Noise and artifact characterization of in vivo Raman spectroscopy skin measurements

In this work principal component analysis (PCA), a multivariate pattern recognition technique, is used to characterize the noise contribution of the experimental apparatus and two commonly used methods for fluorescence removal used in biomedical Raman spectroscopy measurements. These two methods are a fifth degree polynomial fitting and an iterative variation of it commonly known as the Vancouver method. The results show that the noise in Raman spectroscopy measurements is related to the spectral resolution of the measurement equipment, the intrinsic variability of the biological measurements, and the fluorescence removal algorithm used. © 2012 Society for Applied Spectroscopy.

Biomedical measurements; Dermatology; Fluorescence; Noise; PCA; Principal components analysis; Raman spectroscopy Biological measurement; Biomedical measurements; Experimental apparatus; Fifth degree polynomials; In-vivo; Intrinsic variabilities; Iterative variation; Measurement equipment; Multivariate patterns; Noise; Noise contributions; PCA; Principal components analysis; Spectroscopy measurements; Dermatology; Fluorescence; Pattern recognition; Principal component analysis; Raman spectroscopy; Raman scattering; politef; algorithm; article; artifact; chemistry; human; instrumentation; methodology; principal component analysis; Raman spectrometry; signal processing; skin; spectrofluorometry; Algorithms; Artifacts; Humans; Polytetrafluoroethylene; Principal Component Analysis; Signal Processing, Computer-Assisted; Skin; Spectrometry, Fluorescence; Spectrum Analysis, Raman

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