Abstract

Raman spectroscopy is a powerful non-destructive technique for qualitatively and quantitatively characterizing materials. However, noise often obscures interesting Raman peaks due to the inherently weak Raman signal, especially in biological samples. In this study, we develop a method based on spectral reconstruction to recover Raman spectra with low signal-to-noise ratio (SNR). The synthesis of narrow-band measurements from low-SNR Raman spectra eliminates the effect of noise by integrating the Raman signal along the wavenumber dimension, which is followed by spectral reconstruction based on Wiener estimation to recover the Raman spectrum with high spectral resolution. Non-negative principal components based filters are used in the synthesis to ensure that most variance contained in the original Raman measurements are retained. A total of 25 agar phantoms and 20 bacteria samples were measured and data were used to validate our method. Four commonly used de-noising methods in Raman spectroscopy, i.e. Savitzky-Golay (SG) algorithm, finite impulse response (FIR) filtration, wavelet transform and factor analysis, were also evaluated on the same set of data in addition to the proposed method for comparison. The proposed method showed the superior accuracy in the recovery of Raman spectra from measurements with extremely low SNR, compared with the four commonly used de-noising methods.

© 2014 Optical Society of America

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[CrossRef]

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[CrossRef]

2012 (3)

S. Chen, Q. Liu, “Modified Wiener estimation of diffuse reflectance spectra from RGB values by the synthesis of new colors for tissue measurements,” J. Biomed. Opt. 17(3), 030501 (2012).
[CrossRef] [PubMed]

J. Lin, R. Chen, S. Feng, J. Pan, B. Li, G. Chen, S. Lin, C. Li, L. Sun, Z. Huang, H. Zeng, “Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection,” J. Raman Spectrosc. 43(4), 497–502 (2012).
[CrossRef]

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[CrossRef] [PubMed]

2011 (3)

M. Villarroel, P. Barreiro, P. Kettlewell, M. Farish, M. Mitchell, “Time derivatives in air temperature and enthalpy as non-invasive welfare indicators during long distance animal transport,” Biosystems Eng. 110(3), 253–260 (2011).
[CrossRef]

J. Palacký, P. Mojzeš, J. Bok, “SVD-based method for intensity normalization, background correction and solvent subtraction in Raman spectroscopy exploiting the properties of water stretching vibrations,” J. Raman Spectrosc. 42(7), 1528–1539 (2011).
[CrossRef]

D. Chen, Z. Chen, E. Grant, “Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry,” Anal. Bioanal. Chem. 400(2), 625–634 (2011).
[CrossRef] [PubMed]

2010 (2)

2009 (1)

B. Hu, Q. Li, A. Smith, “Noise reduction of hyperspectral data using singular spectral analysis,” Int. J. Remote Sens. 30(9), 2277–2296 (2009).
[CrossRef]

2008 (3)

2007 (1)

M. Člupek, P. Matějka, K. Volka, “Noise reduction in Raman spectra: Finite impulse response filtration versus Savitzky–Golay smoothing,” J. Raman Spectrosc. 38(9), 1174–1179 (2007).
[CrossRef]

2006 (1)

2005 (1)

P. M. Ramos, I. Ruisánchez, “Noise and background removal in Raman spectra of ancient pigments using wavelet transform,” J. Raman Spectrosc. 36(9), 848–856 (2005).
[CrossRef]

2003 (1)

Z. Huang, A. McWilliams, H. Lui, D. I. McLean, S. Lam, H. Zeng, “Near-infrared Raman spectroscopy for optical diagnosis of lung cancer,” Int. J. Cancer 107(6), 1047–1052 (2003).
[CrossRef] [PubMed]

2002 (1)

2001 (2)

J. Khan, J. S. Wei, M. Ringnér, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nat. Med. 7(6), 673–679 (2001).
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F. Ehrentreich, L. Sümmchen, “Spike Removal and Denoising of Raman Spectra by Wavelet Transform Methods,” Anal. Chem. 73(17), 4364–4373 (2001).
[CrossRef] [PubMed]

2000 (1)

1999 (1)

J. Hanuš, K. Chmelová, J. Štěpánek, P. Y. Turpin, J. Bok, I. Rosenberg, Z. Točík, “Raman spectroscopic study of triplex-like complexes of polyuridylic acid with the isopolar, non-isosteric phosphonate analogues of diadenosine monophosphate,” J. Raman Spectrosc. 30(8), 667–676 (1999).
[CrossRef]

1996 (1)

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1989 (1)

Antonescu, C. R.

J. Khan, J. S. Wei, M. Ringnér, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nat. Med. 7(6), 673–679 (2001).
[CrossRef] [PubMed]

Bansal, V.

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[CrossRef]

Berthold, F.

J. Khan, J. S. Wei, M. Ringnér, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nat. Med. 7(6), 673–679 (2001).
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Bhargava, S. K.

A. E. Kandjani, M. J. Griffin, R. Ramanathan, S. J. Griffin, S. K. Bhargava, V. Bansal, “A new paradigm for signal processing of Raman spectra using a smoothing free algorithm: Coupling continuous wavelet transform with signal removal method,” J. Raman Spectrosc. 44(4), 608–621 (2013).
[CrossRef]

Bok, J.

