Hole Localization in Bulk and 2D Lead-Halide Perovskites Studied by Time-Resolved Infrared Spectroscopy

Scattering and localization dynamics of charge carriers in the soft lattice of lead-halide perovskites impact polaron formation and recombination, which are key mechanisms of material function in optoelectronic devices. In this study, we probe the photoinduced lattice and carrier dynamics in perovskite thin films (CsFAPbX3, X = I, Br) using time-resolved infrared spectroscopy. We examine the CN stretching mode of formamidinium (FA) cations located within the lead-halide octahedra of the perovskite structure. Our investigation reveals the formation of an infrared mode due to spatial symmetry breaking within a hundred picoseconds in 3D perovskites. Experiments at cryogenic temperatures show much-reduced carrier localization, in agreement with a localization mechanism that is driven by the dynamic disorder. We extend our analysis to 2D perovskites, where the precise nature of charge carriers is uncertain. Remarkably, the signatures of charge localization we found in bulk perovskites are not observed for 2D Ruddlesden-Popper perovskites ((HexA)2FAPb2I7). This observation implies that the previously reported stabilization of free charge carriers in these materials follows different mechanisms than polaron formation in bulk perovskites. Through the exploration of heterostructures with electron/hole excess, we provide evidence that holes drive the formation of the emerging infrared mode.

Analysing micro- and nanoplastics with cutting-edge infrared spectroscopy techniques: a critical review

The escalating prominence of micro- and nanoplastics (MNPs) as emerging anthropogenic pollutants has sparked widespread scientific and public interest. These minuscule particles pervade the global environment, permeating drinking water and food sources, prompting concerns regarding their environmental impacts and potential risks to human health. In recent years, the field of MNP research has witnessed the development and application of cutting-edge infrared (IR) spectroscopic instruments. This review focuses on the recent application of advanced IR spectroscopic techniques and relevant instrumentation to analyse MNPs. A comprehensive literature search was conducted, encompassing articles published within the past three years. The findings revealed that Fourier transform infrared (FTIR) spectroscopy stands as the most used technique, with focal plane array FTIR (FPA-FTIR) representing the cutting edge in FTIR spectroscopy. The second most popular technique is quantum cascade laser infrared (QCL-IR) spectroscopy, which has facilitated rapid analysis of plastic particles. Following closely is optical photothermal infrared (O-PTIR) spectroscopy, which can furnish submicron spatial resolution. Subsequently, there is atomic force microscopy-based infrared (AFM-IR) spectroscopy, which has made it feasible to analyse MNPs at the nanoscale level. The most advanced IR instruments identified in articles covered in this review were compared. Comparison metrics encompass substrates/filters, data quality, spatial resolution, data acquisition speed, data processing and cost. The limitations of these IR instruments were identified, and recommendations to address these limitations were proposed. The findings of this review offer valuable guidance to MNP researchers in selecting suitable instrumentation for their research experiments, thereby facilitating advancements in research aimed at enhancing our understanding of the environmental and human health risks associated with MNPs.

Multi-wavelength Raman microscopy of nickel-based electron transport in cable bacteria

Cable bacteria embed a network of conductive protein fibers in their cell envelope that efficiently guides electron transport over distances spanning up to several centimeters. This form of long-distance electron transport is unique in biology and is mediated by a metalloprotein with a sulfur-coordinated nickel (Ni) cofactor. However, the molecular structure of this cofactor remains presently unknown. Here, we applied multi-wavelength Raman microscopy to identify cell compounds linked to the unique cable bacterium physiology, combined with stable isotope labeling, and orientation-dependent and ultralow-frequency Raman microscopy to gain insight into the structure and organization of this novel Ni-cofactor. Raman spectra of native cable bacterium filaments reveal vibrational modes originating from cytochromes, polyphosphate granules, proteins, as well as the Ni-cofactor. After selective extraction of the conductive fiber network from the cell envelope, the Raman spectrum becomes simpler, and primarily retains vibrational modes associated with the Ni-cofactor. These Ni-cofactor modes exhibit intense Raman scattering as well as a strong orientation-dependent response. The signal intensity is particularly elevated when the polarization of incident laser light is parallel to the direction of the conductive fibers. This orientation dependence allows to selectively identify the modes that are associated with the Ni-cofactor. We identified 13 such modes, some of which display strong Raman signals across the entire range of applied wavelengths (405-1,064 nm). Assignment of vibrational modes, supported by stable isotope labeling, suggest that the structure of the Ni-cofactor shares a resemblance with that of nickel bis(1,2-dithiolene) complexes. Overall, our results indicate that cable bacteria have evolved a unique cofactor structure that does not resemble any of the known Ni-cofactors in biology.

