Application of the wavelet transform method in quantitative analysis of Raman spectra

Cai, Wensheng; Wang, Liya; Pan, Zhongxiao; Zuo, Jian‐Min; Xu, Chao; Shao, Xueguang

doi:10.1002/jrs.688

Cited by 36 publications

(19 citation statements)

References 7 publications

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“…2 The need for dealing with large data sets with variable backgrounds was a strong practical motivation given for an iterative wavelet-transform-based background subtraction method developed for high-data-volume applications such as Raman mapping. 1 This technique joined a sophisticated suite of approaches that have been successfully used for signal processing, 14,15 de-noising, [16][17][18][19] and background subtraction. 1,2,18 Fourier transform methods figure prominently amongst these approaches, 2,14,20,21 but wavelet transforms offer the benefit that the wavelet basis functions are localized in space and frequency, providing a more suitable analysis of sharp peaks and non-periodic noise.…”

Section: Introductionmentioning

confidence: 99%

An Open Source, Iterative Dual-Tree Wavelet Background Subtraction Method Extended from Automated Diffraction Pattern Analysis to Optical Spectroscopy

Chevalier

Dwyer

2019

Appl Spectrosc

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Background subtraction is a general problem in spectroscopy often addressed with application-specific techniques, or methods that introduce a variety of implementation barriers such as having to specify peak-free regions of the spectrum. An iterative dual-tree complex wavelet transform-based background subtraction method (DTCWT-IA) was recently developed for the analysis of ultrafast electron diffraction patterns. The method was designed to require minimal user intervention, to support streamlined analysis of many diffraction patterns with complex overlapping peaks and time-varying backgrounds, and is implemented in an open-source computer program. We examined the performance of DTCWT-IA for the analysis of spectra acquired by a range of optical spectroscopies including ultraviolet–visible spectroscopy (UV–Vis), X-ray photoelectron spectroscopy (XPS), and surface-enhanced Raman spectroscopy (SERS). A key benefit of the method is that the user need not specify regions of the spectrum where no peaks are expected to occur. SER spectra were used to investigate the robustness of DTCWT-IA to signal-to-noise levels in the spectrum and to user operation, specifically to two of the algorithm parameter settings: decomposition level and iteration number. The single, general DTCWT-IA implementation performs well in comparison to the different conventional approaches to background subtraction for UV–Vis, XPS, and SERS, while requiring minimal input. The method thus holds the same potential for optical spectroscopy as for ultrafast electron diffraction, namely streamlined analysis of spectra with complex distributions of peaks and varying signal levels, thus supporting real-time spectral analysis or the analysis of data acquired from different sources.

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Section: Introductionmentioning

confidence: 99%

An Open Source, Iterative Dual-Tree Wavelet Background Subtraction Method Extended from Automated Diffraction Pattern Analysis to Optical Spectroscopy

Chevalier

Dwyer

2019

Appl Spectrosc

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“…The concentration of the substance detected by Raman microspectroscopy had a good correlation with the corresponding Raman signal after processing by WT (Cai et al . ). In this study, Biorthogonal wavelet was used to remove background and noise of Raman signal.…”

Section: Methodsmentioning

confidence: 97%

“…It has been successfully used in removing background contamination and noise of Raman signal (Cooper et al 2011;Li et al 2011). The concentration of the substance detected by Raman microspectroscopy had a good correlation with the corresponding Raman signal after processing by WT (Cai et al 2001). In this study, Biorthogonal wavelet was used to remove background and noise of Raman signal.…”

Section: Spectral Preprocessing Methods For Raman Spectrummentioning

confidence: 99%

Identification of pesticide varieties and concentrations by detecting characteristics ofChlorella pyrenoidosa

et al. 2015

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Although the low concentration level of pesticides does not have significant impact on the overall water environment, water would become worse due to the pollution accumulation in long-term. The method disclosed in this study can be used as a good example for pesticide detection with the help of microalgae. Raman microspectroscopy technology is a convenient and rapid tool to detect the characteristics of microalgae and contribute for pesticide detection.

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“…In 1997, WT application in chemical analysis was also confirmed by Wang et al ( 1997 ) and Depczynski et al ( 1997 ). Up to date, WT processing of the different types of raw signals has been reported for liquid chromatography (Shao et al, 1997 , 1998a , b , c ) and NMR spectroscopy (Neue, 1996 ; Barache et al, 1997 ), Raman spectra (Cai et al, 2001 ; Ehrentreich and Summchen, 2001 ), and voltammetry (Chen et al, 1996 ; Fang and Chen, 1997 ; Zheng et al, 1998 ; Zhong et al, 1998 ; Aballe et al, 1999 ; Zheng and Mo, 1999 ) IR and Raman spectroscopy (Shao and Zhuang, 2004 ; Hwang et al, 2005 ; Chalus et al, 2007 ; Jun-fang et al, 2007 ; Lai et al, 2011 ). In this context, as in the various fields of mathematics and engineering, the implementations of WT in analytical chemistry and neighbor disciplines has become increasingly attractive as an alternative way to analyze complex mixtures previously unresolved by traditional analytical techniques.…”

Section: Introductionmentioning

confidence: 99%

Wavelet Transform-Based UV Spectroscopy for Pharmaceutical Analysis

Dinç

Yazan

2018

Front. Chem.

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In research and development laboratories, chemical or pharmaceutical analysis has been carried out by evaluating sample signals obtained from instruments. However, the qualitative and quantitative determination based on raw signals may not be always possible due to sample complexity. In such cases, there is a need for powerful signal processing methodologies that can effectively process raw signals to get correct results. Wavelet transform is one of the most indispensable and popular signal processing methods currently used for noise removal, background correction, differentiation, data smoothing and filtering, data compression and separation of overlapping signals etc. This review article describes the theoretical aspects of wavelet transform (i.e., discrete, continuous and fractional) and its characteristic applications in UV spectroscopic analysis of pharmaceuticals.

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Application of the wavelet transform method in quantitative analysis of Raman spectra

Cited by 36 publications

References 7 publications

An Open Source, Iterative Dual-Tree Wavelet Background Subtraction Method Extended from Automated Diffraction Pattern Analysis to Optical Spectroscopy

An Open Source, Iterative Dual-Tree Wavelet Background Subtraction Method Extended from Automated Diffraction Pattern Analysis to Optical Spectroscopy

Identification of pesticide varieties and concentrations by detecting characteristics ofChlorella pyrenoidosa

Wavelet Transform-Based UV Spectroscopy for Pharmaceutical Analysis

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