Sub-optimal wavelet denoising of coaveraged spectra employing statistics from individual scans

Galvão, Roberto Kawakami Harrop; Filho, Heronides Adonias Dantas; Martins, Marcelo Nascimento; Araújo, Mário César Ugulino de; Pasquini, Célio

doi:10.1016/j.aca.2006.07.078

Cited by 16 publications

(12 citation statements)

References 17 publications

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“…In this context, the appropriate use of parallel computing plays an important role, as shown in this paper. It is worth noting that sample-wise parallelization can also be easily implemented in applications such as data compression, 42 noise removal, 43 and spectral library search. 44 In such cases, multiple threads can be used to process different samples simultaneously.…”

Section: Discussionmentioning

confidence: 99%

Multi-core computation in chemometrics: case studies of voltammetric and NIR spectrometric analyses

Soares¹,

Galvão²,

Araújo³

et al. 2010

J. Braz. Chem. Soc.

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A aplicação de técnicas quimiométricas sofisticadas a grandes conjuntos de dados tem se tornado possível devido aos contínuos aprimoramentos tecnológicos em computadores comerciais. Recentemente, tais aprimoramentos têm sido obtidos principalmente através da introdução de processadores com múltiplos núcleos. Contudo, o uso eficiente de hardware com múltiplos núcleos requer o desenvolvimento de software apropriado para computação paralela. Este artigo trata da implementação de paralelismo empregando o Matlab Parallel Computing Toolbox, que requer somente pequenas modificações em códigos quimiométricos já existentes de modo a explorar os benefícios do processamento em múltiplos núcleos. Empregando essa ferramenta de software, mostra-se que implementações paralelas podem proporcionar expressivos ganhos computacionais. Em particular, considera-se o problema de seleção de variáveis empregando o algoritmo das projeções sucessivas e o algoritmo genético, bem como o uso de validação cruzada em mínimos quadrados parciais. Para ilustração, duas aplicações analíticas são apresentadas: determinação de proteína em trigo por espectrometria de reflectância no infravermelho próximo e classificação de óleos vegetais comestíveis por voltametria de onda quadrada. Empregando as implementações propostas para computação paralela, ganhos computacionais de até 204% foram obtidos.The application of sophisticated chemometrics techniques to large datasets has been made possible by continuing technological improvements in off-the-shelf computers. Recently, such improvements have been mainly achieved by the introduction of multi-core processors. However, the efficient use of multi-core hardware requires the development of software that properly address parallel computing. This paper is concerned with the implementation of parallelism using the Matlab Parallel Computing Toolbox, which requires only simple modifications to existing chemometrics code in order to exploit the benefits of multi-core processing. By using this software tool, it is shown that parallel implementations may provide substantial computational gains. In particular, the present study considers the problem of variable selection employing the successive projections algorithm and the genetic algorithm, as well as the use of cross-validation in partial least squares. For demonstration, two analytical applications are presented: determination of protein in wheat by near-infrared reflectance spectrometry and classification of edible vegetable oils by square-wave voltammetry. By using the proposed parallel computing implementations, computational gains of up to 204% were obtained.Keywords: parallel computation, successive projections algorithm, genetic algorithm, partial least squares, voltammetric analysis, near-infrared spectrometric analysis Soares et al. 1627 Vol. 21, No. 9, 2010 IntroductionModern techniques and instrumentation provide ever-growing amounts of data (in terms of variables and samples) that need to be processed for analytical purposes. Hyphenated methods 1 ...

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Section: Discussionmentioning

confidence: 99%

Multi-core computation in chemometrics: case studies of voltammetric and NIR spectrometric analyses

Soares¹,

Galvão²,

Araújo³

et al. 2010

J. Braz. Chem. Soc.

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“…In the present work, different wavelet filters were tested and compared in terms of compression ability for the LIBS data set under consideration. The decomposition levels were set to the maximum number for which the spatial localization features of the WT are not lost [32]. This limit situation occurs when the H, G filters span the entire length of the downsampled approximation coefficients [36].…”

Section: Wavelet Compressionmentioning

confidence: 99%

“…The wavelet transform (WT) is a multi-resolutional signal processing tool [28] that has found several applications in denoising, feature extraction and compression of instrumental signals [29][30][31][32][33][34]. The WT of a spectrum x = [x( 1 ) x( 2 ) · · · x( J )], where j is the jth wavelength, can be obtained by using a digital filter bank structure [28,31,35] of the form depicted in Fig.…”

Section: Wavelet Compressionmentioning

confidence: 99%

Classification of Brazilian soils by using LIBS and variable selection in the wavelet domain

Pontes

Cortez

Galvão

et al. 2009

Analytica Chimica Acta

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109

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“…The wavelet transform (WT) has been used in a variety of signal processing applications, such as filtering [10], compression [24] and classification [8]. In particular, the WT has become a popular tool in a field of Analytical Chemistry known as Multivariate Calibration (MC) [7,15].…”

Section: Introdutionmentioning

confidence: 99%

Combination of Models Obtained by Regression in the Wavelet Domain

Pinto¹,

Galvão²

2010

TEMA - Tendências Em Matemática Aplicada E Computacional

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Abstract. The wavelet transform is a useful tool to preprocess and compress datasets for linear regression modelling. However, the prediction performance of the resulting model depends on the choice of wavelet filter and number of decomposition levels, which may not be a straightforward task. This paper proposes an alternative approach, which consists of combining models obtained from different wavelet decompositions of the dataset. For this purpose, a method is developed to convert wavelet regression models back to the original domain. The proposed approach is illustrated in a case study involving the determination of density in gasoline samples by using infrared spectroscopy. The results are favourably compared to those obtained by using individual wavelet decompositions.Keywords. Wavelet Transform, Combination of Models, Multiple Linear Regression. IntrodutionThe wavelet transform (WT) has been used in a variety of signal processing applications, such as filtering [10], compression [24] and classification [8]. In particular, the WT has become a popular tool in a field of Analytical Chemistry known as Multivariate Calibration (MC) [7,15]. The MC problem consists of building a mathematical model for estimation of physical or chemical properties of a sample from indirect measurements. Such measurements could be, for instance, emission, absorption or reflection intensities acquired over different wavelengths [20]. Applications of MC include determination of metals in steel alloys [16], analysis of composition of pharmaceutical formulae [5] and prediction of fuel properties [3], among many others.

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Sub-optimal wavelet denoising of coaveraged spectra employing statistics from individual scans

Cited by 16 publications

References 17 publications

Multi-core computation in chemometrics: case studies of voltammetric and NIR spectrometric analyses

Multi-core computation in chemometrics: case studies of voltammetric and NIR spectrometric analyses

Classification of Brazilian soils by using LIBS and variable selection in the wavelet domain

Combination of Models Obtained by Regression in the Wavelet Domain

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