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Infrared spectra obtained from cell or tissue specimen have commonly beenobserved to involve a significant degree of (resonant) Mie scattering, whichoften overshadows biochemically relevant spectral information by a non-linear,non-additive spectral component in Fourier transformed infrared (FTIR)spectroscopic measurements. Correspondingly, many successful machine learningapproaches for FTIR spectra have relied on preprocessing procedures thatcomputationally remove the scattering components from an infrared spectrum. Wepropose an approach to approximate this complex preprocessing function usingdeep neural networks. As we demonstrate, the resulting model is not justseveral orders of magnitudes faster, which is important for real-time clinicalapplications, but also generalizes strongly across different tissue types.Furthermore, our proposed method overcomes the trade-off between computationtime and the corrected spectrum being biased towards an artificial referencespectrum.

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