A Two-Dimensionally Coincident Second Difference Cosmic Ray Spike Removal Method for the Fully Automated Processing of Raman Spectra

Charge-coupled device detectors are vulnerable to cosmic rays that can contaminate Raman spectra with positive going spikes. Because spikes can adversely affect spectral processing and data analyses, they must be removed. Although both hardware-based and software-based spike removal methods exist, they typically require parameter and threshold specification dependent on well-considered user input. Here, we present a fully automated spike removal algorithm that proceeds without requiring user input. It is minimally dependent on sample attributes, and those that are required (e.g., standard deviation of spectral noise) can be determined with other fully automated procedures. At the core of the method is the identification and location of spikes with coincident second derivatives along both the spectral and spatiotemporal dimensions of two-dimensional datasets. The method can be applied to spectra that are relatively inhomogeneous because it provides fairly effective and selective targeting of spikes resulting in minimal distortion of spectra. Relatively effective spike removal obtained with full automation could provide substantial benefits to users where large numbers of spectra must be processed.

[1]  Joel M. Harris,et al.  Polynomial filters for data sets with outlying or missing observations: application to charge-coupled-device-detected Raman spectra contaminated by cosmic rays , 1990 .

[2]  Yukihiro Ozaki,et al.  Practical Algorithm for Reducing Convex Spike Noises on a Spectrum , 2003, Applied spectroscopy.

[3]  Ronald A. Li,et al.  Label-free separation of human embryonic stem cells and their cardiac derivatives using Raman spectroscopy. , 2008, Analytical chemistry.

[4]  M. Diem,et al.  Spectroscopy , 2007, Acta Neuropsychiatrica.

[5]  W. Hill,et al.  Spike-correction of weak signals from charge-coupled devices and its application to Raman spectroscopy , 1992 .

[6]  H. G. Schulze,et al.  A Small-Window Moving Average-Based Fully Automated Baseline Estimation Method for Raman Spectra , 2012, Applied spectroscopy.

[7]  Jerilyn A. Timlin,et al.  Preprocessing Strategies to Improve MCR Analyses of Hyperspectral Images. , 2012 .

[8]  Francis W. L. Esmonde-White,et al.  Minor Distortions with Major Consequences: Correcting Distortions in Imaging Spectrographs , 2011, Applied spectroscopy.

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

[10]  Hideo Takeuchi,et al.  Simple and Efficient Method to Eliminate Spike Noise from Spectra Recorded on Charge-Coupled Device Detectors , 1993 .

[11]  Sheng Li,et al.  An Improved Algorithm to Remove Cosmic Spikes in Raman Spectra for Online Monitoring , 2011, Applied spectroscopy.

[12]  H. Georg Schulze,et al.  Raman Microscopy-Based Cytochemical Investigations of Potential Niche-Forming Inhomogeneities Present in Human Embryonic Stem Cell Colonies , 2011, Applied spectroscopy.

[13]  H. G. Schulze,et al.  In situ analysis of living embryonic stem cells by coherent anti-stokes Raman microscopy. , 2007, Analytical chemistry.

[14]  Ute B. Cappel,et al.  Removing Cosmic Ray Features from Raman Map Data by a Refined Nearest Neighbor Comparison Method as a Precursor for Chemometric Analysis , 2010, Applied spectroscopy.

[15]  Jun Zhao,et al.  Image Curvature Correction and Cosmic Removal for High-Throughput Dispersive Raman Spectroscopy , 2003, Applied spectroscopy.

[16]  A.J.P. Theuwissen Influence of Terrestrial Cosmic Rays on the Reliability of CCD Image Sensors—Part 1: Experiments at Room Temperature , 2007, IEEE Transactions on Electron Devices.

[17]  H. Georg Schulze,et al.  Evidence of marked glycogen variations in the characteristic Raman signatures of human embryonic stem cells , 2011 .

[18]  Gavin Jell,et al.  Non‐invasive analysis of cell cycle dynamics in single living cells with Raman micro‐spectroscopy , 2008, Journal of cellular biochemistry.

[19]  J. Mourant,et al.  Raman spectroscopic characterization of necrotic cell death. , 2008, Journal of biomedical optics.

[20]  Max Diem,et al.  Raman and Infrared Microspectral Imaging of Mitotic Cells , 2006, Applied spectroscopy.

[21]  Michael D. Morris,et al.  Identification of Outliers in Hyperspectral Raman Image Data by Nearest Neighbor Comparison , 2002 .

[22]  Dor Ben-Amotz,et al.  Single Scan Cosmic Spike Removal Using the Upper Bound Spectrum Method , 2003, Applied spectroscopy.

[23]  Wee Chew,et al.  Information‐theoretic chemometric analyses of Raman data for chemical reaction studies , 2011 .

[24]  H Georg Schulze,et al.  Automated Estimation of White Gaussian Noise Level in a Spectrum with or without Spike Noise Using a Spectral Shifting Technique , 2006, Applied spectroscopy.

[25]  Lin Zhang,et al.  A Practical Algorithm to Remove Cosmic Spikes in Raman Imaging Data for Pharmaceutical Applications , 2007, Applied spectroscopy.

[26]  Christoph Krafft,et al.  Studies on stress-induced changes at the subcellular level by Raman microspectroscopic mapping. , 2006, Analytical chemistry.