Infrared refractive index dispersion of polymethyl methacrylate spheres from Mie ripples in Fourier-transform infrared microscopy extinction spectra

Inverse reconstruction of model cells: Extracting structural and molecular insights through infrared spectroscopic cytology

Infrared (IR) microspectroscopy stands as a transformative clinical tool for analyzing single biological cells in biopsy samples, offering critical insights into their chemical composition. In this study, we further develop a recently proposed inverse scattering algorithm that accurately reconstructs the dielectric properties of single cells, considering both scattering and absorption. We demonstrate the method's effectiveness using spherical model cells filled with six organic test substances: polymethyl methacrylate (PMMA), polycarbonate (PC), polydimethylsiloxane (PDMS), polyetherimide (PEI), polyethylene terephthalate (PET), and polystyrene (PS). The permittivity values of these substances, reconstructed from their extinction efficiencies and known refractive indexes from the literature, show excellent agreement with experimental data. Our comparative analysis of the basis sets for the reconstruction algorithm reveals that using dielectric functions leads to more accurate results compared to anti-symmetrized Lorentzians. We find that compared to other methods in the literature on PMMA spheres, our approach yields reconstructions of significantly higher quality. These findings not only enhance reconstruction accuracy but also advance the potential of IR microspectroscopy for clinical cytology, where precise molecular analysis is crucial for disease diagnosis and monitoring at the cellular level.

Signatures of top versus bottom illuminations and their predicted implications for infrared transmission microspectroscopy

Since both top and bottom illuminations are widely used in infrared transmission measurements, in this paper, we study the effects of different illuminations on the signatures in infrared microspectroscopy. By simulating a series of dielectric samples, we show that their extinction efficiency,Q ext , remains unchanged when the direction of the incident plane wave is reversed, even though the field distributions both inside and outside of the sample may be dramatically different. We find features inQ ext that are correlated with whispering gallery modes for one beam direction and correspond to completely different field distributions for the opposite beam direction. In addition, by linking the optical theorem and the reciprocity relation of far-field scattered field, we rigorously prove the invariance ofQ ext for arbitrary dielectric targets under opposite plane-wave illuminations. Furthermore, we show the difference in the apparent absorbance spectrum for opposite beam directions when considering numerical apertures.

Single Infrared Spectrum Enables Simultaneous Identification of (Bio)Chemical Nature and Particle Size of Microorganisms and Synthetic Microplastic Beads

Populations of nearly identical chemical and biological microparticles include the synthetic microbeads used in cosmetic, biomedical, agri-food, and pharmaceutical industries as well as the class of living microorganisms such as yeast, pollen, and biological cells. Herein, we identify simultaneously the size and chemical nature of spherical microparticle populations with diameters larger than 1 μm. Our analysis relies on the extraction of both physical and chemical signatures from the same optical spectrum recorded using attenuated total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy. These signatures are the spectral resonances caused by the microparticles, which depend on their size and the absorption peaks revealing their chemical nature. We validate the method first on separated and mixed groups of spherical microplastic particles of two different diameters, where the method is used to calculate the diameter of the microspherical particles. Then, we apply the method to correctly identify and measure the diameter of Saccharomyces cerevisiae yeast cells. Theoretical simulations to help in understanding the effect of size distribution and dispersion support our results.

Effects of solvent environment on the spectroscopic properties of tylosin, an experimental and theoretical approach

Tylosin is a commonly used antibiotic in animal medicine. However, it remains unclear how tylosin impacts the broader ecosystem once the host animal has excreted it. One of the main concerns is that it can lead to the development of antibiotic resistance. Therefore, there exists a need to develop systems that remove tylosin from the environment. Utilizing UV irradiation to destroy pathogens is one technique often deployed by scientists and engineers. However, for light-based techniques to be efficient, it is necessary to understand the spectral properties of the material being removed. Steady-state spectroscopy and density functional theory were used to analyze the electronic transitions of tylosin responsible for its strong absorbance in the mid-UV region. It was observed that the absorbance peak of tylosin stems from two transitions in the conjugated region of the molecule. Moreover, these transitions stem from an electronegative region of the molecule, which would allow them to be manipulated by changing solvent polarity. Finally, a polariton model has been proposed, which can be used to initiate the photodegradation of tylosin without the need for direct irradiation of the molecule with UV-B light.

