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Spectroscopic optical coherence tomography for substance identification

We use Spectroscopic Optical Coherence Tomography (S-OCT) to identify substances by their spectral features in multi layer non-scattering samples. Depth resolved spectra are calculated by a windowed Fourier Transform in the spatial regime at discrete layer borders. By dividing subsequent spectra in an iterative manner transfer functions of the samples layers are calculated. Estimating these spectral transfer functions with high accuracy is still challenging, since the system´s transfer function introduces an error, which can be orders of magnitude higher than the spectroscopic information of the sample. We retrieve the buried spectroscopic information of the sample with high accuracy by correcting the spectral transfer functions with an identically structured reference sample. This spectral calibration method has many critical parameters and is in many cases not even possible. To perform substance identification without spectral calibration we implemented a pattern recognition algorithm, which allocates the transfer functions to known substances. Our results show that substance identification by spectral features with high performance without spectral calibration is feasible. Aside from that we modeled a simplified set up of our OCT system to minimize the error which is introduced by the optical system. The error can be reduced by orders of magnitude, when our improved optical set-up is used. This is an important step towards an improved system for S-OCT.