An Algorithm for the Removal of Cosmic Ray Artifacts in Spectral Data Sets

Abstract

Cosmic ray artifacts may be present in all photo-electric readout systems. In spectroscopy, they present as random unidirectional sharp spikes that distort spectra and may have an affect on post-processing, possibly affecting the results of multivariate statistical classification. A number of methods have previously been proposed to remove cosmic ray artifacts from spectra but the goal of removing the artifacts while making no other change to the underlying spectrum is challenging. One of the most successful and commonly applied methods for the removal of comic ray artifacts involves the capture of two sequential spectra that are compared in order to identify spikes. The disadvantage of this approach is that at least two recordings are necessary, which may be problematic for dynamically changing spectra, and which can reduce the signal-to-noise (S/N) ratio when compared with a single recording of equivalent duration due to the inclusion of two instances of read noise. In this paper, a cosmic ray artefact removal algorithm is proposed that works in a similar way to the double acquisition method but requires only a single capture, so long as a data set of similar spectra is available. The method employs normalized covariance in order to identify a similar spectrum in the data set, from which a direct comparison reveals the presence of cosmic ray artifacts, which are then replaced with the corresponding values from the matching spectrum. The advantage of the proposed method over the double acquisition method is investigated in the context of the S/N ratio and is applied to various data sets of Raman spectra recorded from biological cells.

Automated Raman Micro-Spectroscopy of Epithelial Cell Nuclei for High-Throughput Classification

“…Raw spectra were first input to a cosmic ray removal algorithm [ 34 ]; this algorithm is capable of removing cosmic rays from a spectrum by identifying a closely matching spectrum in the dataset, and replacing each cosmic ray with the corresponding wave-number intensity values in that matching spectrum. It is, therefore, not necessary to apply the commonly used double acquisition cosmic ray removal method to obtain a closely matching spectrum [ 35 ], which was found to be problematic for the high-speed spectral acquisition applied in the automation process.…”

Automated Raman micro-spectroscopy of epithelial cells for the high-throughput classification

“…Raw spectra were first input to a cosmic ray removal algorithm [34]; this algorithm is capable of removing cosmic rays from a spectrum by identifying a closely matching spectrum in the dataset, and replacing each cosmic ray with the corresponding wavenumber intensity values in that matching spectrum. It is, therefore, not necessary to apply the commonly used double acquisition cosmic ray removal method to obtain a closely matching spectrum [35], which was found to be problematic for the high-speed spectral acquisition applied in the automation process.…”

spectrapepper: A Python toolbox for advanced analysis of spectroscopic data for materials and devices.

“…• Noise filters, trimming tools, and despiking methods (Barton & Hennelly, 2019;Whitaker & Hayes, 2018). • Chemometrics algorithms to find peaks, fit curves, and deconvolve spectra.…”