A review of concepts and methods for FTIR imaging of biomarker changes in the post-stroke brain

Stroke represents a core area of study in neurosciences and public health due to its global contribution toward mortality and disability. The intricate pathophysiology of stroke, including ischemic and hemorrhagic events, involves the interruption in oxygen and nutrient delivery to the brain. Disruption of these crucial processes in the central nervous system leads to metabolic dysregulation and cell death. Fourier transform infrared (FTIR) spectroscopy can simultaneously measure total protein and lipid content along with a number of key biomarkers within brain tissue that cannot be observed using conventional techniques. FTIR imaging provides the opportunity to visualize this information in tissue which has not been chemically treated prior to analysis, thus retaining the spatial distribution and in situ chemical information. Here we present a review of FTIR imaging methods for investigating the biomarker responses in the post-stroke brain.

Scattering correction for samples with cylindrical domains measured with polarized infrared spectroscopy

Scattering artifacts are one of the most common effects distorting transmission spectra in Fourier-Transform Infrared spectroscopy. Their increased impact, strongly diminishing the quantitative and qualitative power of IR spectroscopy, is especially observed for structures with a size comparable to the radiation wavelength. To tackle this problem, a wide range of preprocessing techniques based on the Extended Multiplicative Scattering Correction method was developed, using physical properties to remove scattering presence in the spectra. However, until recently those algorithms were mostly focused on spherically shaped samples, for example, cells. Here, an algorithm for samples with cylindrical domains is described, with additional implementation of a linearly polarized light case, which is crucial for the growing field of polarized IR imaging and spectroscopy. An open-source code with GPU based implementation is provided, with a calculation time of several seconds per spectrum. Optimizations done to improve the throughput of this algorithm allow the application of this method into the standard preprocessing pipeline of small datasets.

Virtual histology of Alzheimer's disease: Biometal entrapment within amyloid-β plaques allows for detection via X-ray phase-contrast imaging

Amyloid-β (Aβ) plaques from Alzheimer's Disease (AD) can be visualized ex vivo in label-free brain samples using synchrotron X-ray phase-contrast tomography (XPCT). However, for XPCT to be useful as a screening method for amyloid pathology, it is essential to understand which factors drive the detection of Aβ plaques. The current study was designed to test the hypothesis that Aβ-related contrast in XPCT could be caused by Aβ fibrils and/or by metals trapped in the plaques. Fibrillar and elemental compositions of Aβ plaques were probed in brain samples from different types of AD patients and AD models to establish a relationship between XPCT contrast and Aβ plaque characteristics. XPCT, micro-Fourier-Transform Infrared spectroscopy and micro-X-Ray Fluorescence spectroscopy were conducted on human samples (one genetic and one sporadic case) and on four transgenic rodent strains (mouse: APPPS1, ArcAβ, J20; rat: TgF344). Aβ plaques from the genetic AD patient were visible using XPCT, and had higher β-sheet content and higher metal levels than those from the sporadic AD patient, which remained undetected by XPCT. Aβ plaques in J20 mice and TgF344 rats appeared hyperdense on XPCT images, while they were hypodense with a hyperdense core in the case of APPPS1 and ArcAβ mice. In all four transgenic strains, β-sheet content was similar, while metal levels were highly variable: J20 (zinc and iron) and TgF344 (copper) strains showed greater metal accumulation than APPPS1 and ArcAβ mice. Hence, a hyperdense contrast formation of Aβ plaques in XPCT images was associated with biometal entrapment within plaques. STATEMENT OF SIGNIFICANCE: The role of metals in Alzheimer's disease (AD) has been a subject of continuous interest. It was already known that amyloid-β plaques (Aβ), the earliest hallmark of AD, tend to trap endogenous biometals like zinc, iron and copper. Here we show that this metal accumulation is the main reason why Aβ plaques are detected with a new technique called X-ray phase contrast tomography (XPCT). XPCT enables to map the distribution of Aβ plaques in the whole excised brain without labeling. In this work we describe a unique collection of four transgenic models of AD, together with a human sporadic and a rare genetic case of AD, thus exploring the full spectrum of amyloid contrast in XPCT.

