分析化学2024，Vol.52Issue(9)：1277-1286,10.DOI:10.19756/j.issn.0253-3820.241234
峰值提取结合变分模态分解的复杂样品光谱去噪方法研究
Spectral Denoising Based on Peak Extraction Combined with Variational Mode Decomposition for Complex Samples
摘要
Abstract
To address the issue of peak loss when applying variational mode decomposition(VMD)to denoise spectra with sharp peaks,in this study,a method for spectral signal denoising of complex samples called peak extraction variational mode decomposition(PE-VMD)was introduced.Firstly,the spectral signal was subjected to a process of denoising utilising the VMD algorithm.Next,the first order derivatives of the spectral signals were calculated to determine the peak center.Subsequently,the second order derivatives of the spectral signal was calculated to extract the sharp peaks with high signal-to-noise ratio(SNR).Finally,the peak intercepted that lose information after VMD denoising were removed,and the remaining spectrum was sequentially connected with the extracted sharp peaks to obtain the final denoised spectrum.The effectiveness of the method was evaluated by one of simulated signals and X-ray diffraction(XRD)spectrum of MnCo-ISAs/CN catalysts.Furthermore,the method was compared with other denoising techniques,including Savitzky-Golay(SG)smoothing,empirical mode decomposition(EMD)and VMD.The efficacy of the denoising process was then assessed by analyzing the spectrograms and signal-to-noise ratio before and after denoising.The results demonstrated that PE-VMD denoising achieved the greatest SNR and effectively preserved the essential information of the spectral signals.Consequently,PE-VMD exhibited superior denoising capability for spectra with sharp peaks.关键词
光谱去噪/变分模态分解/峰值提取/X射线衍射Key words
Spectral denoising/Variational mode decomposition/Peak extraction/X-ray diffraction引用本文复制引用
卢素敏,郝悦,石梓彤,初园园,张妍,卞希慧..峰值提取结合变分模态分解的复杂样品光谱去噪方法研究[J].分析化学,2024,52(9):1277-1286,10.基金项目
药物制剂技术研究与评价国家药品监督管理局重点实验室开放课题项目(Nos.2022TREDP04,2023TREDP01)资助. Supported by the Open Projects Fund of National Medical Products Administration Key Laboratory for Technology Research and Evaluation of Drug Products(Nos.2022TREDP04,2023TREDP01). （Nos.2022TREDP04,2023TREDP01）