J. Palacký, P. Mojzeš, J. Bok, “SVD-based method for intensity normalization, background correction and solvent subtraction in Raman spectroscopy exploiting the properties of water stretching vibrations,” J. Raman Spectrosc. 42(7), 1528–1539 (2011).
[CrossRef]

J. Hanuš, K. Chmelová, J. Štěpánek, P. Y. Turpin, J. Bok, I. Rosenberg, Z. Točík, “Raman spectroscopic study of triplex-like complexes of polyuridylic acid with the isopolar, non-isosteric phosphonate analogues of diadenosine monophosphate,” J. Raman Spectrosc. 30(8), 667–676 (1999).
[CrossRef]

Bowling, R. J.

Chen, D.

D. Chen, Z. Chen, E. Grant, “Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry,” Anal. Bioanal. Chem. 400(2), 625–634 (2011).
[CrossRef] [PubMed]

Chen, G.

Y. Li, J. Pan, G. Chen, C. Li, S. Lin, Y. Shao, S. Feng, Z. Huang, S. Xie, H. Zeng, R. Chen, “Micro-Raman spectroscopy study of cancerous and normal nasopharyngeal tissues,” J. Biomed. Opt. 18(2), 027003 (2013).
[CrossRef] [PubMed]

S. Feng, J. Lin, Z. Huang, G. Chen, W. Chen, Y. Wang, R. Chen, H. Zeng, “Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis,” Appl. Phys. Lett. 102(4), 043702 (2013).
[CrossRef]

J. Lin, R. Chen, S. Feng, J. Pan, B. Li, G. Chen, S. Lin, C. Li, L. Sun, Z. Huang, H. Zeng, “Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection,” J. Raman Spectrosc. 43(4), 497–502 (2012).
[CrossRef]

Chen, R.

Y. Li, J. Pan, G. Chen, C. Li, S. Lin, Y. Shao, S. Feng, Z. Huang, S. Xie, H. Zeng, R. Chen, “Micro-Raman spectroscopy study of cancerous and normal nasopharyngeal tissues,” J. Biomed. Opt. 18(2), 027003 (2013).
[CrossRef] [PubMed]

S. Feng, J. Lin, Z. Huang, G. Chen, W. Chen, Y. Wang, R. Chen, H. Zeng, “Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis,” Appl. Phys. Lett. 102(4), 043702 (2013).
[CrossRef]

J. Lin, R. Chen, S. Feng, J. Pan, B. Li, G. Chen, S. Lin, C. Li, L. Sun, Z. Huang, H. Zeng, “Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection,” J. Raman Spectrosc. 43(4), 497–502 (2012).
[CrossRef]

Chen, S.

S. Chen, Y. H. Ong, Q. Liu, “Fast reconstruction of Raman spectra from narrow-band measurements based on Wiener estimation,” J Raman Spectrosc. 44(6), 875–881 (2013).
[CrossRef]

S. Chen, Q. Liu, “Modified Wiener estimation of diffuse reflectance spectra from RGB values by the synthesis of new colors for tissue measurements,” J. Biomed. Opt. 17(3), 030501 (2012).
[CrossRef] [PubMed]

Chen, W.

S. Feng, J. Lin, Z. Huang, G. Chen, W. Chen, Y. Wang, R. Chen, H. Zeng, “Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis,” Appl. Phys. Lett. 102(4), 043702 (2013).
[CrossRef]

Chen, Z.

D. Chen, Z. Chen, E. Grant, “Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry,” Anal. Bioanal. Chem. 400(2), 625–634 (2011).
[CrossRef] [PubMed]

Chmelová, K.

J. Hanuš, K. Chmelová, J. Štěpánek, P. Y. Turpin, J. Bok, I. Rosenberg, Z. Točík, “Raman spectroscopic study of triplex-like complexes of polyuridylic acid with the isopolar, non-isosteric phosphonate analogues of diadenosine monophosphate,” J. Raman Spectrosc. 30(8), 667–676 (1999).
[CrossRef]

Clupek, M.

M. Člupek, P. Matějka, K. Volka, “Noise reduction in Raman spectra: Finite impulse response filtration versus Savitzky–Golay smoothing,” J. Raman Spectrosc. 38(9), 1174–1179 (2007).
[CrossRef]

Dai, D. Q.

Ehrentreich, F.

F. Ehrentreich, L. Sümmchen, “Spike Removal and Denoising of Raman Spectra by Wavelet Transform Methods,” Anal. Chem. 73(17), 4364–4373 (2001).
[CrossRef] [PubMed]

Farish, M.

M. Villarroel, P. Barreiro, P. Kettlewell, M. Farish, M. Mitchell, “Time derivatives in air temperature and enthalpy as non-invasive welfare indicators during long distance animal transport,” Biosystems Eng. 110(3), 253–260 (2011).
[CrossRef]

Feng, S.

