Within this book the use of MatLab as a means to analyze Raman spectroscopy data is explored. Raman measurements of a wide range of biological building blocks, with an increasing degree of complexity, were taken and whole cells were analyzed. The associated peaks were assigned and possible noise sources and sources of baseline were discussed and the generated signals were exported from Labspec into Mathworks MatLab, then analyzed by differentiation and filtering. With a combination of classical approaches and specially designed ones the baseline of signals were automatically identified and consequently subtracted. This procedure was applied to artificial signals as well as to the real taken spectra and then compared regarding their significance, paving the way to a fully automated baseline subtraction regime.

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Peter Knief studied biomedical technology at the University for Applied Science in Aachen. He graduated in 2006 under supervision of Prof. A. Hardt (FH-Juelich) and Prof. H. J. Byrne of the Dublin Institute of Technology. Since 2006 he continues his work on signal processing and uses Raman spectroscopy access the toxicity of Carbon Nano Tubes.

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Signal conditioning in Raman Spectroscopy Signals: Is there a way to tackel the baseline problem in Raman Spectroscopy ?

Knief, Peter

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