Detection of Sub-20 μm Microplastic Particles by Attenuated Total Reflection Fourier Transform Infrared Spectroscopy and Comparison with Raman Spectroscopy

Microplastics are particulate water contaminants that are raising concerns regarding their environmental and health impacts. Optical spectroscopy is the gold standard for their detection; however, it has severe limitations such as tens of hours of analysis time and spatial resolution of more than 10 μm, when targeting the production of a 2D map of the microparticle population. In this work, through a single spectrum acquisition, we aim at quickly getting information about the whole population of identical particles, their chemical nature, and their size in a range below 20 μm. To this end, we built a compact setup enabling both attenuated total reflection Fourier transform infrared (ATR-FTIR) and Raman spectroscopy measurement on the same sample for comparison purposes. We used monodisperse polystyrene and poly(methyl methacrylate) microplastic spheres of sizes ranging between 6 and 20 μm, also measured collectively using a bench-top FTIR spectrometer in ATR mode. The ATR-FTIR technique appears to be more sensitive for the smallest particles of 6 μm, while the opposite trend is observed using Raman spectroscopy. We use theoretical modeling to simulate and explain the ripples observed in the measured spectra at the shortest wavelength (higher wavenumber) region, which appears as an indicator of the microparticle dimension. The latter finding opens new perspectives for ATR-FTIR for the identification and classification of populations of nearly identical micro-scale bodies, such as bacteria and other micro-organisms, where the same measured spectrum embeds dual information about the chemical nature and the size.

Domes and semi-capsules as model systems for infrared microspectroscopy of biological cells

It is well known that infrared microscopy of micrometer sized samples suffers from strong scattering distortions, attributed to Mie scattering. The state-of-the-art preprocessing technique for modelling and removing Mie scattering features from infrared absorbance spectra of biological samples is built on a meta model for perfect spheres. However, non-spherical cell shapes are the norm rather than the exception, and it is therefore highly relevant to evaluate the validity of this preprocessing technique for deformed spherical systems. Addressing these cases, we investigate both numerically and experimentally the absorbance spectra of 3D-printed individual domes, rows of up to five domes, two domes with varying distance, and semi-capsules of varying lengths as model systems of deformed individual cells and small cell clusters. We find that coupling effects between individual domes are small, corroborating previous related literature results for spheres. Further, we point out and illustrate with examples that, while optical reciprocity guarantees the same extinction efficiency for top vs. bottom illumination, a scatterer's internal field may be vastly different in these two situations. Finally, we demonstrate that the ME-EMSC model for preprocessing infrared spectra from spherical biological systems is valid also for deformed spherical systems.

Space-resolved chemical information from infrared extinction spectra

A new method is presented for the extraction of the complex index of refraction from the extinction efficiency, [Formula: see text], of homogeneous and layered dielectric spheres that simultaneously removes scattering effects and corrects measured extinction spectra for systematic experimental errors such as baseline shifts, tilts, curvature, and scaling. No reference spectrum is required and fit functions may be used that automatically satisfy the Kramers-Kronig relations. Thus, the method yields the complex refractive index of a sample for unambiguous interpretation of the chemical information of the sample. In the case of homogeneous spheres, the method also determines the radius of the sphere. In the case of layered spheres, the method determines the substances within each layer. Only a single-element detector is required. Using numerically computed [Formula: see text] data of polymethyl-methacrylate and polystyrene homogeneous and layered spheres, we show that the new reconstruction algorithm is accurate and reliable. Reconstructing the complex refractive index from a published, experimentally measured raw absorbance spectrum shows that the new method simultaneously corrects spectra for scattering effects and, given shape information, corrects raw spectra for systematic errors that result in spectral distortions such as baseline shifts, tilts, curvature, and scaling.