S. Feng, J. Lin, Z. Huang, G. Chen, W. Chen, Y. Wang, R. Chen, H. Zeng, “Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis,” Appl. Phys. Lett. 102(4), 043702 (2013).
[CrossRef]

Y. Li, J. Pan, G. Chen, C. Li, S. Lin, Y. Shao, S. Feng, Z. Huang, S. Xie, H. Zeng, R. Chen, “Micro-Raman spectroscopy study of cancerous and normal nasopharyngeal tissues,” J. Biomed. Opt. 18(2), 027003 (2013).
[CrossRef] [PubMed]

J. Lin, R. Chen, S. Feng, J. Pan, B. Li, G. Chen, S. Lin, C. Li, L. Sun, Z. Huang, H. Zeng, “Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection,” J. Raman Spectrosc. 43(4), 497–502 (2012).
[CrossRef]

Foist, R. B.

Gambhir, S. S.

D. Van de Sompel, E. Garai, C. Zavaleta, S. S. Gambhir, “A comparison of noise models in a hybrid reference spectrum and principal components analysis algorithm for Raman spectroscopy,” J. Raman Spectrosc. 44(6), 841–856 (2013).
[CrossRef]

Garai, E.

D. Van de Sompel, E. Garai, C. Zavaleta, S. S. Gambhir, “A comparison of noise models in a hybrid reference spectrum and principal components analysis algorithm for Raman spectroscopy,” J. Raman Spectrosc. 44(6), 841–856 (2013).
[CrossRef]

Gnyba, M.

A. Kwiatkowski, M. Gnyba, J. Smulko, P. Wierzba, “Algorithms of chemicals detection using raman spectra,” Metrol. Meas. Syst. 17(4), 549–559 (2010).
[CrossRef]

Grant, E.

D. Chen, Z. Chen, E. Grant, “Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry,” Anal. Bioanal. Chem. 400(2), 625–634 (2011).
[CrossRef] [PubMed]

Griffin, M. J.

A. E. Kandjani, M. J. Griffin, R. Ramanathan, S. J. Griffin, S. K. Bhargava, V. Bansal, “A new paradigm for signal processing of Raman spectra using a smoothing free algorithm: Coupling continuous wavelet transform with signal removal method,” J. Raman Spectrosc. 44(4), 608–621 (2013).
[CrossRef]

Griffin, S. J.

A. E. Kandjani, M. J. Griffin, R. Ramanathan, S. J. Griffin, S. K. Bhargava, V. Bansal, “A new paradigm for signal processing of Raman spectra using a smoothing free algorithm: Coupling continuous wavelet transform with signal removal method,” J. Raman Spectrosc. 44(4), 608–621 (2013).
[CrossRef]

Haneishi, H.

Hanuš, J.

J. Hanuš, K. Chmelová, J. Štěpánek, P. Y. Turpin, J. Bok, I. Rosenberg, Z. Točík, “Raman spectroscopic study of triplex-like complexes of polyuridylic acid with the isopolar, non-isosteric phosphonate analogues of diadenosine monophosphate,” J. Raman Spectrosc. 30(8), 667–676 (1999).
[CrossRef]

Harrell, F. E.

F. E. Harrell, K. L. Lee, D. B. Mark, “Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors,” Stat. Med. 15(4), 361–387 (1996).
[CrossRef] [PubMed]

Hasegawa, T.

Hosoi, A.

Hu, B.

B. Hu, Q. Li, A. Smith, “Noise reduction of hyperspectral data using singular spectral analysis,” Int. J. Remote Sens. 30(9), 2277–2296 (2009).
[CrossRef]

Huang, Z.

Y. Li, J. Pan, G. Chen, C. Li, S. Lin, Y. Shao, S. Feng, Z. Huang, S. Xie, H. Zeng, R. Chen, “Micro-Raman spectroscopy study of cancerous and normal nasopharyngeal tissues,” J. Biomed. Opt. 18(2), 027003 (2013).
[CrossRef] [PubMed]

S. Feng, J. Lin, Z. Huang, G. Chen, W. Chen, Y. Wang, R. Chen, H. Zeng, “Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis,” Appl. Phys. Lett. 102(4), 043702 (2013).
[CrossRef]

J. Lin, R. Chen, S. Feng, J. Pan, B. Li, G. Chen, S. Lin, C. Li, L. Sun, Z. Huang, H. Zeng, “Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection,” J. Raman Spectrosc. 43(4), 497–502 (2012).
[CrossRef]

Z. Huang, A. McWilliams, H. Lui, D. I. McLean, S. Lam, H. Zeng, “Near-infrared Raman spectroscopy for optical diagnosis of lung cancer,” Int. J. Cancer 107(6), 1047–1052 (2003).
[CrossRef] [PubMed]

Ivanov, A.

Jirasek, A.

Kandjani, A. E.

A. E. Kandjani, M. J. Griffin, R. Ramanathan, S. J. Griffin, S. K. Bhargava, V. Bansal, “A new paradigm for signal processing of Raman spectra using a smoothing free algorithm: Coupling continuous wavelet transform with signal removal method,” J. Raman Spectrosc. 44(4), 608–621 (2013).
[CrossRef]

Kettlewell, P.

M. Villarroel, P. Barreiro, P. Kettlewell, M. Farish, M. Mitchell, “Time derivatives in air temperature and enthalpy as non-invasive welfare indicators during long distance animal transport,” Biosystems Eng. 110(3), 253–260 (2011).
[CrossRef]

Khan, J.

