Autocorrelation Analysis Combined with a Wavelet Transform Method to Detect and Remove Cosmic Rays in a Single Raman Spectrum

Maury, Augusto; Revilla, Reynier I.

doi:10.1366/14-07834

Cited by 22 publications

(21 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…combined a kernel PCA with noise estimation to detect spikes and then a best‐fit spectrum replacement to correct the Raman spectra. A different approach was proposed by Maury and co‐workers, who combined an autocorrelation analysis with a discrete wavelet transform filter to identify spikes and interpolate them by making use of a weighted‐average interpolation technique. Their algorithm is limited to spikes with a much narrower bandwidth than the Raman bands.…”

Section: Introductionmentioning

confidence: 99%

Label‐Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

et al. 2017

View full text Add to dashboard Cite

Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012.

show abstract

Section: Introductionmentioning

confidence: 99%

Label‐Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

et al. 2017

View full text Add to dashboard Cite

show abstract

“…However, some sharp peaks can resemble spikes and might be falsely interpreted as such (i.e., false alarms) in a variety of different despiking methods. 46,[66][67][68] Where a data set contains sharp peaks that repeatedly produce false alarms, the false alarms can, to some extent, be identified when they also occur in other spectra in the same position. This can be detected by examining the number of spike occurrences at every wavenumber and discounting the ''spikes'' if they occur with frequency greater than what would be expected for the detector used at its location.…”

Section: Cosmic Ray-induced Spike Removalmentioning

confidence: 99%

Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples

et al. 2018

View full text Add to dashboard Cite

Spectral preprocessing is frequently required to render Raman spectra useful for further processing and analyses. The various preprocessing steps, individually and sequentially, are increasingly being automated to cope with large volumes of data from, for example, hyperspectral imaging studies. Full automation of preprocessing is especially desirable when it produces consistent results and requires minimal user input. It is therefore essential to evaluate the "quality" of such preprocessed spectra. However, relatively few methods exist to evaluate preprocessing quality, and fully automated methods for doing so are virtually non-existent. Here we provide a brief overview of fully automated spectral preprocessing and fully automated quality assessment of preprocessed spectra. We follow this with the introduction of fully automated methods to establish figures-of-merit that encapsulate preprocessing quality. By way of illustration, these quantitative methods are applied to simulated and real Raman spectra. Quality factor and quality parameter figures-of-merit resulting from individual preprocessing step quality tests, as well as overall figures-of-merit, were found to be consistent with the quality of preprocessed spectra.

show abstract

“…In the second category methods such as analyzing the full CCD image 10 , division of the spectrograph slit and image curvature correction 11 . Finally in the third category methods such as moving window filtering 12 , spike fitting , wavelet transforms 14,15,16 and median or polynomial filters 17,18 .…”

Section: Introductionmentioning

confidence: 99%

A Simple Algorithm for Despiking Raman Spectra

Whitaker¹,

Hayes²

2018

Preprint

View full text Add to dashboard Cite

Raman Spectroscopy is a widely used analytical technique, favoured when molecular specificity with minimal sample preparation is required.<br>The majority of Raman instruments use charge-coupled device (CCD) detectors, these are susceptible to cosmic rays and as such multiple spurious spikes can occur in the measurement. These spikes are problematic as they may hinder subsequent analysis, particularly if multivariate data analysis is required. In this work we present a new algorithm to remove these spikes from spectra after acquisition. Specifically we use calculation of modified <i>Z</i> scores to locate spikes followed by a simple moving average filter to remove them. The algorithm is very simple and its execution is essentially instantaneous, resulting in spike-free spectra with minimal distortion of actual Raman data. The presented algorithm represents an improvement on existing spike removal methods by utilising simple, easy to understand mathematical concepts, making it ideal for experts and non-experts alike. <br>

show abstract

Autocorrelation Analysis Combined with a Wavelet Transform Method to Detect and Remove Cosmic Rays in a Single Raman Spectrum

Cited by 22 publications

References 27 publications

Label‐Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

Label‐Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches

Developing Fully Automated Quality Control Methods for Preprocessing Raman Spectra of Biomedical and Biological Samples

A Simple Algorithm for Despiking Raman Spectra

Contact Info

Product

Resources

About