Recovery of absorbance spectra of micrometer-sized biological and inanimate particles

Analytical and experimental solutions for Fourier transform infrared microspectroscopy measurements of microparticles: A case study on Quercus pollen

BACKGROUND

FTIR microspectroscopy is a popular non-destructive technique for chemical analysis and identification of microparticles, such as microplastics, pollen, spores, microplankton organisms, sediments and microfossils. Unfortunately, measured spectra of microparticles are usually distorted by Mie-type scattering interferents thus hindering the analysis of spectral data. To retrieve chemical absorbance spectra, two different approaches are regularly employed: analytical (application of scatter-correction preprocessing methods), and experimental (measurement in an embedding matrix). The comparative studies of preprocessing spectral strategies are needed to determine pros and cons of these approaches, and when they are most suitable for use.

RESULTS

We conducted the first-ever comparative study on 12 different analytical and experimental approaches for FTIR measurements of microparticles, as demonstrated on classification and chemical characterisation of pollen of four Quercus species. Individual pollen grains were measured on 1) microscope slides and 2) embedded in a paraffin-polyethylene (PEP) matrix. For analytical approaches, we have applied simple model-based algorithm (EMSC: extended multiplicative signal correction), Mie-theory model-based algorithm (ME-EMSC: Mie-extinction EMSC) and deep learning-based algorithm (DCNN: deep convolutional neural network). Moreover, we applied algorithms for the correction of the embedded spectra: fringe-correction EMSC and two different paraffin-correction EMSC algorithms. The best classification accuracy is obtained for simple preprocessing, where scattering information is not completely removed, as well as for complex algorithms where scattering information is parameterized and retained. In chemical characterisation studies, strong scattering signals hinder valuable chemical information, and it is imperative to suppress them either by embedding or by an analytical approach.

SIGNIFICANCE

The results show that scattering spectral interferents are not necessarily detrimental for classification studies of biological microparticles. In fact, they have considerable diagnostic value even in closely related microorganisms due to species-specific physical properties. The results clearly show that analytical and experimental solutions for FTIR measurements of microparticles should be carefully selected, taking into account the origin of the microparticles (i.e., biological or artificial) and purpose of the study (classification or chemical characterisation).

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.

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.

An automated approach for fringe frequency estimation and removal in infrared spectroscopy and hyperspectral imaging of biological samples

In infrared spectroscopy of thin film samples, interference introduces distortions in spectra, commonly referred to as fringes. Fringes may alter absorbance peak ratios, which hampers the spectral analysis. We have previously introduced extended multiplicative signal correction (EMSC) for fringes correction. In the current article, we provide a robust open-source algorithm for fringe correction in infrared spectroscopy and propose several improvements to the Fringe EMSC model. The suggested algorithm achieves a more precise fringe frequency estimation by mean centering of the measured spectrum and applying a window function prior to the Fourier transform. It selects two frequencies from a user defined number of maxima in the Fourier domain. The improved Fringe EMSC algorithm is validated on two experimental datasets, one of them being a hyperspectral image. Techniques for separating sample spectra from background spectra in hyperspectral images, and techniques to identify spectra affected by fringes are also provided.

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.

Discrimination of grass pollen of different species by FTIR spectroscopy of individual pollen grains

Fourier-transform infrared (FTIR) spectroscopy enables the chemical characterization and identification of pollen samples, leading to a wide range of applications, such as paleoecology and allergology. This is of particular interest in the identification of grass (Poaceae) species since they have pollen grains of very similar morphology. Unfortunately, the correct identification of FTIR microspectroscopy spectra of single pollen grains is hindered by strong spectral contributions from Mie scattering. Embedding of pollen samples in paraffin helps to retrieve infrared spectra without scattering artifacts. In this study, pollen samples from 10 different populations of five grass species (Anthoxanthum odoratum, Bromus inermis, Hordeum bulbosum, Lolium perenne, and Poa alpina) were embedded in paraffin, and their single grain spectra were obtained by FTIR microspectroscopy. Spectra were subjected to different preprocessing in order to suppress paraffin influence on spectral classification. It is shown that decomposition by non-negative matrix factorization (NMF) and extended multiplicative signal correction (EMSC) that utilizes a paraffin constituent spectrum, respectively, leads to good success rates for the classification of spectra with respect to species by a partial least square discriminant analysis (PLS-DA) model in full cross-validation for several species. PLS-DA, artificial neural network, and random forest classifiers were applied on the EMSC-corrected spectra using an independent validation to assign spectra from unknown populations to the species. Variation within and between species, together with the differences in classification results, is in agreement with the systematics within the Poaceae family. The results illustrate the great potential of FTIR microspectroscopy for automated classification and identification of grass pollen, possibly together with other, complementary methods for single pollen chemical characterization.

