Automatization of spike correction in Raman spectra of biological samples

Abstract Raman spectroscopy as a technique has high potential for biological applications, e.g. cell and tissue analysis. In these applications, large data sets are normally recorded which require automated analysis. Unfortunately, a lot of disturbing external influences exist, which negatively affect the analysis of Raman spectra. A problematic corrupting effect in big data sets is cosmic ray noise, which usually produces intense spikes within the Raman spectra. In order to exploit Raman spectroscopy in real world applications, detection and removing of spikes should be stable, data-independent and performed without manual control. Herein, an automatic algorithm for cosmic ray noise correction is presented. The algorithm distinguishes spikes from spectra based on their response to a Laplacian, e.g. their sharpness. Manual rating of the spike presence was used as a benchmark for algorithm validation. The algorithm's sensitivity was estimated to be above 99%.