J. Khan, J. S. Wei, M. Ringnér, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nat. Med. 7(6), 673–679 (2001).
[CrossRef] [PubMed]

Kwiatkowski, A.

A. Kwiatkowski, M. Gnyba, J. Smulko, P. Wierzba, “Algorithms of chemicals detection using raman spectra,” Metrol. Meas. Syst. 17(4), 549–559 (2010).
[CrossRef]

Ladanyi, M.

J. Khan, J. S. Wei, M. Ringnér, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nat. Med. 7(6), 673–679 (2001).
[CrossRef] [PubMed]

Lam, S.

Z. Huang, A. McWilliams, H. Lui, D. I. McLean, S. Lam, H. Zeng, “Near-infrared Raman spectroscopy for optical diagnosis of lung cancer,” Int. J. Cancer 107(6), 1047–1052 (2003).
[CrossRef] [PubMed]

Lee, K. L.

F. E. Harrell, K. L. Lee, D. B. Mark, “Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors,” Stat. Med. 15(4), 361–387 (1996).
[CrossRef] [PubMed]

Li, B.

J. Lin, R. Chen, S. Feng, J. Pan, B. Li, G. Chen, S. Lin, C. Li, L. Sun, Z. Huang, H. Zeng, “Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection,” J. Raman Spectrosc. 43(4), 497–502 (2012).
[CrossRef]

Li, C.

Y. Li, J. Pan, G. Chen, C. Li, S. Lin, Y. Shao, S. Feng, Z. Huang, S. Xie, H. Zeng, R. Chen, “Micro-Raman spectroscopy study of cancerous and normal nasopharyngeal tissues,” J. Biomed. Opt. 18(2), 027003 (2013).
[CrossRef] [PubMed]

J. Lin, R. Chen, S. Feng, J. Pan, B. Li, G. Chen, S. Lin, C. Li, L. Sun, Z. Huang, H. Zeng, “Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection,” J. Raman Spectrosc. 43(4), 497–502 (2012).
[CrossRef]

Li, Q.

B. Hu, Q. Li, A. Smith, “Noise reduction of hyperspectral data using singular spectral analysis,” Int. J. Remote Sens. 30(9), 2277–2296 (2009).
[CrossRef]

Li, Y.

Y. Li, J. Pan, G. Chen, C. Li, S. Lin, Y. Shao, S. Feng, Z. Huang, S. Xie, H. Zeng, R. Chen, “Micro-Raman spectroscopy study of cancerous and normal nasopharyngeal tissues,” J. Biomed. Opt. 18(2), 027003 (2013).
[CrossRef] [PubMed]

Lim, M.

Lin, J.

S. Feng, J. Lin, Z. Huang, G. Chen, W. Chen, Y. Wang, R. Chen, H. Zeng, “Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis,” Appl. Phys. Lett. 102(4), 043702 (2013).
[CrossRef]

J. Lin, R. Chen, S. Feng, J. Pan, B. Li, G. Chen, S. Lin, C. Li, L. Sun, Z. Huang, H. Zeng, “Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection,” J. Raman Spectrosc. 43(4), 497–502 (2012).
[CrossRef]

Lin, S.

Y. Li, J. Pan, G. Chen, C. Li, S. Lin, Y. Shao, S. Feng, Z. Huang, S. Xie, H. Zeng, R. Chen, “Micro-Raman spectroscopy study of cancerous and normal nasopharyngeal tissues,” J. Biomed. Opt. 18(2), 027003 (2013).
[CrossRef] [PubMed]

J. Lin, R. Chen, S. Feng, J. Pan, B. Li, G. Chen, S. Lin, C. Li, L. Sun, Z. Huang, H. Zeng, “Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection,” J. Raman Spectrosc. 43(4), 497–502 (2012).
[CrossRef]

Liu, Q.

S. Chen, Y. H. Ong, Q. Liu, “Fast reconstruction of Raman spectra from narrow-band measurements based on Wiener estimation,” J Raman Spectrosc. 44(6), 875–881 (2013).
[CrossRef]

Y. H. Ong, M. Lim, Q. Liu, “Comparison of principal component analysis and biochemical component analysis in Raman spectroscopy for the discrimination of apoptosis and necrosis in K562 leukemia cells,” Opt. Express 20(20), 22158–22171 (2012).
[CrossRef] [PubMed]

S. Chen, Q. Liu, “Modified Wiener estimation of diffuse reflectance spectra from RGB values by the synthesis of new colors for tissue measurements,” J. Biomed. Opt. 17(3), 030501 (2012).
[CrossRef] [PubMed]

Lui, H.

Z. Huang, A. McWilliams, H. Lui, D. I. McLean, S. Lam, H. Zeng, “Near-infrared Raman spectroscopy for optical diagnosis of lung cancer,” Int. J. Cancer 107(6), 1047–1052 (2003).
[CrossRef] [PubMed]

Mark, D. B.

F. E. Harrell, K. L. Lee, D. B. Mark, “Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors,” Stat. Med. 15(4), 361–387 (1996).
[CrossRef] [PubMed]

Matejka, P.