Metasurface-Enhanced Mid-Infrared Spectrochemical Imaging of Tissues

Label-free and nondestructive mid-infrared vibrational hyperspectral imaging is an essential tissue analysis tool, providing spatially resolved biochemical information critical to understanding physiological and pathological processes. However, the chemically complex and spatially heterogeneous composition of tissue specimens and the inherently weak interaction of infrared light with biomolecules limit the analytical performance of infrared absorption spectroscopy. Here, an advanced mid-infrared spectrochemical tissue imaging modality is introduced using metasurfaces that support strong surface-localized electromagnetic fields to capture quantitative molecular maps of large-area murine brain tissue sections. The approach leverages polarization-multiplexed multi-resonance plasmonic metasurfaces to simultaneously detect various functional biomolecules. The surface-enhanced mid-infrared spectral imaging method eliminates the non-specific effects of bulk tissue morphology on quantitative spectral analysis and improves chemical selectivity. This study shows that metasurface enhancement increases the retrieval of amide I and II bands associated with protein secondary structures. Moreover, it is demonstrated that plasmonic metasurfaces enhance the chemical contrast in infrared images and enable detection of ultrathin tissue regions that are not otherwise visible to conventional mid-infrared spectral imaging. While this work uses murine brain tissue sections, the chemical imaging method is well-suited for other tissue types, which broadens its potential impact for translational research and clinical histopathology.

A Brief Review of FT-IR Spectroscopy Studies of Sphingolipids in Human Cells

In recent years, sphingolipids have attracted significant attention due to their pivotal role in cellular functions and physiological diseases. A valuable tool for investigating the characteristics of sphingolipids can be represented via FT-IR spectroscopy, generally recognized as a very powerful technique that provides detailed biochemical information on the examined sample with the unique properties of sensitivity and accuracy. In the present paper, some fundamental aspects of sphingolipid components of human cells are summarized, and the most relevant articles devoted to the FT-IR spectroscopic studies of sphingolipids are revised. A short description of different FT-IR experimental approaches adopted for investigating sphingolipids is also given, with details about the most commonly used data analysis procedures. The present overview of FT-IR investigations, although not exhaustive, attests to the relevant role this vibrational technique has played in giving significant insight into many aspects of this fascinating class of lipids.

Influence of measurement mode on the results of glioblastoma multiforme analysis with the FTIR microspectroscopy

Fourier Transform Infrared (FTIR) microspectroscopy is well known for its effectiveness in spectral and biochemical analyses of various materials. It enables to determine the sample biochemical composition by assigning detected frequencies, characteristic for functional groups of main biological macromolecules. In analysis of tissue sections one of two measurement modes, namely transmission and transflection, is usually applied. The first one has relatively straightforward geometry, hence it is considered to be more precise and accurate. However, IR-transparent media are very fragile and expensive. Transflection does not require expensive substrates, but is more prone to disruptive influence of Mie scattering as well as electric field standing wave effect. The excessive comparison of spectra' characteristics, obtained via both measurement modes, was performed in this paper. By the means of Mann-Whitney non-parametrical U test and PCA, the comparison of results obtained with both modes and assessment of usefulness of IR spectra obtained with transmission and transflection modes to differentiate between healthy and GBM-affected tissue, were performed. The main objective of the presented research is to compare the results of FTIR analysis of unfixed biological samples performed with transflection and transmission mode. In frame of the study we demonstrated the discrepancies between results of biochemical analysis performed based on data obtained with transmission and transflection. Such observation suggests that caution should be taken in drawing conclusions from the results obtained with transflection geometry, as its more prone to disruptive effects. Despite that, IR spectra developed with both modes allowed to distinguish GBM area from healthy tissue, which proves their diagnostic potential. Especially, application of the ME-EMSC correction of spectra before PCA enhances the performance of both methods to distinguish the analysed tissue areas.

