Despite the existence of various methods to remove cosmic spikes from Raman data, only a few of them are suitable for process Raman spectroscopy. The disadvantages of these algorithms include increased analysis time, low accuracy of spike detection, or reliance on variable parameters that must be chosen by trial and error in each case. We demonstrate a novel approach to detecting cosmic spikes in process Raman data and validate it using a wide range of experimental data. This new method features a multistage spike recognition algorithm that is based on tracking sharp changes of intensity in the time domain. The algorithm effectively distinguishes cosmic spikes from random spectral noise and abrupt variations of Raman peaks, allowing accurate detection of both high and low intensity cosmic spikes. The procedure is free from variable user-defined parameters and operates reliably in a fully automated manner with a wide range of time-series process Raman data sets containing more than 40 to 50 spectra.
Vol. 7, Iss. 12 Virtual Journal for Biomedical Optics
Sergey Mozharov, Alison Nordon, David Littlejohn, and Brian Marquardt, "Automated Cosmic Spike Filter Optimized for Process Raman Spectroscopy," Appl. Spectrosc. 66, 1326-1333 (2012)
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