M. Člupek, P. Matějka, K. Volka, “Noise reduction in Raman spectra: Finite impulse response filtration versus Savitzky–Golay smoothing,” J. Raman Spectrosc. 38(9), 1174–1179 (2007).
[CrossRef]

McCreery, R. L.

McLean, D. I.

Z. Huang, A. McWilliams, H. Lui, D. I. McLean, S. Lam, H. Zeng, “Near-infrared Raman spectroscopy for optical diagnosis of lung cancer,” Int. J. Cancer 107(6), 1047–1052 (2003).
[CrossRef] [PubMed]

McWilliams, A.

Z. Huang, A. McWilliams, H. Lui, D. I. McLean, S. Lam, H. Zeng, “Near-infrared Raman spectroscopy for optical diagnosis of lung cancer,” Int. J. Cancer 107(6), 1047–1052 (2003).
[CrossRef] [PubMed]

Meltzer, P. S.

J. Khan, J. S. Wei, M. Ringnér, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nat. Med. 7(6), 673–679 (2001).
[CrossRef] [PubMed]

Mitchell, M.

M. Villarroel, P. Barreiro, P. Kettlewell, M. Farish, M. Mitchell, “Time derivatives in air temperature and enthalpy as non-invasive welfare indicators during long distance animal transport,” Biosystems Eng. 110(3), 253–260 (2011).
[CrossRef]

Miyake, Y.

Mojzeš, P.

J. Palacký, P. Mojzeš, J. Bok, “SVD-based method for intensity normalization, background correction and solvent subtraction in Raman spectroscopy exploiting the properties of water stretching vibrations,” J. Raman Spectrosc. 42(7), 1528–1539 (2011).
[CrossRef]

Ong, Y. H.

Palacký, J.

J. Palacký, P. Mojzeš, J. Bok, “SVD-based method for intensity normalization, background correction and solvent subtraction in Raman spectroscopy exploiting the properties of water stretching vibrations,” J. Raman Spectrosc. 42(7), 1528–1539 (2011).
[CrossRef]

Pan, J.

Y. Li, J. Pan, G. Chen, C. Li, S. Lin, Y. Shao, S. Feng, Z. Huang, S. Xie, H. Zeng, R. Chen, “Micro-Raman spectroscopy study of cancerous and normal nasopharyngeal tissues,” J. Biomed. Opt. 18(2), 027003 (2013).
[CrossRef] [PubMed]

J. Lin, R. Chen, S. Feng, J. Pan, B. Li, G. Chen, S. Lin, C. Li, L. Sun, Z. Huang, H. Zeng, “Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection,” J. Raman Spectrosc. 43(4), 497–502 (2012).
[CrossRef]

Peterson, C.

J. Khan, J. S. Wei, M. Ringnér, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nat. Med. 7(6), 673–679 (2001).
[CrossRef] [PubMed]

Piché, R.

Ramanathan, R.

A. E. Kandjani, M. J. Griffin, R. Ramanathan, S. J. Griffin, S. K. Bhargava, V. Bansal, “A new paradigm for signal processing of Raman spectra using a smoothing free algorithm: Coupling continuous wavelet transform with signal removal method,” J. Raman Spectrosc. 44(4), 608–621 (2013).
[CrossRef]

Ramos, P. M.

P. M. Ramos, I. Ruisánchez, “Noise and background removal in Raman spectra of ancient pigments using wavelet transform,” J. Raman Spectrosc. 36(9), 848–856 (2005).
[CrossRef]

Ringnér, M.

J. Khan, J. S. Wei, M. Ringnér, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nat. Med. 7(6), 673–679 (2001).
[CrossRef] [PubMed]

Rosenberg, I.

J. Hanuš, K. Chmelová, J. Štěpánek, P. Y. Turpin, J. Bok, I. Rosenberg, Z. Točík, “Raman spectroscopic study of triplex-like complexes of polyuridylic acid with the isopolar, non-isosteric phosphonate analogues of diadenosine monophosphate,” J. Raman Spectrosc. 30(8), 667–676 (1999).
[CrossRef]

Ruisánchez, I.

P. M. Ramos, I. Ruisánchez, “Noise and background removal in Raman spectra of ancient pigments using wavelet transform,” J. Raman Spectrosc. 36(9), 848–856 (2005).
[CrossRef]

Saal, L. H.

J. Khan, J. S. Wei, M. Ringnér, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nat. Med. 7(6), 673–679 (2001).
[CrossRef] [PubMed]

Schulze, H. G.

Schwab, M.

J. Khan, J. S. Wei, M. Ringnér, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nat. Med. 7(6), 673–679 (2001).
[CrossRef] [PubMed]

Shao, Y.

Y. Li, J. Pan, G. Chen, C. Li, S. Lin, Y. Shao, S. Feng, Z. Huang, S. Xie, H. Zeng, R. Chen, “Micro-Raman spectroscopy study of cancerous and normal nasopharyngeal tissues,” J. Biomed. Opt. 18(2), 027003 (2013).
[CrossRef] [PubMed]

Smith, A.

