
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  2. Liang Xu  (许亮)
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  3. Jia-qing Mo  (莫家庆)
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  4. Xiao-yi Lü  (吕小毅)
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Corresponding author

Correspondence to Xiao-yi Lü  (吕小毅).

Additional information

This work has been supported by the National Natural Science Foundation of China (No.61308120), the Doctor Startup Project of Xinjiang University (No.BS120122), the Young Talents Project in Xinjiang Uygur Autonomous Region (No.2013731003), and the Xinjiang Science and Technology Project (Nos.201412107 and 2014211B003).

These authors contributed equally to this work and should be considered co-first authors.

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Yu, X., Xu, L., Mo, Jq. et al. Raman spectroscopy de-noising based on EEMD combined with VS-LMS algorithm. Optoelectron. Lett. 12, 16–19 (2016). https://doi.org/10.1007/s11801-016-5200-9

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  • DOI: https://doi.org/10.1007/s11801-016-5200-9

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