Deep Neural Networks for the Correction of Mie Scattering in Fourier-Transformed Infrared Spectra of Biological Samples
This work addresses the need for real-time clinical applications by improving the speed and accuracy of scattering correction in FTIR spectroscopy for biological samples, though it is incremental as it builds on existing preprocessing methods.
The paper tackled the problem of Mie scattering overshadowing biochemical information in FTIR spectra of biological samples by using deep neural networks to approximate preprocessing functions, resulting in a method that is orders of magnitude faster and generalizes across tissue types while avoiding bias towards artificial reference spectra.
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.