Deep learning-enabled Inference of 3D molecular absorption distribution of biological cells from IR spectra

Infrared spectroscopy delivers abundant information about the chemical composition, as well as the structural and optical properties of intact samples in a non-destructive manner. We present a deep convolutional neural network which exploits all of this information and solves full-wave inverse scattering problems and thereby obtains the 3D optical, structural and chemical properties from infrared spectroscopic measurements of intact micro-samples. The proposed model encodes scatter-distorted infrared spectra and infers the distribution of the complex refractive index function of concentrically spherical samples, such as many biological cells. The approach delivers simultaneously the molecular absorption, sample morphology and effective refractive index in both the cell wall and interior from a single measured spectrum. The model is trained on simulated scatter-distorted spectra, where absorption in the distinct layers is simulated and the scatter-distorted spectra are estimated by analytic solutions of Maxwell's equations for samples of different sizes. This allows for essentially real-time deep learning-enabled infrared diffraction micro-tomography, for a large subset of biological cells.

Effects of the coupling of dielectric spherical particles on signatures in infrared microspectroscopy

Infrared microspectroscopy is a powerful tool in the analysis of biological samples. However, strong electromagnetic scattering may occur since the wavelength of the incident radiation and the samples may be of comparable size. Based on the Mie theory of single spheres, correction algorithms have been developed to retrieve pure absorbance spectra. Studies of the scattering characteristics of samples of different types, obtained by microspectroscopy, have been performed. However, the detailed, microscopic effects of the coupling of the samples on signatures in spectra, obtained by infrared microspectroscopy, are still not clear. The aim of this paper is to investigate how the coupling of spherical samples influences the spectra. Applying the surface integral equation (SIE) method, we simulate small dielectric spheres, arranged as double-spheres or small arrays of spheres. We find that the coupling of the spheres hardly influences the broad oscillations observed in infrared spectra (the Mie wiggles) unless the radii of the spheres are different or the angle between the direction of the incident radiation and the normal of the plane where the spheres are located is large. Sharp resonance features in the spectra (the Mie ripples) are affected by the coupling of the spheres and this effect depends on the polarization of the incident wave. Experiments are performed to verify our conclusions.

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

The chemical composition of recent and fossil organic-walled dinoflagellate cyst walls and its diversity is poorly understood and analyses on single microscopic specimens are rare. A series of infrared spectroscopic experiments resulted in the proposition of a standardized attenuated total reflection micro-Fourier transform infrared-based method that allows the collection of robust data sets consisting of spectra from individual dinocysts. These data sets are largely devoid of nonchemical artifacts inherent to other infrared spectrochemical methods, which have typically been used to study similar specimens in the past. The influence of sample preparation, specimen morphology and size and spectral data processing steps is also assessed within this methodological framework. As a result, several guidelines are proposed which facilitate the collection and qualitative interpretation of highly reproducible and repeatable spectrochemical data. These, in turn, pave the way for a systematic exploration of dinocyst chemistry and its assessment as a chemotaxonomical tool or proxy.

Infrared Refraction Spectroscopy

We suggest a new modality of infrared spectroscopy termed Infrared Refraction Spectroscopy, which is complimentary to absorption spectroscopy. The beauty of this new modality lies not only in its simplicity but also in the fact that it closes an important gap: It allows to quantitatively interpret reflectance spectra by simplest means. First, the refractive index spectrum is calculated from reflectance by neglecting absorption. The change of the refractive index is proportional to concentration, and the spectra with features similar to second derivative absorbance spectra can simply be computed by numerically deriving the refractive index spectra, something which can be easily carried out by standard spectra software packages. The peak values of the derived spectra indicate oscillator positions and are approximately proportional to the concentration in a similar way as absorbance is. In contrast to absorbance spectra, there are no baseline ambiguities for first derivative refractive index spectra, and in refractive index spectra, instead of integrating over a band area, a simple difference of two refractive index values before and after an absorption leads to a quantity that correlates perfectly linearly with concentration in the absence of local field effects.

The effect of deformation of absorbing scatterers on Mie-type signatures in infrared microspectroscopy

Mie-type scattering features such as ripples (i.e., sharp shape-resonance peaks) and wiggles (i.e., broad oscillations), are frequently-observed scattering phenomena in infrared microspectroscopy of cells and tissues. They appear in general when the wavelength of electromagnetic radiation is of the same order as the size of the scatterer. By use of approximations to the Mie solutions for spheres, iterative algorithms have been developed to retrieve pure absorbance spectra. However, the question remains to what extent the Mie solutions, and approximations thereof, describe the extinction efficiency in practical situations where the shapes of scatterers deviate considerably from spheres. The aim of the current study is to investigate how deviations from a spherical scatterer can change the extinction properties of the scatterer in the context of chaos in wave systems. For this purpose, we investigate a chaotic scatterer and compare it with an elliptically shaped scatterer, which exhibits only regular scattering. We find that chaotic scattering has an accelerating effect on the disappearance of Mie ripples. We further show that the presence of absorption and the high numerical aperture of infrared microscopes does not explain the absence of ripples in most measurements of biological samples.