Cell Phase Identification in a Three-Dimensional Engineered Tumor Model by Infrared Spectroscopic Imaging

Cell cycle progression plays a vital role in regulating proliferation, metabolism, and apoptosis. Three-dimensional (3D) cell cultures have emerged as an important class of in vitro disease models, and incorporating the variation occurring from cell cycle progression in these systems is critical. Here, we report the use of Fourier transform infrared (FT-IR) spectroscopic imaging to identify subtle biochemical changes within cells, indicative of the G1/S and G2/M phases of the cell cycle. Following previous studies, we first synchronized samples from two-dimensional (2D) cell cultures, confirmed their states by flow cytometry and DNA quantification, and recorded spectra. We determined two critical wavenumbers (1059 and 1219 cm-1) as spectral indicators of the cell cycle for a set of isogenic breast cancer cell lines (MCF10AT series). These two simple spectral markers were then applied to distinguish cell cycle stages in a 3D cell culture model using four cell lines that represent the main stages of cancer progression from normal cells to metastatic disease. Temporal dependence of spectral biomarkers during acini maturation validated the hypothesis that the cells are more proliferative in the early stages of acini development; later stages of the culture showed stability in the overall composition but unique spatial differences in cells in the two phases. Altogether, this study presents a computational and quantitative approach for cell phase analysis in tissue-like 3D structures without any biomarker staining and provides a means to characterize the impact of the cell cycle on 3D biological systems and disease diagnostic studies using IR imaging.

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.

Protein conformation characterization via a silicon resonator-based optical sensor based on the combination of wavelength interrogation and dual polarization detection

Protein conformational abnormality causes cell malfunction. Conformational change of amyloid protein causes neuron malfunction, which renders "protein conformational disease" Alzheimer's disease. Dual polarization interferometry enables to provide one-dimensional structure of a protein biolayer via deconvolution of interference patterns, which in turn is interpreted as the protein molecule conformation. However, it is still challenging to avoid interference patterns becoming faint and obscure sometimes. Resonance wavelength response to the biolayer structure can achieve a very low detection limit due to inherent high Q factor of an optical resonator. Here, we introduce the concept of combining dual polarization detection with wavelength interrogation via a simple and compact resonator-based optical biosensor. Biolayer were probed by the wave of dual polarization and its opto-geometrical parameters were resolved into resonance wavelength shift. Because protein molecule with distinct conformation produced a biolayer with unique thickness and mass density. Amyloid proteins in monomeric and dimeric morphology were respectively characterized. This concept enables protein conformation characterization in an easy and direct paradigm and provides a desirable sensing performance due to sensitive resonance response in the form of the sharp resonance profile occurring in a nonoverlapping spectrum.

Contributions of vibrational spectroscopy to virology: A review

Vibrational spectroscopic techniques, both infrared absorption and Raman scattering, are high precision, label free analytical techniques which have found applications in fields as diverse as analytical chemistry, pharmacology, forensics and archeometrics and, in recent times, have attracted increasing attention for biomedical applications. As analytical techniques, they have been applied to the characterisation of viruses as early as the 1970 s, and, in the context of the coronavirus disease 2019 (COVID-19) pandemic, have been explored in response to the World Health Organisation as novel methodologies to aid in the global efforts to implement and improve rapid screening of viral infection. This review considers the history of the application of vibrational spectroscopic techniques to the characterisation of the morphology and chemical compositions of viruses, their attachment to, uptake by and replication in cells, and their potential for the detection of viruses in population screening, and in infection response monitoring applications. Particular consideration is devoted to recent efforts in the detection of severe acute respiratory syndrome coronavirus 2, and monitoring COVID-19.

Molecular Dynamics and Structure of Poly(Methyl Methacrylate) Chains Grafted from Barium Titanate Nanoparticles

Core−shell nanocomposites comprising barium titanate, BaTiO₃ (BTO), and poly(methyl methacrylate) (PMMA) chains grafted from its surface with varied grafting densities were prepared. BTO nanocrystals are high-k inorganic materials, and the obtained nanocomposites exhibit enhanced dielectric permittivity, as compared to neat PMMA, and a relatively low level of loss tangent in a wide range of frequencies. The impact of the molecular dynamics, structure, and interactions of the BTO surface on the polymer chains was investigated. The nanocomposites were characterized by broadband dielectric and vibrational spectroscopies (IR and Raman), transmission electron microscopy, differential scanning calorimetry, and nuclear magnetic resonance. The presence of ceramic nanoparticles in core–shell composites slowed down the segmental dynamic of PMMA chains, increased glass transition temperature, and concurrently increased the thermal stability of the organic part. It was also evidenced that, in addition to segmental dynamics, local β relaxation was affected. The grafting density influenced the self-organization and interactions within the PMMA phase, affecting the organization on a smaller size scale of polymeric chains. This was explained by the interaction of the exposed surface of nanoparticles with polymer chains.

