Scattering in infrared spectroscopy of cells and tissues can be well estimated by the Mie theory, which provides a formalism for calculating the so-called extinction efficiency determining the loss of radiation due to scattering and absorption as a function of the wavelength. The loss of radiation due to scattering is considerable in infrared microspectroscopy of cells and tissues, since strong scattering effects appear when the wavelength of the infrared radiation matches the size of the cell and tissue components.

However, radiation is lost in forward direction not only due to absorption by the material, but also as a result of scattering. This results in spectra that quantify the loss of radiation in forward direction with respect to the incoming radiation.

In infrared microspectroscopy, infrared radiation impinges on thin biological materials and the radiation transmitted in forward direction (or over a numerical aperture) is collected by a detector, which is either a single element or a focal plane array (FPA) detector. (b) Cells and tissues are highly scattering samples and thus the acquired spectral data is distorted by scattering signatures, which carry information about cell morphology and shapes of the cell components. The information content provided by infrared techniques for the investigation of biological samples such as cells and tissues is overwhelming and twofold: (a) Chemical functional groups absorb in the mid-infrared region, thus the obtained absorption signal is a precise and interpretable fingerprint of the chemical composition of the sample. Infrared spectroscopy has become a popular tool in biology and medical and material sciences for investigating the structure and chemistry of intact materials. We confirm that the same chemical information is contained in the DSAE corrected spectra as in the spectra corrected with ME-EMSC. The DSAE outperformed the ME-EMSC correction in terms of speed, robustness, and noise levels. One DSAE was trained on the spectra, which were corrected with different reference spectra and validated on independent test data. Since the raw spectra showed large variability in chemical features, different reference spectra matching the chemical signals of the spectra were used to initialize the ME-EMSC algorithm, which is beneficial for the quality of the correction and the speed of the algorithm. In this paper, we present a deep convolutional descattering autoencoder (DSAE) which was trained on a set of ME-EMSC corrected infrared spectra and which can massively reduce the computation time for scatter correction. However, the algorithm is computationally expensive and the correction of large infrared imaging datasets requires weeks of computations.

The state-of-the-art Mie extinction extended multiplicative signal correction (ME-EMSC) algorithm is a powerful tool for the recovery of pure absorbance spectra from highly scatter-distorted spectra. Infrared spectroscopy of cells and tissues is prone to Mie scattering distortions, which grossly obscure the relevant chemical signals.

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