Extracting pure absorbance spectra in infrared microspectroscopy by modeling absorption bands as Fano resonances

Abstract

Midinfrared absorbance spectra obtained from spatially inhomogeneous and finite samples often contain scattering effects characterized by derivative-like bands with shifted peak positions. Such features may be interpreted and accurately modeled by Fano theory when the imaginary part of the complex dielectric function is small and Lorentzian in nature—as is the case for many biological media. Furthermore, by fitting Fano line shapes to isolated absorbance bands, recovery of the peak position and pure absorption strength can be obtained with high accuracy. Additionally, for small and optically soft spherical scatterers, recovery of one or the other of constant refractive index or radius (given approximate knowledge of the other) is possible.

Attenuated Total Reflection (ATR) Micro-Fourier Transform Infrared (Micro-FT-IR) Spectroscopy to Enhance Repeatability and Reproducibility of Spectra Derived from Single Specimen Organic-Walled Dinoflagellate Cysts

“…36– 50 For some sample setups and geometries, these spectral distortions can be partly quantified and corrected with model-based correction algorithms which often need pure absorbance spectra as a reference. 37,51–59 In the past few years, deep learning approaches have made their advance in FT-IR spectroscopy. 60–63 Worth noticing is that recently developed deep neural network architectures can successfully approximate complex, often computationally heavy, model-based correction algorithms.…”

“…Extensions to basic EMSC exist to account for specific types of light scattering such as resonant Mie-type scattering (RMieS-EMSC) that dominantly occur in spherical scatterers 54,55,59 or scattering in fibers and cylindrical domains. 97 It is however still unclear if EMSC-based algorithms are able to correct perfectly and extract pure absorbance spectra, a feat which might only be currently achievable by other modeling and deep learning approaches, 58,60–62,64 which are nonlinear by nature. 98 For some deep learning classification algorithms, the best classification results are observed without any prior data processing, except for assignment of a target class and splitting of the data set into training, validation, and testing sets.…”

Analysis of Fixed and Live Single Cells Using Optical Photothermal Infrared with Concomitant Raman Spectroscopy

“…Water is a strong IR absorber, meaning that live cell imaging in an aqueous environment is extremely difficult since the features in the cell spectrum, particularly the amide I vibration associated with proteins, can be obscured by the strong and broad bending mode of water centered at ∼1630–1640 cm –1 . , One way to address this problem is to use a high-brightness IR source. A number of papers have employed synchrotron FTIR, but the experiments are far from trivial and involve significant compromise in the spectral information obtained. ,,, …”

“…A number of papers have employed synchrotron FTIR, but the experiments are far from trivial and involve significant compromise in the spectral information obtained. 41,47,54,55 Recently, however, a completely optically based photothermal method of obtaining infrared spectra has been developed that overcomes some of the limitations of FTIR. 56 Based on the well-established principles of thermal lens spectroscopy 57 and termed optical photothermal infrared (O-PTIR) spectroscopy, this technique is essentially a pump− probe setup, whereby the pump is the pulsed IR laser and the probe is a visible (green, 532 nm) laser.…”

Infrared spectra of micro-structured samples with microPhotoacoustic spectroscopy and synchrotron radiation

“…12,13 The role of Mie scattering in this context has recently been discussed in detail. 14 The distortion of band shapes is often attributed to a contribution from the real part of the refractive index of the sample. In some cases, the distortion is so severe as to affect or even prevent a realistic interpretation of the spectroscopic data.…”