The Use of Wavelets for Signal Denoising in Capillary Electrophoresis

Perrin, Catherine; Walczak, Beata; Massart, D.L.

doi:10.1021/ac010416a

Cited by 73 publications

(77 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Use of the Haar wavelet basis removes the need for the inverse transform. The use of wavelet transforms has allowed noise removal and baseline correction to be applied simultaneously [24,26]. However, as with curve fitting algorithms for baseline correction, wavelet methods require electropherograms with sections that consist only of noise indicating the true baseline.…”

Section: Ce and Cec 951mentioning

confidence: 99%

“…Wavelet transforms are now commonly used in chemometric data analysis and have been used for denoising CE data (see, for example [24][25][26][27]). At each level of the wavelet transform, a combination of a low-pass filter, giving a smoothed approximation, and a high-pass filter, storing the high frequency details, is applied.…”

Section: Noise Reductionmentioning

confidence: 99%

“…The final denoised signal is obtained by averaging over these filtered signals after applying the inverse DWT to each. This method, known as cycle-spinning, can be computationally expensive but this is alleviated by taking some fixed number (n) of shifts to reduce the effect of artefacts [26].…”

Section: Noise Reductionmentioning

confidence: 99%

“…Wavelet transforms have become the tool of choice for signal denoising [24][25][26][27] and have been shown to perform better than other commonly used smoothing algorithms [26]. The discrete wavelet transform (DWT) can introduce artefacts related to the choice of wavelet basis and to overcome this we use an undecimated wavelet transform.…”

Section: Introductionmentioning

confidence: 99%

“…The discrete wavelet transform (DWT) can introduce artefacts related to the choice of wavelet basis and to overcome this we use an undecimated wavelet transform. The implementation differs from that employed in [26] in that, rather than using cycle-spinning, which can be computationally expensive, we simply interleave the results from the transforms associated with the 2 n alternative origins (where n is the number of wavelet levels). Use of the Haar wavelet basis removes the need for the inverse transform.…”

mentioning

confidence: 99%

See 4 more Smart Citations

Data processing in metabolic fingerprinting by CE‐UV: Application to urine samples from autistic children

Soria

Wright²,

Goodall

et al. 2007

Electrophoresis

View full text Add to dashboard Cite

Data processing in metabolic fingerprinting by CE-UV: Application to urine samples from autistic childrenMetabolic fingerprinting of biofluids such as urine can be used to detect and analyse differences between individuals. However, before pattern recognition methods can be utilised for classification, preprocessing techniques for the denoising, baseline removal, normalisation and alignment of electropherograms must be applied. Here a MEKC method using diode array detection has been used for high-resolution separation of both charged and neutral metabolites. Novel and generic algorithms have been developed for use prior to multivariate data analysis. Alignment is achieved by combining the use of reference peaks with a method that uses information from multiple wavelengths to align electropherograms to a reference signal. This metabolic fingerprinting approach by MEKC has been applied for the first time to urine samples from autistic and control children in a nontargeted and unbiased search for markers for autism. Although no biomarkers for autism could be determined using MEKC data here, the general approach presented could also be applied to the processing of other data collected by CE with UV-Vis detection. Keywords IntroductionMetabolic fingerprinting approaches [1] have recently emerged as a nonbiased alternative to the determination of individual levels of a specific metabolite or class of metabolites in large sets of samples, as is necessary for clinical diagnosis purposes. 1 H-NMR has been widely used in urinary fingerprinting and the search for disease biomarkers [2,3]. The approach has been recently complemented by chromatographic techniques hyphenated to MS (GC-MS and LC-MS), with the advantage over 1 H-NMR of a higher sensitivity [4][5][6][7]. Other spectroscopic techniques used less frequently for urinary fingerprinting include direct injection MS [8,9] and FTIR spectroscopy [10]. CE has recently been incorporated to the family of metabolic fingerprinting techniques used for clinical studies [11][12][13][14]. CE is an efficient and sensitive high-resolution separation technique that allows fast analysis of ionic species and the use of small samples.MEKC, applicable to both ionic and neutral species, has proved to be a useful tool in the study of different target metabolites in urine [15][16][17][18]. However, its application as a nontargeted approach has not yet been fully exploited. CD-modified MEKC allows the separation of over 80 UVabsorbing endogenous analytes of different charge types (neutral, anionic and cationic) below 25 min [18][19][20]. Since minimal sample pretreatment is required, this methodology has the potential for high-throughput analysis of the large number of samples required in screening applications.Urinary fingerprints produced by MEKC are highly complex datasets and require multivariate techniques for interpretation. Unsupervised pattern recognition methods such as cluster analysis and principal component analysis (PCA) have been used to examine the structure of these dataset...

show abstract

Section: Ce and Cec 951mentioning

confidence: 99%

Section: Noise Reductionmentioning

confidence: 99%

Section: Noise Reductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

Data processing in metabolic fingerprinting by CE‐UV: Application to urine samples from autistic children

Soria

Wright²,

Goodall

et al. 2007

Electrophoresis

View full text Add to dashboard Cite

show abstract

Denoising

2004

Encyclopedic Dictionary of Genetics, Genomics and Proteomics

View full text Add to dashboard Cite

Accurate Information from Fermentation Processes – Optimal Rate Calculation by Dynamic Window Adaptation

Zahel

Sagmeister

Suchocki

et al. 2016

Chemie Ingenieur Technik

View full text Add to dashboard Cite

The calculation of metabolic turnover rates is essential for the scalable design, analysis and control of bioprocesses. A novel rate calculation algorithm is presented that is based on the dynamic adaptation of window sizes in order to deliver robust and precise rates with uniform signal-to-noise ratios. Additionally a model-based generic algorithm for deriving optimal rate calculation workflows was developed. The generic algorithms delivered more precise and accurate rates for online and offline signals, which was demonstrated for both in silico-and real batch and fed-batch fermentation process data. The presented algorithms will strongly support bioprocess development and control as enabling tools for multivariate data analysis, mechanistic modeling and dynamic experimentation.

show abstract

The Use of Wavelets for Signal Denoising in Capillary Electrophoresis

Cited by 73 publications

References 17 publications

Data processing in metabolic fingerprinting by CE‐UV: Application to urine samples from autistic children

Data processing in metabolic fingerprinting by CE‐UV: Application to urine samples from autistic children

Denoising

Accurate Information from Fermentation Processes – Optimal Rate Calculation by Dynamic Window Adaptation

Contact Info

Product

Resources

About