FTIR Nanospectroscopy Shows Molecular Structures of Plant Biominerals and Cell Walls

Plant tissues are complex composite structures of organic and inorganic components whose function relies on molecular heterogeneity at the nanometer scale. Scattering-type near-field optical microscopy (s-SNOM) in the mid-infrared (IR) region is used here to collect IR nanospectra from both fixed and native plant samples. We compared structures of chemically extracted silica bodies (phytoliths) to silicified and nonsilicified cell walls prepared as a flat block of epoxy-embedded awns of wheat (Triticum turgidum), thin sections of native epidermis cells from sorghum (Sorghum bicolor) comprising silica phytoliths, and isolated cells from awns of oats (Avena sterilis). The correlation of the scanning-probe IR images and the mechanical phase image enables a combined probing of mechanical material properties together with the chemical composition and structure of both the cell walls and the phytolith structures. The data reveal a structural heterogeneity of the different silica bodies in situ, as well as different compositions and crystallinities of cell wall components. In conclusion, IR nanospectroscopy is suggested as an ideal tool for studies of native plant materials of varied origins and preparations and could be applied to other inorganic-organic hybrid materials.

Deep learning for 'artefact' removal in infrared spectroscopy

It has been well recognized that infrared spectra of microscopically heterogeneous media do not merely reflect the absorption of the sample but are influenced also by geometric factors and the wave nature of light causing scattering, reflection, interference, etc. These phenomena often occur simultaneously in complex samples like tissues and manifest themselves as intense baseline profiles, fringes, band distortion and band intensity changes in a measured IR spectrum. The information on the molecular level contained in IR spectra is thus entangled with the geometric structure of a sample and the optical model behind it, which largely hinders the data interpretation and in many cases renders the Beer-Lambert law invalid. It is required to recover the pure absorption (i.e., absorbance) of the sample from the measurement (i.e., apparent absorbance), that is, to remove the 'artefacts' caused merely by optical influences. To do so, we propose an artefact removal approach based on a deep convolutional neural network (CNN), specifically a 1-dimensional U-shape convolutional neural network (1D U-Net), and based our study on poly(methyl methacrylate) (PMMA) as materials. To start, a simulated dataset composed of apparent absorbance and absorbance pairs was generated according to the Mie-theory for PMMA spheres. After a data augmentation procedure, this dataset was utilized to train the 1D U-Net aiming to transform the input apparent absorbance into the corrected absorbance. The performance of the artefact removal was evaluated by the hit-quality-index (HQI) between the corrected and the true absorbance. Based on the prediction and the HQI of two experimental and one simulated independent testing datasets, we could demonstrate that the network was able to retrieve the absorbance very well, even in cases where the absorbance is completely overwhelmed by extremely large 'artefacts'. As the testing datasets bear different patterns of absorbance and 'artefacts' to the training data, the promising correction also indicated a good generalization performance of the 1D U-Net. Finally, the reliability and computational mechanism of the trained network were illustrated via two interpretation approaches including a direct visualization of layer-wise outputs as well as a saliency-based method.

Extended Multiplicative Signal Correction for Infrared Microspectroscopy of Heterogeneous Samples with Cylindrical Domains

Optical scattering corrections are invoked to computationally distinguish between scattering and absorption contributions to recorded data in infrared (IR) microscopy, with a goal to obtain an absorption spectrum that is relatively free of the effects of sample morphology. Here, we present a modification of the extended multiplicative signal correction (EMSC) approach that allows for spectral recovery from fibers and cylindrical domains in heterogeneous samples. The developed theoretical approach is based on exact Mie theory for infinite cylinders. Although rigorous Mie theory implies utilization of comprehensive and time-consuming calculations, we propose to change the workflow of the original EMSC algorithm to minimize extensive calculations for each recorded spectrum at each iteration step. This makes the modified EMSC approach practical for routine use. First, we tested our approach using synthetic data derived from a rigorous model of scattering from cylinders in an IR microscope. Second, we applied the approach to Fourier transform IR (FT-IR) microspectroscopy data recorded from filamentous fungal and cellulose samples with pronounced fiber-like shapes. While the corrected spectra show greatly reduced baseline offsets and consistency, strongly absorbing regions of the spectrum require further refinement. The modified EMSC algorithm broadly mitigates the effects of scattering, offering a practical approach to more consistent and accurate spectra from cylindrical objects or heterogeneous samples with cylindrical domains.