Model-based correction algorithm for Fourier Transform infrared microscopy measurements of complex tissue-substrate systems

Model-based algorithms have recently attracted much attention for data pre-processing in tissue mapping and imaging by Fourier transform infrared micro-spectroscopy (FTIR). Their versatility, robustness and computational performance enabled the improvement of spectral quality by mitigating the impact of scattering and fringing in FTIR spectra of chemically homogeneous biological systems. However, to date, no comprehensive algorithm has been optimized and automated for large-area FTIR imaging of histologically complex tissue samples. Herein, for the first time, we propose a unique, integrated and fully-automated Multiple Linear Regression Multi-Reference (MLR-MR) method for correcting linear baseline effects due to diffuse scattering, for compensating substrate thickness inhomogeneity and accounting for sample chemical heterogeneity in FTIR images. In particular, the algorithm uses multiple-reference spectra for histologically heterogeneous biological samples. The performance of the procedure was demonstrated for FTIR imaging of chemically complex rat brain frontal cortex tissue samples, mounted onto Ultralene® films. The proposed MLR-MR correction algorithm allows the efficient retrieval of "pure" absorbance spectra and greatly improves the histological fidelity of FTIR imaging data, as compared with the one-reference approach. In addition, the MLR-MR algorithm here presented opens up the possibility for extracting information on substrate thickness variability, thus enabling the indirect evaluation of its topography. As a whole, the MLR-MR procedure can be easily extended to more complex systems for which Mie scattering effects must also be eliminated.

FTIR bio-spectroscopy scattering correction using natural biological characteristics of different cell lines

Fourier transform infrared (FTIR) spectroscopy is a well-known method of analysis, with various applications, including promising potential for analyzing biological samples. In the bio-spectroscopy of cells, Mie scattering may increase, which then causes spectral distortion, due to the similarity of cell size with the IR medium-wavelength. These changes make the spectrum unreliable. In previous scattering elimination studies, questionable estimations were considered. For instance, all cells were considered as spherical objects or cell size was estimated randomly. In an attempt to provide the best equation based on the natural existence of cells for the FTIR Mie scattering correction, we examined the actual biological data of cells - as opposed to those yielded from mathematical manipulations. So five biological factors: cell size, shape, granularity, circularity, and edge irregularities, for each cell line were considered as factors which cause scattering. For measuring cell size, roundness and edge irregularity, microscopy images were obtained and processed. For evaluating cell line granularity, flow cytometry was used. Finally, by including these factors, an algorithm was designed. To assess the accuracy of the proposed algorithm, the trypsinized cell spectrum was considered as the high scattering spectrum. Cells were also cultured on a MirrIR slide, and their ATR-FTIR spectrum was considered as the minimum scattering spectrum. The algorithm using the abovementioned five characteristics was used for 13 different cell lines, and in some cases the corrected spectrum demonstrated more than 97% resemblance with the ATR spectra of the same cells. A comparison between the results of this algorithm with the Bassan et al. (2017) algorithm for scattering correction that is freely available on the Internet was then conducted on two different cell lines, clearly showing the advantages of our algorithm, in terms of accuracy and precision. Therefore, this method can be viewed as a more suitable solution for scattering correction in cell investigations.

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.

An open-source code for Mie extinction extended multiplicative signal correction for infrared microscopy spectra of cells and tissues

Infrared spectroscopy of single cells and tissue is affected by Mie scattering. During recent years, several methods have been proposed for retrieving pure absorbance spectra from such measurements, while currently no user-friendly version of the state-of-the-art algorithm is available. In this work, an open-source code for correcting highly scatter-distorted absorbance spectra of cells and tissues is presented, as well as several improvements of the latest version of the Mie correction algorithm based on extended multiplicative signal correction (EMSC) published by Konevskikh et al. In order to test the stability of the code, a set of apparent absorbance spectra was simulated. To this purpose, pure absorbance spectra based on a Matrigel spectrum are simulated. Scattering contributions where obtained by mimicking the scattering features observed in a set of experimentally obtained spectra . It can be concluded that the algorithm is not depending strongly on the reference spectrum used for initializing the algorithm and retrieves well the underlying pure absorbance spectrum. The calculation time of the algorithm is considerably improved with respect to the resonant Mie scattering EMSC algorithm used by the community today.

