Fluorescence and noise subtraction from Raman spectra by using wavelets

Villanueva-Luna, A. E.; Castro‐Ramos, J.; Vázquez‐Montiel, S.; Flores, Aaron; Delgado-Atencio, J. A.; Orozco-Guillen, E. E.

doi:10.3103/s1060992x10040089

Cited by 9 publications

(16 citation statements)

References 16 publications

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“…3(g)) methods showed good performance in noise removal, at the cost of losing some important spectral shape information, e.g., the central wavelengths and bandwidths of the peaks. 21 Unfortunately, the factor analysis did not work well when the SNR was extremely low, which could not even smoothen out the noise and lost plenty of useful information during bacterial discrimination, as shown in Fig. 3(h).…”

Section: Pca-svm Based Classication Methodsmentioning

confidence: 99%

“…20 In contrast, the wavelet transform, FIR ltration, and factor analysis commonly remove noise by ltering techniques. For the wavelet transform, 21,22 the spectral data were decomposed into the wavelet domain by various wavelet basis and reconstructed aer noise removal by certain thresholds. The FIR ltration is a linear ltration technique, in which a windowbased FIR lter is designed based on the frame size and cut-off frequency and was subsequently used for noise removal in this study.…”

Section: Spectral Recovery Methodsmentioning

confidence: 99%

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Weighted spectral reconstruction method for discrimination of bacterial species with low signal-to-noise ratio Raman measurements

Zhu

Cui

et al. 2019

RSC Adv.

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Section: Pca-svm Based Classication Methodsmentioning

confidence: 99%

Section: Spectral Recovery Methodsmentioning

confidence: 99%

Weighted spectral reconstruction method for discrimination of bacterial species with low signal-to-noise ratio Raman measurements

Zhu

Cui

et al. 2019

RSC Adv.

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“…In the first one, we used the algorithm developed by A. Cao et al that is based in polynomial fitting to remove the fluorescence background. In the second one, we used the algorithm developed by Villanueva‐Luna et al that is based in wavelet transform to reduce the shot noise, finally we used normalization at 1486 cm –1 to reduce the intensity differences. To develop our algorithm we use PCA.…”

Section: Methodsmentioning

confidence: 99%

Raman spectroscopy and PCA-SVM as a non-invasive diagnostic tool to identify and classify qualitatively glycated hemoglobin levels in vivo

et al. 2016

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In this study we identify and classify high and low levels of glycated hemoglobin (HbA1c) in healthy volunteers (HV) and diabetic patients (DP). Overall, 86 subjects were evaluated. The Raman spectrum was measured in three anatomical regions of the body: index fingertip, right ear lobe, and forehead. The measurements were performed to compare the difference between the HV and DP (22 well controlled diabetic patients (WCDP) (HbA1c <6.5%), and 49 not controlled diabetic patients (NCDP) (HbA1c ≥6.5%)). Multivariable methods such as principal components analysis (PCA) combined with support vector machine (SVM) were used to develop effective diagnostic algorithms for classification among these groups. The forehead of HV versus WCDP showed the highest sensitivity (100%) and specificity (100%). Sensitivity (100%) and specificity (60%), were highest in the forehead of WCDP, versus NCDP. In HV versus NCDP, the fingertip had the highest sensitivity (100%) and specificity (80%). The efficacy of the diagnostic algorithm by receiver operating characteristic (ROC) curve was confirmed. Overall, our study demonstrated that the combination of Raman spectroscopy and PCA-SVM are feasible non-invasive diagnostic tool in diabetes to classify qualitatively high and low levels of HbA1c in vivo.

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“…Given the fact that the background fluorescence in Raman spectroscopy affects the accuracy signal detection, background fluorescence was removed. In order to remove the fluorescence background in the Raman spectrum, the method described by Villanueva-Luna et al [29] was used. The technique is based in wavelets theory, using symlets and bi-orthogonals wavelets, which improve the accuracy in the determination of spectral peaks.…”

Section: Fluorescence Background Removalmentioning

confidence: 99%

Novel Assessment of Urinary Albumin Excretion in Type 2 Diabetes Patients by Raman Spectroscopy

et al. 2020

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Urinary albumin excretion remains the key biomarker to detect renal complications in type 2 diabetes. As diabetes epidemy increases, particularly in low-income countries, efficient and low-cost methods to measure urinary albumin are needed. In this pilot study, we evaluated the performance of Raman spectroscopy in the assessment of urinary albumin in patients with type 2 diabetes. The spectral Raman analysis of albumin was performed using artificial urine, at five concentrations of albumin and 24 h collection urine samples from ten patients with Type 2 Diabetes. The spectra were obtained after removing the background fluorescence and fitting Gaussian curves to spectral regions containing features of such metabolites. In the samples from patients with type 2 diabetes, we identified the presence of albumin in the peaks of the spectrum located at 663.07, 993.43, 1021.43, 1235.28, 1429.91 and 1633.91 cm−1. In artificial urine, there was an increase in the intensity of the Raman signal at 1450 cm−1, which corresponds to the increment of the concentrations of albumin. The highest concentration of albumin was located at 1630 cm−1. The capability of Raman spectroscopy for detection of small concentrations of urinary albumin suggests the feasibility of this method for the screening of type 2 diabetes renal complications.

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Fluorescence and noise subtraction from Raman spectra by using wavelets

Cited by 9 publications

References 16 publications

Weighted spectral reconstruction method for discrimination of bacterial species with low signal-to-noise ratio Raman measurements

Weighted spectral reconstruction method for discrimination of bacterial species with low signal-to-noise ratio Raman measurements

Raman spectroscopy and PCA-SVM as a non-invasive diagnostic tool to identify and classify qualitatively glycated hemoglobin levels in vivo

Novel Assessment of Urinary Albumin Excretion in Type 2 Diabetes Patients by Raman Spectroscopy

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