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Fluorescence and noise subtraction from Raman spectra by using wavelets

  • This Issue is Dedicated to Memory of Academician Andrey L. Mikaelyan
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Abstract

We develop a technique to remove the fluorescence background in the Raman spectrum. The technique is based in wavelets theory, using symlets and biorthogonals wavelets, allowing us to obtain better accuracy in the determination of spectral peaks. We test the method in Raman spectra from 300–1800 (cm−1) range, of different biological samples, which were excited with a 785 nm laser, and we were able to make assignation of spectral peaks of cow bone, diiodine drug, pig and chicken bone. Our results of peaks assignation are somewhat comparable with those obtained with spectral software, which is specialized to analyze astronomical spectra, that is based in polynomial fitting. However, our method is friendly and easy to apply to Raman spectra.

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References

  1. Mahadevan-Jansen, A. and Richards-Kortum, R., Raman Spectroscopy for Detection of Cáncer and Precancers, J. Biomed. Optics, 1996, vol. 1 pp. 31–70.

    Article  Google Scholar 

  2. Koo, T.W., Berger, A.J., Itzkan, I., Horowitz, G., and Feld, M.S., Reagentless Blood Analysis by Near-Infrared Raman Spectroscopy, Diabetes Technology and Therapeutics, 1999, vol. 1, no. 2, pp. 153–157.

    Article  Google Scholar 

  3. Shih, W.C., Bechtel, K., and Feld, M.S., Intrinsic Raman Spectroscopy for Quantitative Biological Spectroscopy, Part II: Experimental Applications, MS. Optics Express, 2008, vol. 16, no. 17, pp. 12737–12745.

    Google Scholar 

  4. Requena, A.Y. and Zúñiga, J., Espectroscopía, España: Prentice Hall, 2004.

    Google Scholar 

  5. Smith, E. and Dent, G., Modern Raman Spectroscopy: Practical Approach, USA, 2005.

  6. Ke, W. and Wu, J., Comparison of Several Methods of Fluorescence Quenching in Protein Molecules Spectrochim., Acta A 51 L25, 1995.

    Google Scholar 

  7. Greek, L., Shane, L., Schulze, H.G., Blades, M.W., Bree, A.V., Gorzalka, B.B., and Turner, R.F.B., SNR Enhancement and Deconvolution of Raman Spectra Using a Two-Point Entropy Regularization, Appl. Spectrosc., 1995, vol. 49, p. 425.

    Article  Google Scholar 

  8. Craggs, C., Galloway, K.P., and Gardiner, D.J., Maximum Entropy Methods Applied to Simulated and Observed Raman Spectra, Appl. Spectrosc. 1996, vol. 50 p. 43.

    Article  Google Scholar 

  9. Venkatakrishna, K., Kurien, J., Pai, K.M., Valiathan, M., Kumar, N.N., Krishna, C.M., Ullas, G., and Kartha, V.B., Optical Pathology of Oral Tissue: a Raman Spectroscopy Diagnostic Method, Curr. Sci., 2001, vol. 80, p. 665.

    Google Scholar 

  10. Hasegawa, T., Nishijo, J., and Umemura, J., Separation of Raman Spectra from Fluorescence Emission by Principal Component Analysis, Chem. Phys. Lett., 2000, vol. 317, p. 642.

    Article  Google Scholar 

  11. Schulze, G., Jirasek, A., Yu, M.M.L., Lim, A., Turner, R.F.B., and Blades, M.W., Investigation of Selected Baseline Removal Techniques as Candidates for Automated Implementation, Appl. Spectrosc., 2005, vol. 56, p. 54.

    Google Scholar 

  12. Cai, W., Wang, L., Pan, Z., Zuo, J., Xu, C., and Shao, X., Application of the Wavelet Transform Method in Quantitative Analysis of Raman Spectra, J. Raman Spectrosc., 2001, vol. 32, p. 207.

    Article  Google Scholar 

  13. Cai, T.T., Zhang, D., and Ben-Amotz, D., Enhanced Chemical Classification of Raman Images Using Multi-resolution Wavelet Transformation, Appl. Spectrosc., 2001, vol. 55, p. 1124.

    Article  Google Scholar 

  14. Lieber, C.A. and Mahadevan-Jansen, A., Automated Method for Subtraction of Fluorescence from Biological Raman Spectra, Applied Spectroscopy, 2003, vol. 7, no. 11.

  15. Camerlingo, C., Zenone, F., Gaeta, G.M., Riccio, R., and Lepore, M., Wavelet Data Processing of Micro-Raman Spectra of Biological Samples, Meas. Sci. Technol., 2006, vol. 17, pp. 298–303.

    Article  Google Scholar 

  16. Mallat, S., Wavelets Tours of Signal Processing, Academic press, 1999.

  17. Walczak, B., Wavelets in Chemistry, Amsterdam: Elsevier, 2000.

    Google Scholar 

  18. Misiti, M., Misiti, Y., Oppenheim, G., and Poggi, J.-M., Wavelet Toolbox User’s Guide, Massachusetts: Math-Works, 2000.

    Google Scholar 

  19. Alsberg, B.K., Woodward, and Kell, D.B., An Introduction to Wavelet Transform for Chemometricians, Chemom. Intell. Lab. Syst., 1997, vol. 37.

  20. Cai, T.T., On Block Thresholding in Wavelet Regression: Adaptivity, Block Size, and Threshold Level, Stat. Sin., 2002, vol. 12, pp. 1241–1273.

    MATH  Google Scholar 

  21. Swee, E.G.T. and Elangovan, S., Applications of Symlets for Denoising and Load Forecasting (Proc. of the IEEE Signal Processing Workshop On), 1999.

  22. Cohen, A., Daubechies, I., and Feauveau, J., Biorthogonal Bases of Compactly Supported Wavelets, Commun. Pure Appl. Math., 1992, vol. 45, pp. 485–493.

    Article  MATH  MathSciNet  Google Scholar 

  23. Daubechies, I., Ten Lectures on Wavelets (CBMS-NSF Series in Applied Mathematics, vol. 61, Philadelphia, PA: SIAM, 1992.

    MATH  Google Scholar 

  24. http://www.labcognition.com/.

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Authors and Affiliations

  1. Instituto Nacional de Astrofíisica, Óptica y Electrónica, Luis Enrique Erro no. 1, Tonantzintla, Puebla, México

    A. E. Villanueva-Luna, J. Castro-Ramos, S. Vazquez-Montiel & J. A. Delgado-Atencio

  2. Universidad Autónoma del Carmen. C. 56, no. 4 por Avenida Concordia Cd. del Carmen, Campeche, México

    A. Flores-Gil

  3. Facultad Experimental de Ciencia y Tecnología, Departamento de Física, Universidad de Carabobo, C.P. 2005, Valencia, Venezuela

    E. E. Orozco-Guillen

Authors
  1. A. E. Villanueva-Luna
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  2. J. Castro-Ramos
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  3. S. Vazquez-Montiel
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  4. A. Flores-Gil
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  5. J. A. Delgado-Atencio
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  6. E. E. Orozco-Guillen
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Corresponding author

Correspondence to A. E. Villanueva-Luna.

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Villanueva-Luna, A.E., Castro-Ramos, J., Vazquez-Montiel, S. et al. Fluorescence and noise subtraction from Raman spectra by using wavelets. Opt. Mem. Neural Networks 19, 310–317 (2010). https://doi.org/10.3103/S1060992X10040089

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  • DOI: https://doi.org/10.3103/S1060992X10040089

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