Fossil pollen and spores as a tool for reconstructing ancient solar-ultraviolet irradiance received by plants: an assessment of prospects and challenges using proxy-system modelling

Ultraviolet-B radiation (UV-B, 280-315 nm) constitutes less than 1% of the total solar radiation that reaches the Earth's surface but has a disproportional impact on biological and ecological processes from the individual to the ecosystem level. Absorption of UV-B by ozone is also one of the primary heat sources to the stratosphere, so variations in UV-B have important relationships to the Earth's radiation budget. Yet despite its importance for understanding atmospheric and ecological processes, there is limited understanding about the changes in UV-B radiation in the geological past. This is because systematic measurements of total ozone and surface UV-B only exist since the 1970s, so biological or geochemical proxies from sediment archives are needed to reconstruct UV-B irradiance received at the Earth surface beyond the experimental record. Recent developments have shown that the quantification of UV-B-absorbing compounds in pollen and spores have the potential to provide a continuous record of the solar-ultraviolet radiation received by plants. There is increasing interest in developing this proxy in palaeoclimatic and palaeoecological research. However, differences in interpretation exist between palaeoecologists, who are beginning to apply the proxy under various geological settings, and UV-B ecologists, who question whether a causal dose-response relationship of pollen and spore chemistry to UV-B irradiance has really been established. Here, we use a proxy-system modelling approach to systematically assess components of the pollen- and spore-based UV-B-irradiance proxy to ask how these differences can be resolved. We identify key unknowns and uncertainties in making inferences about past UV-B irradiance, from the pollen sensor, the sedimentary archive, and through the laboratory and experimental procedures in order to target priority areas of future work. We argue that an interdisciplinary approach, modifying methods used by plant ecologists studying contemporary responses to solar-UV-B radiation specifically to suit the needs of palaeoecological analyses, provides a way forward in developing the most reliable reconstructions for the UV-B irradiance received by plants across a range of timescales.

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.

An improved algorithm for fast resonant Mie scatter correction of infrared spectra of cells and tissues

Mie scattering effects create serious problems for the interpretation of Fourier-transform infrared spectroscopy spectra of single cells and tissues. During recent years, different techniques were proposed to retrieve pure absorbance spectra from spectra with Mie distortions. Recently, we published an iterative algorithm for correcting Mie scattering in spectra of single cells and tissues, which we called "the fast resonant Mie scatter correction algorithm." The algorithm is based on extended multiplicative signal correction (EMSC) and employs a meta-model for a parameter range of refractive index and size parameters. In the present study, we suggest several improvements of the algorithm. We demonstrate that the improved algorithm reestablishes chemical features of the measured spectra, and show that it tends away from the reference spectrum employed in the EMSC. We suggest strategies for choosing parameter ranges and other model parameters such as the number of principal components of the meta-model and the number of iterations. We demonstrate that the suggested algorithm optimizes an error function of the refractive index in a forward Mie model. We suggest a stop criterion for the iterative algorithm based on the error function of the forward model.

Chemical characterization and identification of Pinaceae pollen by infrared microspectroscopy

FTIR microspectroscopy, in combination with spectral averaging procedure, enables precise analysis of pollen grains for chemical characterization and identification studies of fresh and fossilised pollen in botany, ecology and palaeosciences. Infrared microspectroscopy (µFTIR) of Pinaceae pollen can provide valuable information on plant phenology, ecophysiology and paleoecology, but measurements are challenging, resulting in unreproducible spectra. The comparative analysis of µFTIR spectra belonging to morphologically different Pinaceae pollen, namely bisaccate Pinus and monosaccate Tsuga pollen, was conducted. The study shows that the main cause of spectral variability is non-radial symmetry of bisaccate pollen grains, while additional variation is caused by Mie scattering. Averaging over relatively small number of single pollen grain spectra (approx. 5-10) results with reproducible data on pollen chemical composition. The practical applicability of the µFTIR spectral averaging method has been demonstrated by the partial least-squares regression-based differentiation of the two closely related Pinus species with morphologically indistinguishable pollen: Pinus mugo (mountain pine) and Pinus sylvestris (Scots pine). The study has demonstrated that the µFTIR approach can be used for identification, differentiation and chemical characterization of pollen with complex morphology. The methodology enables analysis of fresh pollen, as well as fossil pollen from sediment core samples, and can be used in botany, ecology and paleoecology for study of biotic and abiotic effects on plants.

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.

FTIR imaging of the molecular burden around Aβ deposits in an early-stage 3-Tg-APP-PSP1-TAU mouse model of Alzheimer's disease

High spatial resolution FTIR imaging of early-stage 3-Tg-APP-PSP1-TAU mouse brain identifies molecular burden around Aβ deposits.

