The wavelet transform: A new preprocessing method for peak recognition of infrared spectra

Ehrentreich, F.; Nikolov, SG; Wolkenstein, M.; Hutter, Herbert

doi:10.1007/bf01243056

Cited by 11 publications

(6 citation statements)

References 6 publications

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“…The first covers applications for improvement of signal-to-noise ratios [18,19,20,21,22,23,24,25] and the second concerns data compression [26,27,28,29,30]. Often, appropriate routines are embedded in larger procedures as feature extraction [26], peak recognition [21], calibration [27,28] or spectral library search [30].…”

Section: Applications In Analytical Chemistrymentioning

confidence: 99%

Wavelet transform applications in analytical chemistry

Ehrentreich

2001

Anal Bioanal Chem

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The wavelet transform has been established with the Fourier transform as a data-processing method in analytical chemistry. The main fields of application in analytical chemistry are related to denoising, compression, variable reduction, and signal suppression. Analytical applications were selected showing prospects and limitations of the wavelet transform. An important aspect consists in showing the advantage of wavelet transform over Fourier transform with respect to dual localization of a signal in both the original and the transformed domain enabling principal new application fields in comparison with Fourier transform.

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Section: Applications In Analytical Chemistrymentioning

confidence: 99%

Wavelet transform applications in analytical chemistry

Ehrentreich

2001

Anal Bioanal Chem

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“…It has been shown to aid in peak definition in infrared spectra 30 and ToF-SIMS image analysis and classification. 20,31,32 Wavelet denoising is accomplished by applying a wavelet transform to the image, which decomposes the image signal into multiple levels containing varying degrees of high-to low-frequency information.…”

Section: Introductionmentioning

confidence: 99%

Denoising and multivariate analysis of time‐of‐flight SIMS images

Wickes

Kim

Castner

2003

Surface & Interface Analysis

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Time-of-flight SIMS (ToF-SIMS) imaging offers a modality for simultaneously visualizing the spatial distribution of different surface species. However, the utility of ToF-SIMS datasets may be limited by their large size, degraded mass resolution and low ion counts per pixel. Through denoising and multivariate image analysis, regions of similar chemistries may be differentiated more readily in ToF-SIMS image data. Three established denoising algorithms -down-binning, boxcar and wavelet filtering -were applied to ToF-SIMS images of different surface geometries and chemistries. The effect of these filters on the performance of principal component analysis (PCA) was evaluated in terms of the capture of important chemical image features in the principal component score images, the quality of the principal component score images and the ability of the principal components to explain the chemistries responsible for the image contrast. All filtering methods were found to improve the performance of PCA for all image datasets studied by improving capture of image features and producing principal component score images of higher quality than the unfiltered ion images. The loadings for filtered and unfiltered PCA models described the regions of chemical contrast by identifying peaks defining the regions of different surface chemistry. Down-binning the images to increase pixel size and signal was the most effective technique to improve PCA performance.

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“…Hence a "softer" distribution of the small coefficients takes place after the thresholding. In contrast, hard thresholding produces a gap between 0 and t. Soft thresholding usually results in a good visual quality of the signal, and hard thresholding gives generally a better reproduction of peak height and discontinuities but sometimes at the cost of occasional artifacts [14] .…”

Section: S Median X =mentioning

confidence: 97%

“…Barclay and Bonner [13] distinguished between various methods for smoothing and denoising data sets in the signal domain and its transformed domain and also presented other methods based on discrete wavelet transform (DWT) technique, which was showed to be highly successful for data sets with great dynamic range. Ehrentreich et al [14] reported WT to be used in peak recognition in infrared spectroscopy and showed that some of the wavelet bases lead to a very good compromise between signal/noise ratio enhancement and preservation of the real data structures. Ma et al [15] proposed continuous wavelet transform (CWT) as a preprocessing tool for the near-infrared (NIR) spectra.…”

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confidence: 99%

Effect of wavelet denoising techniques on the determination of navel orange sugar content with near-infrared spectra

Xie

Huang

et al. 2006

SPIE Proceedings

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Near infrared (NIR) spectroscopy is an ideal analytical method for rapid and nondestrctive measurement of the properties of agriculture products. The efficient use of this method is dependent on multivariate calibration methods determined by the sensitivity to variations. However, fluctuation of background and noise are unavoidable during collecting spectra, which will not only worsen the precision of prediction, but also complicate the multivariate models. Therefore, the first step of a multivariate calibration based on NIR spectra data is often to preprocess the data for the purpose of removing the varying background and noise. In this study, wavelet transform (WT) was used to eliminate the varying background and noise simultaneously in the near infrared spectroscopy signals of 55 navel oranges. Three families of mother wavelets (Symlets, Daubechies and Coiflet), four threshold selection rules (Rigrsure, Heursure, Minimaxi, Fixed form threshold), and two threshold functions (soft and hard) were applied to estimate the performances. The sugar content of intact navel orange was calculated by partial least squares regression (PLSR) with the reconstructed spectra after denoised. The results show that the best denoising performance was reached via the combination of Daubechies 5, "Fixed form" threshold selection rule, and hard threshold function. Based on the optimization parameter, wavelet regression models on sugar content in navel orange were also developed and resulted in a smaller prediction error than a traditional PLSR model.

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The wavelet transform: A new preprocessing method for peak recognition of infrared spectra

Cited by 11 publications

References 6 publications

Wavelet transform applications in analytical chemistry

Wavelet transform applications in analytical chemistry

Denoising and multivariate analysis of time‐of‐flight SIMS images

Effect of wavelet denoising techniques on the determination of navel orange sugar content with near-infrared spectra

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