Spike Removal and Denoising of Raman Spectra by Wavelet Transform Methods

Ehrentreich, F.; Sümmchen, L.

doi:10.1021/ac0013756

Cited by 148 publications

(125 citation statements)

References 28 publications

(30 reference statements)

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“…This technique is simple and efficient. In this paper we were de-noise the Raman Spectral signal of PZT-PMN using Daubechies, Symlets and Coiflet wavelet at different levels [3,5,6,7,8,9].…”

Section: Transformmentioning

confidence: 99%

Raman Spectral Data De-noising based on Wavelet Analysis

Kumar¹,

Siddiqi²,

Alam³

2014

IJCA

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Nowadays, most analytical instruments in modern laboratories are computerized, partly owing to the rapid development of advanced micro-electronic technology. Digitalized spectroscopic data can be exported from these instruments very easily for subsequent signal processing. Raman Spectroscopy is widely recognized as powerful, nondestructive techniques for characterizing materials. The key to realize the qualitative and quantitative analysis is data processing and analysis. But signals in Raman spectral analysis often have noise, which greatly influences the achievement of accurate analytical results. The de-noising of Raman spectral is an important part of de-noising. Wavelet functions are localized both time and frequency (or scale) and in time, via dilations and translations of the mother wavelet, respectively, both time and frequency information are maintained after transformation. This paper presents wavelet based de-noising method for Raman Spectral data of Sr 2+ modified PMN-PZT and compared the results with Daubechies, Coiflet, Symlet.

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Section: Transformmentioning

confidence: 99%

Raman Spectral Data De-noising based on Wavelet Analysis

Kumar¹,

Siddiqi²,

Alam³

2014

IJCA

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“…2,3,6,7 Embora em alguns casos a aquisição de imagens pelo monitoramento seletivo de um determinado comprimento de onda seja possível, soluções mais robustas apontam para a utilização de uma abordagem multivariada. 11 Na PCA, a presença de spikes nos espectros pode causar uma distorção na direção dos eixos das componentes principais em função do aumento da variância em um dado comprimento de onda.…”

Section: 2unclassified

“…2,5 A identificação e a eliminação de spikes presentes nos espectros são de fundamental importância para as aplicações de ERI, porque podem mascarar ou deformar as bandas de interesse químico e limitar o desempenho dos métodos de análise de dados, seja univariada ou multivariada. 2,3,6,7 Embora em alguns casos a aquisição de imagens pelo monitoramento seletivo de um determinado comprimento de onda seja possível, soluções mais robustas apontam para a utilização de uma abordagem multivariada. 8 Os métodos multivariados mais empregados em análise 10 e mínimos quadrados parciais, Partial Least Squares (PLS).…”

Section: Introductionunclassified

Desenvolvimento de um algoritmo para identificação e correção de spikes em espectroscopia raman de imagem