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

We performed high-resolution Fourier-transform infrared (FTIR) spectroscopy of a polymethyl methacrylate (PMMA) sphere of unknown size in the Mie scattering region. Apart from a slow, oscillatory structure (wiggles), which is due to an interference effect, the measured FTIR extinction spectrum exhibits a ripple structure, which is due to electromagnetic resonances. We fully characterize the underlying electromagnetic mode structure of the spectrum by assigning mode numbers to each of the ripples in the measured spectrum. We show that analyzing the ripple structure in the spectrum in the wavenumber region from about 3000  cm^-1 to 8000  cm^-1 allows us to determine both the unknown radius of the sphere and the PMMA index of refraction, which shows a strong frequency dependence in this infrared spectral region. While in this paper we focus on examining a PMMA sphere as an example, our method of determining the refractive index and its dispersion from infrared extinction spectra is generally applicable for the determination of the index of refraction of any transparent substance that can be shaped into micron-sized spheres.

Mie scatter corrections in single cell infrared microspectroscopy

Strong Mie scattering signatures hamper the chemical interpretation and multivariate analysis of the infrared microscopy spectra of single cells and tissues. During recent years, several numerical Mie scatter correction algorithms for the infrared spectroscopy of single cells have been published. In the paper at hand, we critically reviewed existing algorithms for the correction of Mie scattering and suggest improvements. We developed an iterative algorithm based on Extended Multiplicative Scatter Correction (EMSC), for the retrieval of pure absorbance spectra from highly distorted infrared spectra of single cells. The new algorithm uses the van de Hulst approximation formula for the extinction efficiency employing a complex refractive index. The iterative algorithm involves the establishment of an EMSC meta-model. While existing iterative algorithms for the correction of resonant Mie scattering employ three independent parameters for establishing a meta-model, we could decrease the number of parameters from three to two independent parameters, which reduced the calculation time for the Mie scattering curves for the iterative EMSC meta-model by a factor of 10. Moreover, by employing the Hilbert transform for evaluating the Kramers–Kronig relations based on a FFT algorithm in Matlab, we further improved the speed of the algorithm by a factor of 100. For testing the algorithm we simulate distorted apparent absorbance spectra by utilizing the exact theory for the scattering of infrared light at absorbing spheres, taking into account the high numerical aperture of infrared microscopes employed for the analysis of single cells and tissues. In addition, the algorithm was applied to measured absorbance spectra of single lung cancer cells.

A Multiscale Vibrational Spectroscopic Approach for Identification and Biochemical Characterization of Pollen

BACKGROUND

Analysis of pollen grains reveals valuable information on biology, ecology, forensics, climate change, insect migration, food sources and aeroallergens. Vibrational (infrared and Raman) spectroscopies offer chemical characterization of pollen via identifiable spectral features without any sample pretreatment. We have compared the level of chemical information that can be obtained by different multiscale vibrational spectroscopic techniques.

METHODOLOGY

Pollen from 15 different species of Pinales (conifers) were measured by seven infrared and Raman methodologies. In order to obtain infrared spectra, both reflectance and transmission measurements were performed on ground and intact pollen grains (bulk measurements), in addition, infrared spectra were obtained by microspectroscopy of multigrain and single pollen grain measurements. For Raman microspectroscopy measurements, spectra were obtained from the same pollen grains by focusing two different substructures of pollen grain. The spectral data from the seven methodologies were integrated into one data model by the Consensus Principal Component Analysis, in order to obtain the relations between the molecular signatures traced by different techniques.

RESULTS

The vibrational spectroscopy enabled biochemical characterization of pollen and detection of phylogenetic variation. The spectral differences were clearly connected to specific chemical constituents, such as lipids, carbohydrates, carotenoids and sporopollenins. The extensive differences between pollen of Cedrus and the rest of Pinaceae family were unambiguously connected with molecular composition of sporopollenins in pollen grain wall, while pollen of Picea has apparently higher concentration of carotenoids than the rest of the family. It is shown that vibrational methodologies have great potential for systematic collection of data on ecosystems and that the obtained phylogenetic variation can be well explained by the biochemical composition of pollen. Out of the seven tested methodologies, the best taxonomical differentiation of pollen was obtained by infrared measurements on bulk samples, as well as by Raman microspectroscopy measurements of the corpus region of the pollen grain. Raman microspectroscopy measurements indicate that measurement area, as well as the depth of focus, can have crucial influence on the obtained data.