Raman spectra enhancement with a fuzzy logic approach

Soneira, M. J.; Pérez-Pueyo, R.; Ruiz-Moreno, Sergio

doi:10.1002/jrs.885

Cited by 22 publications

(14 citation statements)

References 14 publications

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“…It is developed to eliminate the cosmic noises with random wave numbers and shot noises. The results show that the fuzzy filter can be successfully applied to enhance the signal to noise ratio [5]. Basically Raman spectroscopy is used to send monochromatic light onto the sample and capture its scattered light simultaneously.…”

Section: Introductionmentioning

confidence: 99%

Spectral feature extraction and characterization via simple linear and nonlinear technology integration

Ye¹,

Mohamadian²

2011

2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI)

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A systematic approach for spectral calibration, extraction and characterization is presented. It involves the simple technology integration of the linear and nonlinear methodologies. Raman spectra are essentially weak signals whose features are sensitive to a variety of noises, while the background spectra (e.g., fluorescence spectra) act as another primary intensity component in the raw Raman spectrograph. Thus the calibrated Raman spectra from measurements are subject to detrend and denoising so as to extract the intrinsic spectra, which contain solely the unique spectral signatures of the individual biomedical samples. By removing the slowly varying baseline background spectra and rapidly varying noises, intrinsic Raman spectra could be extracted using a combination of linear and nonlinear methodologies. The goal is to capture the distinguishable wave number information on the Raman shift for feature identification. This approach is crucial for spectra analysis to associate Raman spectra with the medical diagnosis. Considering the slowly varying nature of background spectra such as fluorescence, linear least squares estimation is applied to detrend the background spectra from the measured spectra being calibrated. Since most noises are rapidly varying, nonlinear discrete wavelet transform (DWT) denoising is applied to separate the intrinsic Raman spectra from noises for spectral identification. This approach is proposed so as to differentiate the normal spectra from the abnormal spectra of the fresh mice lung samples. Numerical simulation outcomes include the calibrated Raman spectra, detrended spectra free of background effects and the extracted intrinsic spectra after nonlinear DWT denoising. The satisfactory results indicate that the proposed simple linear and nonlinear technology integration provides a useful approach for biomedical sample characterization. In addition, it reduces computational complexity compared with artificial intelligence approaches, which has no technical difficulty in real time medical diagnosis implementation. This serves as a fundamental step for further data clustering and accurate decision making among numerous diverse samples.

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Section: Introductionmentioning

confidence: 99%

Spectral feature extraction and characterization via simple linear and nonlinear technology integration

Ye¹,

Mohamadian²

2011

2011 IEEE 12th International Symposium on Computational Intelligence and Informatics (CINTI)

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“…There is no single strategy for noise filtering in Raman spectroscopy. Several methods have been proposed to enhance the Raman information . The most frequently used methods are software procedures, which do not require upgrading the existing instrumentation.…”

Section: Introductionmentioning

confidence: 99%

Automatic morphology‐based cubic p‐spline fitting methodology for smoothing and baseline‐removal of Raman spectra

González-Vidal

Pérez-Pueyo

Soneira

2017

J Raman Spectroscopy

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Noise filtering is considered a crucial step for the proper interpretation of Raman spectra. In this work, we present a new denoising procedure which enhances the Raman information whilst reducing unwanted contributions from the most frequent noise sources, i.e. the shot noise and the fluorescence's baseline. The procedure increases the signal-to-noise ratio whilst preserving simultaneously the shapes, positions and intensity ratios of the Raman bands. The method relies on cubic penalized spline fitting and mathematical morphology and requires no user input. We describe the details of this method and include a benchmark to study the performance of the presented approach compared with the most commonly used denoising techniques. The method has been successfully applied to improve the signal quality of Raman spectra from artistic pigments. The reliable results that were obtained make the methodology a useful tool to help the analyst in the interpretation of Raman spectra from pigments in artworks. Copyright © 2017 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

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“…Prior to Raman data analysis, data pretreatment needs to be performed in a correct order. Various approaches have been conducted to enhance Raman spectrum by increasing the signal to noise ratio, among which the shot noise and background dark noise are main factors [5][6]. Approaches such as denoise, detrend and standard normal variate (SNV) have been used to extract the shot noise, the laser excited fluorescence as well as the scatter effects [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Control oriented sample characterization using systematic multivariate statistical method

Ye¹,

Ye²,

Hua³

2004

Second IEEE International Conference on Computational Cybernetics, 2004. ICCC 2004.

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The identification and decision-making for biomedical samples can be taken via Raman spectroscopic technology.Conventional approach for Raman spectral analysis is to search the peak spectrum location directly so as to determine relevant Raman shift information. In many cases it is hard to find specific Raman band spectrum that is suitable for discrimination among various samples. This is due to the influence from numerous types of slowly varying and rapidly varying noises. A proper pretreatment must be conducted prior to Raman data analysiS.Multivariate statistical technique can then be employed to take advantage of simultaneous information presented in the whole spectrum or in any specific parts. Multivariate spectral analysis has already been implemented and reported for absorption or reflectance spectroscopy, while it is applicable to the analysis of Raman spectrum as well. Raman spectroscopy can indicate molecular composition and it enables the detection of sample pathological changes in a non-destructive manner. Due to its nature as a weak signal, Raman spectrum is undoubtedly sensitive to most types of noises. Wavelet technique is therefore used for noise filtering to an acceptable level. To eliminate the scatter effect due to varying conditions, standard normal variate (SNV) can be processed so that all spectra can contribute equally. A dimension reduction approach -principal component analysis (PCA) is then proposed for sample characterization to capture significant signatures. Eventually different principal components are selected for clustering and various samples from different organs can be differentiated. A feasible approach for sample analysis is formulated and satisfactory results are obtained

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Raman spectra enhancement with a fuzzy logic approach

Cited by 22 publications

References 14 publications

Spectral feature extraction and characterization via simple linear and nonlinear technology integration

Spectral feature extraction and characterization via simple linear and nonlinear technology integration

Automatic morphology‐based cubic p‐spline fitting methodology for smoothing and baseline‐removal of Raman spectra

Control oriented sample characterization using systematic multivariate statistical method

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