Removing Cosmic Ray Features from Raman Map Data by a Refined Nearest Neighbor Comparison Method as a Precursor for Chemometric Analysis

Abstract

An algorithm to remove cosmic ray (CR) features from Raman spectra collected in mapping experiments using a charge-coupled device (CCD) is presented. Each spectrum is compared to spectra collected from adjacent points in space using correlation values. The most similar neighbor (MSN) spectrum is selected, offset, and used for identification of CRs. The offset values are defined in terms of the noise level for data with a low signal-to-noise ratio and in terms of the peak height for data with a high signal-to-noise ratio. Scaled intensity values of the MSN spectra are used for replacement of contaminated pixels, allowing for full recovery of underlying spectral features. The algorithm is applicable for any Raman map where the particle sizes within the analyzed mixture are larger than the sampling size or to any other data where the sampling is more frequent than the variation, e.g., time series or temperature profiles. Its application to several maps of pharmaceutical samples is discussed here. With an appropriate offset value for the MSN spectra, no misdetections occur, and all CRs more intense than the offset are removed, which includes the CRs that would have hampered subsequent chemometric analysis by methods such as principal component analysis (PCA).

Fusion of MALDI Spectrometric Imaging and Raman Spectroscopic Data for the Analysis of Biological Samples

“…Therefore, this approach is not suitable for Raman imaging when a large number of spectra are recorded. Thus, specialized spike correction approaches like wavelet transform (Ehrentreich and Summchen, 2001 ), correlation methods (Cappel et al, 2010 ), calculation of the Laplacian of the spectral data matrix (Schulze and Turner, 2014 ; Ryabchykov et al, 2016 ), or a difference between the original and a smoothed spectrum (Zhang and Henson, 2007 ) must be used for spike removal.…”

Recent advancement of chemical imaging in pharmaceutical quality control: From final product testing to industrial utilization

“…Method \Nearest Neighbor Comparison (NNC)" is therefore raised to detect spikes and recover spectral signal. 25 Based on the statistical assumption that neighboring pixels share similar intensity values, NNC determines an o®set value to identify spikes and replace them with scaled value of the most similar neighbors. Most commercial software are also able to achieve spikes corrections and in a real-time manner.…”

Optimal choice of sample substrate and laser wavelength for Raman spectroscopic analysis of biological specimen

“…This metric has previously been applied to compare the similarity between Raman spectra for cosmic ray removal. 31 The corresponding values obtained using this method are available in Table 2, where 1.000 is the optimum result, and lower values represent less covariance between spectra.…”