B. Hu, Q. Li, A. Smith, “Noise reduction of hyperspectral data using singular spectral analysis,” Int. J. Remote Sens. 30(9), 2277–2296 (2009).
[CrossRef]

Smulko, J.

A. Kwiatkowski, M. Gnyba, J. Smulko, P. Wierzba, “Algorithms of chemicals detection using raman spectra,” Metrol. Meas. Syst. 17(4), 549–559 (2010).
[CrossRef]

Spencer, P.

Štepánek, J.

J. Hanuš, K. Chmelová, J. Štěpánek, P. Y. Turpin, J. Bok, I. Rosenberg, Z. Točík, “Raman spectroscopic study of triplex-like complexes of polyuridylic acid with the isopolar, non-isosteric phosphonate analogues of diadenosine monophosphate,” J. Raman Spectrosc. 30(8), 667–676 (1999).
[CrossRef]

Sümmchen, L.

F. Ehrentreich, L. Sümmchen, “Spike Removal and Denoising of Raman Spectra by Wavelet Transform Methods,” Anal. Chem. 73(17), 4364–4373 (2001).
[CrossRef] [PubMed]

Sun, L.

J. Lin, R. Chen, S. Feng, J. Pan, B. Li, G. Chen, S. Lin, C. Li, L. Sun, Z. Huang, H. Zeng, “Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection,” J. Raman Spectrosc. 43(4), 497–502 (2012).
[CrossRef]

Tocík, Z.

J. Hanuš, K. Chmelová, J. Štěpánek, P. Y. Turpin, J. Bok, I. Rosenberg, Z. Točík, “Raman spectroscopic study of triplex-like complexes of polyuridylic acid with the isopolar, non-isosteric phosphonate analogues of diadenosine monophosphate,” J. Raman Spectrosc. 30(8), 667–676 (1999).
[CrossRef]

Tsumura, N.

Turner, R. F.

Turpin, P. Y.

J. Hanuš, K. Chmelová, J. Štěpánek, P. Y. Turpin, J. Bok, I. Rosenberg, Z. Točík, “Raman spectroscopic study of triplex-like complexes of polyuridylic acid with the isopolar, non-isosteric phosphonate analogues of diadenosine monophosphate,” J. Raman Spectrosc. 30(8), 667–676 (1999).
[CrossRef]

Van de Sompel, D.

D. Van de Sompel, E. Garai, C. Zavaleta, S. S. Gambhir, “A comparison of noise models in a hybrid reference spectrum and principal components analysis algorithm for Raman spectroscopy,” J. Raman Spectrosc. 44(6), 841–856 (2013).
[CrossRef]

Villarroel, M.

M. Villarroel, P. Barreiro, P. Kettlewell, M. Farish, M. Mitchell, “Time derivatives in air temperature and enthalpy as non-invasive welfare indicators during long distance animal transport,” Biosystems Eng. 110(3), 253–260 (2011).
[CrossRef]

Volka, K.

M. Člupek, P. Matějka, K. Volka, “Noise reduction in Raman spectra: Finite impulse response filtration versus Savitzky–Golay smoothing,” J. Raman Spectrosc. 38(9), 1174–1179 (2007).
[CrossRef]

Wang, Y.

S. Feng, J. Lin, Z. Huang, G. Chen, W. Chen, Y. Wang, R. Chen, H. Zeng, “Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis,” Appl. Phys. Lett. 102(4), 043702 (2013).
[CrossRef]

Y. P. Wang, Y. Wang, P. Spencer, “Fuzzy clustering of Raman spectral imaging data with a wavelet-based noise-reduction approach,” Appl. Spectrosc. 60(7), 826–832 (2006).
[CrossRef] [PubMed]

Wang, Y. P.

Wei, J. S.

J. Khan, J. S. Wei, M. Ringnér, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nat. Med. 7(6), 673–679 (2001).
[CrossRef] [PubMed]

Westermann, F.

J. Khan, J. S. Wei, M. Ringnér, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nat. Med. 7(6), 673–679 (2001).
[CrossRef] [PubMed]

Wierzba, P.

A. Kwiatkowski, M. Gnyba, J. Smulko, P. Wierzba, “Algorithms of chemicals detection using raman spectra,” Metrol. Meas. Syst. 17(4), 549–559 (2010).
[CrossRef]

Williamson, J. M.

Xie, S.

Y. Li, J. Pan, G. Chen, C. Li, S. Lin, Y. Shao, S. Feng, Z. Huang, S. Xie, H. Zeng, R. Chen, “Micro-Raman spectroscopy study of cancerous and normal nasopharyngeal tissues,” J. Biomed. Opt. 18(2), 027003 (2013).
[CrossRef] [PubMed]

Yokoyama, Y.

Zavaleta, C.

D. Van de Sompel, E. Garai, C. Zavaleta, S. S. Gambhir, “A comparison of noise models in a hybrid reference spectrum and principal components analysis algorithm for Raman spectroscopy,” J. Raman Spectrosc. 44(6), 841–856 (2013).
[CrossRef]

Zeng, H.

