  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.
