Deep Neural Networks for the Correction of Mie Scattering in Fourier-Transformed Infrared Spectra of Biological Samples

• URL: http://arxiv.org/abs/2002.07681v1
• Date: Tue, 18 Feb 2020 16:07:07 GMT
• Title: Deep Neural Networks for the Correction of Mie Scattering in Fourier-Transformed Infrared Spectra of Biological Samples
• Authors: Arne P. Raulf and Joshua Butke and Lukas Menzen and Claus K\"upper and Frederik Gro{\ss}erueschkamp and Klaus Gerwert and Axel Mosig
• Abstract summary: We propose an approach to approximate this complex preprocessing function using deep neural networks. Our proposed method overcomes the trade-off between time and the corrected spectrum being biased towards an artificial reference spectrum.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Infrared spectra obtained from cell or tissue specimen have commonly been observed to involve a significant degree of (resonant) Mie scattering, which often overshadows biochemically relevant spectral information by a non-linear, non-additive spectral component in Fourier transformed infrared (FTIR) spectroscopic measurements. Correspondingly, many successful machine learning approaches for FTIR spectra have relied on preprocessing procedures that computationally remove the scattering components from an infrared spectrum. We propose an approach to approximate this complex preprocessing function using deep neural networks. As we demonstrate, the resulting model is not just several orders of magnitudes faster, which is important for real-time clinical applications, but also generalizes strongly across different tissue types. Furthermore, our proposed method overcomes the trade-off between computation time and the corrected spectrum being biased towards an artificial reference spectrum.

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