S. Feng, J. Lin, Z. Huang, G. Chen, W. Chen, Y. Wang, R. Chen, H. Zeng, “Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis,” Appl. Phys. Lett. 102(4), 043702 (2013).
[CrossRef]

Y. Li, J. Pan, G. Chen, C. Li, S. Lin, Y. Shao, S. Feng, Z. Huang, S. Xie, H. Zeng, R. Chen, “Micro-Raman spectroscopy study of cancerous and normal nasopharyngeal tissues,” J. Biomed. Opt. 18(2), 027003 (2013).
[CrossRef] [PubMed]

J. Lin, R. Chen, S. Feng, J. Pan, B. Li, G. Chen, S. Lin, C. Li, L. Sun, Z. Huang, H. Zeng, “Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection,” J. Raman Spectrosc. 43(4), 497–502 (2012).
[CrossRef]

Z. Huang, A. McWilliams, H. Lui, D. I. McLean, S. Lam, H. Zeng, “Near-infrared Raman spectroscopy for optical diagnosis of lung cancer,” Int. J. Cancer 107(6), 1047–1052 (2003).
[CrossRef] [PubMed]

Zhang, W. F.

Anal. Bioanal. Chem. (1)

D. Chen, Z. Chen, E. Grant, “Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry,” Anal. Bioanal. Chem. 400(2), 625–634 (2011).
[CrossRef] [PubMed]

Anal. Chem. (1)

F. Ehrentreich, L. Sümmchen, “Spike Removal and Denoising of Raman Spectra by Wavelet Transform Methods,” Anal. Chem. 73(17), 4364–4373 (2001).
[CrossRef] [PubMed]

Appl. Opt. (1)

Appl. Phys. Lett. (1)

S. Feng, J. Lin, Z. Huang, G. Chen, W. Chen, Y. Wang, R. Chen, H. Zeng, “Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis,” Appl. Phys. Lett. 102(4), 043702 (2013).
[CrossRef]

Appl. Spectrosc. (5)

Biosystems Eng. (1)

M. Villarroel, P. Barreiro, P. Kettlewell, M. Farish, M. Mitchell, “Time derivatives in air temperature and enthalpy as non-invasive welfare indicators during long distance animal transport,” Biosystems Eng. 110(3), 253–260 (2011).
[CrossRef]

Int. J. Cancer (1)

Z. Huang, A. McWilliams, H. Lui, D. I. McLean, S. Lam, H. Zeng, “Near-infrared Raman spectroscopy for optical diagnosis of lung cancer,” Int. J. Cancer 107(6), 1047–1052 (2003).
[CrossRef] [PubMed]

Int. J. Remote Sens. (1)

B. Hu, Q. Li, A. Smith, “Noise reduction of hyperspectral data using singular spectral analysis,” Int. J. Remote Sens. 30(9), 2277–2296 (2009).
[CrossRef]

J Raman Spectrosc. (1)

S. Chen, Y. H. Ong, Q. Liu, “Fast reconstruction of Raman spectra from narrow-band measurements based on Wiener estimation,” J Raman Spectrosc. 44(6), 875–881 (2013).
[CrossRef]

J. Biomed. Opt. (2)

S. Chen, Q. Liu, “Modified Wiener estimation of diffuse reflectance spectra from RGB values by the synthesis of new colors for tissue measurements,” J. Biomed. Opt. 17(3), 030501 (2012).
[CrossRef] [PubMed]

Y. Li, J. Pan, G. Chen, C. Li, S. Lin, Y. Shao, S. Feng, Z. Huang, S. Xie, H. Zeng, R. Chen, “Micro-Raman spectroscopy study of cancerous and normal nasopharyngeal tissues,” J. Biomed. Opt. 18(2), 027003 (2013).
[CrossRef] [PubMed]

J. Opt. Soc. Am. A (2)

J. Raman Spectrosc. (7)

A. E. Kandjani, M. J. Griffin, R. Ramanathan, S. J. Griffin, S. K. Bhargava, V. Bansal, “A new paradigm for signal processing of Raman spectra using a smoothing free algorithm: Coupling continuous wavelet transform with signal removal method,” J. Raman Spectrosc. 44(4), 608–621 (2013).
[CrossRef]

J. Lin, R. Chen, S. Feng, J. Pan, B. Li, G. Chen, S. Lin, C. Li, L. Sun, Z. Huang, H. Zeng, “Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection,” J. Raman Spectrosc. 43(4), 497–502 (2012).
[CrossRef]

M. Člupek, P. Matějka, K. Volka, “Noise reduction in Raman spectra: Finite impulse response filtration versus Savitzky–Golay smoothing,” J. Raman Spectrosc. 38(9), 1174–1179 (2007).
[CrossRef]

P. M. Ramos, I. Ruisánchez, “Noise and background removal in Raman spectra of ancient pigments using wavelet transform,” J. Raman Spectrosc. 36(9), 848–856 (2005).
[CrossRef]

D. Van de Sompel, E. Garai, C. Zavaleta, S. S. Gambhir, “A comparison of noise models in a hybrid reference spectrum and principal components analysis algorithm for Raman spectroscopy,” J. Raman Spectrosc. 44(6), 841–856 (2013).
[CrossRef]

