A UV-visible absorption spectrum denoising method based on EEMD and an improved universal threshold filter. Issue 16 (23rd February 2018)

• Record Type: Journal Article
• Authors: Li, Jingwei; Tong, Yifei; Guan, Li; Wu, Shaofeng; Li, Dongbo
• Is Part Of: RSC advances. Volume 8:Issue 16(2018)
• Page Start: 8558
• Page End: 8568
• Publication Date: 2018-02-23
• Subjects: Chemistry -- Periodicals; 540.5
• DOI: 10.1039/c7ra13202f
• Languages: English
• ISSNs: 2046-2069

Abstract

Abstract : When using ultraviolet-visible spectroscopy (UV-visible spectroscopy) to detect water quality parameters, the measured absorption spectrum signal often contains a lot of interference information. Abstract : When using ultraviolet-visible spectroscopy (UV-visible spectroscopy) to detect water quality parameters, the measured absorption spectrum signal often contains a lot of interference information. Therefore, denoising is extremely important in spectrum data processing and analysis, which directly affects the subsequent quantitative analysis and information mining. Choosing an appropriate denoising method is key to improve the spectral analysis accuracy and promote the spectral analysis ability. In this paper, a new UV-visible absorption spectrum denoising method is proposed: a denoising method based on ensemble empirical mode decomposition (EEMD) and improved universal threshold filtering (EEMD-based method). The noisy UV-visible absorption spectrum signal is firstly decomposed into a finite set of band limited signals called intrinsic mode functions (IMFs) via EEMD. Spearman's rank correlation coefficient (Spearman's rho) is then used as a criterion for the IMFs dominated by noise or useful signals, and the improved universal threshold filtering method is applied to the noise dominant IMFs to eliminate the noise. Finally, the denoised UV-visible absorption spectrum signal is reconstructed. In order to discuss the effectiveness of the EEMD-based denoising method proposed in this paper, we compare it with various wavelet-based threshold denoising methods. Both methods have been implemented on synthetic signals with diverse waveforms ('Blocks', 'Bumps' and 'Heavy sine'). It is demonstrated that the proposed method outperforms the wavelet-based methods. Then, the measured UV-visible absorption spectra with different SNR were denoised by the wavelet and proposed methods. The method proposed also performs well in the spectrum denoising experiment.