A real-time hyper-accuracy integrative approach to peak identification using lifting-based wavelet and Gaussian model for field mobile mass spectrometer

Li, Cuiping; Han, Jiuqiang; Huang, Qibin; Mu, Ning; Li, Baoqiang; Cao, Bingqing

doi:10.1016/j.chemolab.2013.06.010

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…where f (x) is the original signal, ∼ f (x) is the denoised signal and s is the number of variable points [44]. A larger SNR means less noise mixed into the signal, and a smaller SNR means more noise mixed into the signal.…”

Section: Evaluation Of Denoising Performancementioning

confidence: 99%

Variational Mode Decomposition for Raman Spectral Denoising

Bian,

Shi,

Shao

et al. 2023

Molecules

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As a fast and non-destructive spectroscopic analysis technique, Raman spectroscopy has been widely applied in chemistry. However, noise is usually unavoidable in Raman spectra. Hence, denoising is an important step before Raman spectral analysis. A novel spectral denoising method based on variational mode decomposition (VMD) was introduced to solve the above problem. The spectrum is decomposed into a series of modes (uk) by VMD. Then, the high-frequency noise modes are removed and the remaining modes are reconstructed to obtain the denoised spectrum. The proposed method was verified by two artificial noised signals and two Raman spectra of inorganic materials, i.e., MnCo ISAs/CN and Fe-NCNT. For comparison, empirical mode decomposition (EMD), Savitzky–Golay (SG) smoothing, and discrete wavelet transformation (DWT) are also investigated. At the same time, signal-to-noise ratio (SNR) was introduced as evaluation indicators to verify the performance of the proposed method. The results show that compared with EMD, VMD can significantly improve mode mixing and the endpoint effect. Moreover, the Raman spectrum by VMD denoising is more excellent than that of EMD, SG smoothing and DWT in terms of visualization and SNR. For the small sharp peaks, some information is lost after denoising by EMD, SG smoothing, DWT and VMD while VMD loses fewest information. Therefore, VMD may be an alternative method for Raman spectral denoising.

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Section: Evaluation Of Denoising Performancementioning

confidence: 99%

Variational Mode Decomposition for Raman Spectral Denoising

Bian,

Shi,

Shao

et al. 2023

Molecules

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“…After filtering the spectral signal curve, arithmetic operations are used to obtain the signal slope curve for spectral peak identification [21,22] . It is assumed that in an ideal condition the signal slope curve would be a totally smooth curve.…”

Section: Peak Identificationmentioning

confidence: 99%

Development of an Automated LIBS Analytical Test System Integrated with Component Control and Spectrum Analysis Capabilities

Ding

Tian

Chen

et al. 2015

Plasma Sci. Technol.

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The present paper proposes an automated Laser-Induced Breakdown Spectroscopy (LIBS) analytical test system, which consists of a LIBS measurement and control platform based on a modular design concept, and a LIBS qualitative spectrum analysis software and is developed in C#. The platform provides flexible interfacing and automated control; it is compatible with different manufacturer component models and is constructed in modularized form for easy expandability. During peak identification, a more robust peak identification method with improved stability in peak identification has been achieved by applying additional smoothing on the slope obtained by calculation before peak identification. For the purpose of element identification, an improved main lines analysis method, which detects all elements on the spectral peak to avoid omission of certain elements without strong spectral lines, is applied to element identification in the tested LIBS samples. This method also increases the identification speed. In this paper, actual applications have been carried out. According to tests, the analytical test system is compatible with components of various models made by different manufacturers. It can automatically control components to get experimental data and conduct filtering, peak identification and qualitative analysis, etc. on spectral data.

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“…Several algorithms for peak searching and setting of the peak limits have been described. 19,20,[23][24][25][26] Another frequently used strategy is the unsupervised division of the range of the scanned variable into a number of segments, each one to be recognized as a variable. The segments are usually termed moving windows (MWs), since they are thought as a range of points that move along the scan variable by incorporating new points at the window front and dropping off old ones at the rear end.…”

Section: Introductionmentioning

confidence: 99%