Observation of Mie ripples in the synchrotron Fourier transform infrared spectra of spheroidal pollen grains

Conceptually, biological cells are dielectric, photonic resonators that are expected to show a rich variety of shape resonances when exposed to electromagnetic radiation. For spheroidal cells, these shape resonances may be predicted and analyzed using the Mie theory of dielectric spheres, which predicts that a special class of resonances, i.e., whispering gallery modes (WGMs), causes ripples in the absorbance spectra of spheroidal cells. Indeed, the first tentative indication of the presence of Mie ripples in the synchrotron Fourier transform infrared (SFTIR) absorbance spectra of Juniperus chinensis pollen has already been reported [Analyst140, 3273 (2015)ANLYAG0365-488510.1039/C5AN00401B]. To show that this observation is no isolated incidence, but a generic spectral feature that can be expected to occur in all spheroidal biological cells, we measured and analyzed the SFTIR absorbance spectra of Cunninghamia lanceolata, Juniperus chinensis, Juniperus communis, and Juniperus excelsa. All four pollen species show Mie ripples. Since the WGMs causing the ripples are surface modes, we propose ripple spectroscopy as a powerful tool for studying the surface properties of spheroidal biological cells. In addition, our paper draws attention to the fact that shape resonances need to be taken into account when analyzing (S)FTIR spectra of isolated biological cells since shape resonances may distort the shape or mimic the presence of chemical absorption bands.

Extinction spectra of suspensions of microspheres: determination of the spectral refractive index and particle size distribution with nanometer accuracy

A method is presented to infer simultaneously the wavelength-dependent real refractive index (RI) of the material of microspheres and their size distribution from extinction measurements of particle suspensions. To derive the averaged spectral optical extinction cross section of the microspheres from such ensemble measurements, we determined the particle concentration by flow cytometry to an accuracy of typically 2% and adjusted the particle concentration to ensure that perturbations due to multiple scattering are negligible. For analysis of the extinction spectra, we employ Mie theory, a series-expansion representation of the refractive index and nonlinear numerical optimization. In contrast to other approaches, our method offers the advantage to simultaneously determine size, size distribution, and spectral refractive index of ensembles of microparticles including uncertainty estimation.

Dielectric Sphere Clusters as a Model to Understand Infrared Spectroscopic Imaging Data Recorded from Complex Samples

Understanding the infrared (IR) spectral response of materials as a function of their morphology is not only of fundamental importance but also of contemporary practical need in the analysis of biological and synthetic materials. While significant work has recently been reported in understanding the spectra of particles with well-defined geometries, we report here on samples that consist of collections of particles. First, we theoretically model the importance of multiple scattering effects and computationally predict the impact of local particles' environment on the recorded IR spectra. Both monodisperse and polydisperse particles are considered in clusters with various degrees of packing. We show that recorded spectra are highly dependent on the cluster morphology and size of particles but the origin of this dependence is largely due to the scattering that depends on morphology and not absorbance that largely depends on the volume of material. The effect of polydispersity is to reduce the fine scattering features in the spectrum, resulting in a closer resemblance to bulk spectra. Fourier transform-IR (FT-IR) spectra of clusters of electromagnetically coupled poly(methyl methacrylate) (PMMA) spheres with wavelength-scale diameters were recorded and compared to simulated results. Measured spectra agreed well with those predicted. Of note, when PMMA spheres occupy a volume greater than 18% of the focal volume, the recorded IR spectrum becomes almost independent of the cluster's morphological changes. This threshold, where absorbance starts to dominate the signal, exactly matches the percolation threshold for hard spheres and quantifies the transition between the single particle and bulk behavior. Our finding enables an understanding of the spectral response of structured samples and points to appropriate models for recovering accurate chemical information from in IR microspectroscopy data.