Recovery of Raman spectra with low signal-to-noise ratio using Wiener estimation.

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.

[1]  James M. Williamson,et al.  Near-Infrared Raman Spectroscopy with a 783-nm Diode Laser and CCD Array Detector , 1989 .

[2]  S. Lam,et al.  Near‐infrared Raman spectroscopy for optical diagnosis of lung cancer , 2003, International journal of cancer.

[3]  Da Chen,et al.  Adaptive wavelet transform suppresses background and noise for quantitative analysis by Raman spectrometry , 2011, Analytical and bioanalytical chemistry.

[4]  P. Barreiro,et al.  Time derivatives in air temperature and enthalpy as non-invasive welfare indicators during long distance animal transport , 2011 .

[5]  P. M. Ramos,et al.  Noise and background removal in Raman spectra of ancient pigments using wavelet transform , 2005 .

[6]  Janusz Smulko,et al.  Algorithms of Chemicals Detection Using Raman Spectra , 2010 .

[7]  Quan Liu,et al.  Modified Wiener estimation of diffuse reflectance spectra from RGB values by the synthesis of new colors for tissue measurements. , 2012, Journal of biomedical optics.

[8]  Daniel B. Mark,et al.  TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .

[9]  H. Georg Schulze,et al.  A Matrix-Based Two-Dimensional Regularization Algorithm for Signal-to-Noise Ratio Enhancement of Multidimensional Spectral Data , 2010, Applied spectroscopy.

[10]  Mayasari Lim,et al.  Comparison of principal component analysis and biochemical component analysis in Raman spectroscopy for the discrimination of apoptosis and necrosis in K562 leukemia cells. , 2012, Optics express.

[11]  Baoxin Hu,et al.  Noise reduction of hyperspectral data using singular spectral analysis , 2009 .

[12]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[13]  Pavel Matějka,et al.  Noise reduction in Raman spectra: Finite impulse response filtration versus Savitzky–Golay smoothing , 2007 .

[14]  Rong Chen,et al.  Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection , 2013 .

[15]  Yu-Ping Wang,et al.  Fuzzy Clustering of Raman Spectral Imaging Data with a Wavelet-Based Noise-Reduction Approach , 2006, Applied spectroscopy.

[16]  Andre Ivanov,et al.  Chi-Squared-Based Filters for High-Fidelity Signal-to-Noise Ratio Enhancement of Spectra , 2008, Applied spectroscopy.

[17]  Haishan Zeng,et al.  Micro-Raman spectroscopy study of cancerous and normal nasopharyngeal tissues , 2013, Journal of biomedical optics.

[18]  Yi Hong Ong,et al.  Fast reconstruction of Raman spectra from narrow‐band measurements based on Wiener estimation , 2013 .

[19]  Dominique Van de Sompel,et al.  A comparison of noise models in a hybrid reference spectrum and principal components analysis algorithm for Raman spectroscopy , 2013 .

[20]  Andre Ivanov,et al.  Fully Automated High-Performance Signal-to-Noise Ratio Enhancement Based on an Iterative Three-Point Zero-Order Savitzky—Golay Filter , 2008, Applied spectroscopy.

[21]  J. Bok,et al.  SVD‐based method for intensity normalization, background correction and solvent subtraction in Raman spectroscopy exploiting the properties of water stretching vibrations , 2011 .

[22]  R. Piché.  Nonnegative color spectrum analysis filters from principal component analysis characteristic spectra. , 2002, Journal of the Optical Society of America. A, Optics, image science, and vision.

[23]  F. Harrell,et al.  Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors , 2005 .

[24]  Rong Chen,et al.  Esophageal cancer detection based on tissue surface-enhanced Raman spectroscopy and multivariate analysis , 2013 .

[25]  F Ehrentreich,et al.  Spike removal and denoising of Raman spectra by wavelet transform methods. , 2001, Analytical chemistry.

[26]  Gang Li,et al.  Noise Removal of Raman Spectra Using Interval Thresholding Method , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.

[27]  Suresh K. Bhargava,et al.  A new paradigm for signal processing of Raman spectra using a smoothing free algorithm: Coupling continuous wavelet transform with signal removal method , 2013 .

[28]  Alain Klotz,et al.  A Practical Guide to CCD Astronomy , 1997 .

[29]  Dao-Qing Dai,et al.  Spectral reflectance estimation from camera responses by support vector regression and a composite model. , 2008, Journal of the Optical Society of America. A, Optics, image science, and vision.

[30]  H Haneishi,et al.  System design for accurately estimating the spectral reflectance of art paintings. , 2000, Applied optics.

[31]  J. Bok,et al.  Raman spectroscopic study of triplex‐like complexes of polyuridylic acid with the isopolar, non‐isosteric phosphonate analogues of diadenosine monophosphate , 1999 .

[32]  Zufang Huang,et al.  Surface-enhanced Raman scattering spectroscopy for potential noninvasive nasopharyngeal cancer detection: SERS spectroscopy for potential noninvasive nasopharyngeal cancer detection , 2012 .