Sabin¹,

Souza²,

Breitkreitz³

et al. 2012

Quím. Nova

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Recebido em 25/5/11; aceito em 9/9/11; publicado na web em 8/11/11 DEVELOPMENT OF AN ALGORITHM FOR IDENTIFICATION AND CORRECTION OF SPIKES IN RAMAN IMAGING SPECTROSCOPY. Raman imaging spectroscopy is a highly useful analytical tool that provides spatial and spectral information on a sample. However, CCD detectors used in dispersive instruments present the drawback of being sensitive to cosmic rays, giving rise to spikes in Raman spectra. Spikes influence variance structures and must be removed prior to the use of multivariate techniques. A new algorithm for correction of spikes in Raman imaging was developed using an approach based on comparison of nearest neighbor pixels. The algorithm showed characteristics including simplicity, rapidity, selectivity and high quality in spike removal from hyperspectral images.Keywords: Raman imaging; spikes filter; algorithm development. INTRODUÇÃOEspectroscopia Raman de Imagem (ERI) combina espectroscopia Raman com tecnologia de imagem digital para fornecer, simultaneamente, informação espacial e espectral sobre uma amostra e, por isso, oferece novas possibilidades para a caracterização de materiais biológicos, farmacêuticos, entre outros. Para a geração da imagem, seleciona-se uma área da amostra, com dimensões espaciais x e y, a qual é dividida em pixels. São então obtidos espectros a cada pixel, gerando um cubo de dados (cubo hiperespectral). Devido ao elevado volume de dados gerados e ao fato de que as respostas espectrais se encontram correlacionadas, métodos de análise multivariada de tratamento de imagens hiperespectrais são comumente aplicados para a identificação dos componentes da amostra e para a efetiva extração de informação sobre a distribuição espacial destes componentes. 1,2Detectores de dispositivo de acoplamento de carga, Charge Coupled Device (CCD), têm sido utilizados em equipamentos de espectroscopia Raman dispersivos devido à alta sensibilidade e baixo ruído, entretanto, um problema intrínseco ao uso dos detectores de CCD é sua vulnerabilidade a raios cósmicos.3,4 Raios cósmicos produzem interferências nos espectros, chamadas de spikes, que são picos estreitos de intensidade variada. Estes interferentes resultam de raios gama (altamente energéticos) que são capazes de penetrar em diversos materiais e atingir os detectores de CCD. Os sinais resultantes dos spikes derivam do excesso de elétrons gerados em um único pixel ou em vários pixels adjacentes no detector sendo caracterizados por: 3 bandas tipicamente estreitas; unidirecionais, sempre positivas em relação à linha de base; padrão aleatório de distribuição temporal e, consequentemente, imprevisibilidade de ocorrência no espaço amostral.2,5 A identificação e a eliminação de spikes presentes nos espectros são de fundamental importância para as aplicações de ERI, porque podem mascarar ou deformar as bandas de interesse químico e limitar o desempenho dos métodos de análise de dados, seja univariada ou multivariada. 2,3,6,7 Embora em alguns casos a aquisição de imagens pelo monitoramento seletivo de um det...

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“…Since the incidence of cosmic ray Raman spikes cannot be predicted, they are particularly problematic in Raman data acquired during chemical reaction or process monitoring, in which repeated Raman measurements may not always be a viable option because the chemical composition of the sample changes with time. Several different numerical techniques have been devised to computationally detect and remove stray cosmic ray signals in Raman spectra, such as missing point fitting, [31 -34] robust summation, [35] wavelet transform, [32,36] upper bound spectrum, [37 -39] polynomial interpolation [32] and moving average window. [34] In this paper, a set of numerical techniques entirely based on the information-theoretic approach is utilized in concert to elucidate chemical information from chemical reaction studies via in situ Raman spectroscopy.…”

Section: Introductionmentioning

confidence: 99%

Information‐theoretic chemometric analyses of Raman data for chemical reaction studies

Chew

2011

J Raman Spectroscopy

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A methodology of multivariate chemometric techniques based on the information-theoretic approach was applied for elucidating chemical reaction information from a Raman data array R m×ν that arises from in situ reaction monitoring. This reaction-induced dynamic dataset R m×ν can be contaminated by random cosmic ray spikes found in the midst of characteristic spectral variations associated with the disappearance or emergence of Raman active reactants, intermediates and products. Such spurious cosmic spikes were identified and removed using a novel and fast numerical approach based on maximum and minimum spectral entropy principles while preserving the genuine reaction-induced spectral variations. Subsequently, the band-target entropy minimization (BTEM) algorithm, a minimum spectral entropy based self-modeling curve resolution technique, was applied to recover the pure component spectra of Raman active chemical species. Information gain through the chemometric analyses was calculated using information entropies with base 2 logarithm. This sequence of information-theoretic chemometric analyses (or transinformations) was successfully tested on the reaction spectral data obtained from alcoholysis of acetic anhydride, which contains four Raman active chemical species. It is envisioned that this series of multivariate statistical analyses will be useful in chemical reaction studies and process analytical technology (PAT) applications that utilize in situ Raman spectroscopy to monitor transient dynamic changes in chemical concentrations, and also in Raman microscopy/imaging data containing spatial variations.

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Spike Removal and Denoising of Raman Spectra by Wavelet Transform Methods

Cited by 148 publications

References 28 publications

Raman Spectral Data De-noising based on Wavelet Analysis

Raman Spectral Data De-noising based on Wavelet Analysis

Desenvolvimento de um algoritmo para identificação e correção de spikes em espectroscopia raman de imagem

Information‐theoretic chemometric analyses of Raman data for chemical reaction studies

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