Sparse decomposition enables adaptive and accurate Raman spectral denoising

Biao Sun; Jinglei Zhai; Zilong Wang; Tengyu Wu; Siwei Yang; Yuhao Xie; Yunfeng Li; Pei Liang

doi:10.1016/j.talanta.2023.125120

Sparse decomposition enables adaptive and accurate Raman spectral denoising

Talanta. 2024 Jan 1;266(Pt 2):125120. doi: 10.1016/j.talanta.2023.125120. Epub 2023 Aug 27.

Authors

Biao Sun¹, Jinglei Zhai¹, Zilong Wang², Tengyu Wu¹, Siwei Yang³, Yuhao Xie³, Yunfeng Li⁴, Pei Liang⁵

Affiliations

¹ School of Electrical and Information Engineering, Tianjin University, No. 92, Weijin Road, Nankai District, Tianjin 300072, China.
² College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018 China; College of Information Engineering, China Jiliang University, Hangzhou 310018 China.
³ College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018 China.
⁴ College of Information Engineering, China Jiliang University, Hangzhou 310018 China. Electronic address: liyunfeng@cjlu.edu.cn.
⁵ College of Optical and Electronic Technology, China Jiliang University, Hangzhou, 310018 China; College of Information Engineering, China Jiliang University, Hangzhou 310018 China. Electronic address: plianghust@gmail.com.

PMID: 37657375
DOI: 10.1016/j.talanta.2023.125120

Abstract

Enhancing the quality of spectral denoising plays a vital role in Raman spectroscopy. Nevertheless, the intricate nature of the noise, coupled with the existence of impurity peaks, poses significant challenges to achieving high accuracy while accommodating various Raman spectral types. In this study, an innovative adaptive sparse decomposition denoising (ASDD) method is proposed for denoising Raman spectra. This approach features several innovations. Firstly, a dictionary comprising spectral feature peaks is established from the input spectra by applying a chemometric feature extraction method, which better aligns with the original data compared to traditional dictionaries. Secondly, a dynamic Raman spectral dictionary construction technique is introduced to swiftly adapt to new substances, employing a limited amount of additional Raman spectral data. Thirdly, the orthogonal matching pursuit algorithm is utilized to sparsely decompose the Raman spectra onto the constructed dictionaries, effectively eliminating various random and background noises in the Raman spectra. Empirical results confirm that ASDD enhances the accuracy and robustness of denoising Raman spectra. Significantly, ASDD surpasses existing algorithms in processing Raman spectra of pesticide.

Keywords: Denoising methods of spectra; Orthogonal matching pursuit; Raman spectra; Signal reconstruction; Sparse decomposition.