J. Palacký, P. Mojzeš, J. Bok, “SVD-based method for intensity normalization, background correction and solvent subtraction in Raman spectroscopy exploiting the properties of water stretching vibrations,” J. Raman Spectrosc. 42(7), 1528–1539 (2011).
[CrossRef]

J. Hanuš, K. Chmelová, J. Štěpánek, P. Y. Turpin, J. Bok, I. Rosenberg, Z. Točík, “Raman spectroscopic study of triplex-like complexes of polyuridylic acid with the isopolar, non-isosteric phosphonate analogues of diadenosine monophosphate,” J. Raman Spectrosc. 30(8), 667–676 (1999).
[CrossRef]

Metrol. Meas. Syst. (1)

A. Kwiatkowski, M. Gnyba, J. Smulko, P. Wierzba, “Algorithms of chemicals detection using raman spectra,” Metrol. Meas. Syst. 17(4), 549–559 (2010).
[CrossRef]

Nat. Med. (1)

J. Khan, J. S. Wei, M. Ringnér, L. H. Saal, M. Ladanyi, F. Westermann, F. Berthold, M. Schwab, C. R. Antonescu, C. Peterson, P. S. Meltzer, “Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks,” Nat. Med. 7(6), 673–679 (2001).
[CrossRef] [PubMed]

Opt. Express (1)

Stat. Med. (1)

F. E. Harrell, K. L. Lee, D. B. Mark, “Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors,” Stat. Med. 15(4), 361–387 (1996).
[CrossRef] [PubMed]

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L. Birgé and P. Massart, “Birge-Massart strategy,” in Research Papers in Probaility and Statistics (Springer, 1997, 35).

G. Li, “Noise Removal of Raman Spectra Using Interval Thresholding Method.” in Proceeding of IEEE conference on Intelligent Information Technology Application (Shanghai,2008), pp. 535–539.
[CrossRef]

J. S. U. Hjorth, “Cross validation,” in Computer Intensive Statistical Methods: Validation, Model Selection, and Boostrap (Chapman and Hall/CRC, 1993).

P. Martinez, “Bias,” in A Practical Guide to CCD Astronomy (Cambridge University, 1998, 8).

R. L. McCreery, “SNR,” in Raman Spectroscopy for Chemical Analysis (John Wiley & Sons, 2005, 157).

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Figures (4)

Fig. 1
Fig. 1

(a) Raman spectra with low SNR and (b) reference Raman spectra with high SNR measured from 25 phantoms and (c) non-negative PCs based filters’ transmittance spectra

Fig. 2
Fig. 2

(a) Raman spectra with low SNR (b) reference Raman spectra with high SNR measured from bacteria samples and (c) non-negative PCs based filters’ transmittance spectra

Fig. 3
Fig. 3

Comparison of the reference Raman spectra from phantoms and the corresponding spectra recovered from low-SNR Raman measurements using (a) SG algorithm, (b) FIR filtration method, (c) wavelet transform, (d) factor analysis and (e) WE method, in which the relative RMSE is close to the mean value. Fluorescence background has been removed and spectra have been normalized.

Fig. 4
Fig. 4

Comparison of the reference Raman spectra from bacteria samples and the Raman spectra recovered from low-SNR Raman measurements using (a) SG algorithm, (b) FIR filtration method, (c) wavelet transform, (d) factor analysis and (e) WE method, in which the relative RMSE is close to the mean value. Fluorescence background has been removed and spectra have been normalized.

Tables (5)

Tables Icon

Table 1 Comparison in the mean relative RMSE of Raman spectra of phantoms (after fluorescence background removal and normalization) recovered/smoothed from low-SNR Raman measurements using SG algorithm, FIR filtration, wavelet transform, factor analysis and WE method

Tables Icon

Table 2 Comparison in the mean relative RMSE of Raman spectra of bacteria samples (after fluorescence background removal and normalization) recovered/smoothed from low-SNR Raman measurements using SG algorithm, FIR filtration, wavelet transform, factor analysis and WE method

Tables Icon

Table 3 Comparison in the mean relative RMSE of Raman spectra of phantoms (after normalization but without background removal) recovered/smoothed from low-SNR Raman measurements using SG algorithm, FIR filtration, wavelet transform, factor analysis and WE method

Tables Icon

Table 4 Comparison in the mean relative RMSE of Raman spectra of bacteria samples (after normalization but without background removal) recovered/smoothed from low-SNR Raman measurements using SG algorithm, FIR filtration, wavelet transform, factor analysis and WE method

Tables Icon

Table 5 Comparison in the mean relative RMSE of Raman spectra of bacteria samples (after fluorescence background removal and normalization) recovered from low-SNR Raman measurements with different exposure time using SG algorithm, FIR filtration, wavelet transform, factor analysis and WE method

Equations (5)

Equations on this page are rendered with MathJax. Learn more.

W=E( S high C cal T ) [E( C cal C cal T )] 1
C=F S low
S ^ high =W C test
SNR = s σ
RelativeRMSE= [ i=1 N [ R low ( λ i ) R high ( λ i )] 2 N× {max[ R high ( λ i )]} 2 ] 1/2

Metrics