Scalable Synthesis and Characterization of Multilayer γ-Graphyne, New Carbon Crystals with a Small Direct Band Gap

γ-Graphyne is the most symmetric sp²/sp¹ allotrope of carbon, which can be viewed as graphene uniformly expanded through the insertion of two-carbon acetylenic units between all the aromatic rings. To date, synthesis of bulk γ-graphyne has remained a challenge. We here report the synthesis of multilayer γ-graphyne through crystallization-assisted irreversible cross-coupling polymerization. A comprehensive characterization of this new carbon phase is described, including synchrotron powder X-ray diffraction, electron diffraction, lateral force microscopy, Raman spectroscopy, infrared spectroscopy, and cyclic voltammetry. Experiments indicate that γ-graphyne is a 0.48 eV band gap semiconductor, with a hexagonal a-axis spacing of 6.88 Å and an interlayer spacing of 3.48 Å, which is consistent with theoretical predictions. The observed crystal structure has an aperiodic sheet stacking. The material is thermally stable up to 240 °C but undergoes transformation at higher temperatures. While conventional 2D polymerization and reticular chemistry rely on error correction through reversibility, we demonstrate that a periodic covalent lattice can be synthesized under purely kinetic control. The reported methodology is scalable and inspires extension to other allotropes of the graphyne family.

Multiresolution spectrally-encoded terahertz reflection imaging through a highly diffusive cloak

Turbid media, made of wavelength-scale inhomogeneous particles, can give rise to many significant imaging and spectroscopy challenges. The random variation of the refractive index within such media distorts the spherical wavefronts, resulting in smeared and speckly images. The scattering-induced artifacts can obscure the characteristic spectral fingerprints of the chemicals in a sample. This in turn prevents accurate chemical imaging and characterization of the materials cloaked with a diffusive medium. In this work, we present a novel computational technique for creating spatially- and spectrally-resolved chemical maps through a diffusive cloak using terahertz time-domain spectroscopy. We use the maximal overlap discrete wavelet transform to obtain a multiresolution spectral decomposition of THz extinction coefficients. We define a new spectroscopic concept dubbed the "bimodality coefficient spectrum" using the skewness and kurtosis of the spectral images. We demonstrate that broadband wavelet-based reconstruction of the bimodality coefficient spectrum can resolve the signature resonant frequencies through the scattering layers. Additionally, we show that our approach can achieve spectral images with diffraction-limited resolution. This technique can be used for stand-off characterization of materials and spectral imaging in nondestructive testing and biological applications.

Evaluation of Proton-Induced Biomolecular Changes in MCF-10A Breast Cells by Means of FT-IR Microspectroscopy

Radiotherapy (RT) with accelerated beams of charged particles (protons and carbon ions), also known as hadrontherapy, is a treatment modality that is increasingly being adopted thanks to the several benefits that it grants compared to conventional radiotherapy (CRT) treatments performed by means of high-energy photons/electrons. Hence, information about the biomolecular effects in exposed cells caused by such particles is needed to better realize the underlying radiobiological mechanisms and to improve this therapeutic strategy. To this end, Fourier transform infrared microspectroscopy (μ-FT-IR) can be usefully employed, in addition to long-established radiobiological techniques, since it is currently considered a helpful tool for examining radiation-induced cellular changes. In the present study, MCF-10A breast cells were chosen to evaluate the effects of proton exposure using μ-FT-IR. They were exposed to different proton doses and fixed at various times after exposure to evaluate direct effects due to proton exposure and the kinetics of DNA damage repair. Irradiated and control cells were examined in transflection mode using low-e substrates that have been recently demonstrated to offer a fast and direct way to examine proton-exposed cells. The acquired spectra were analyzed using a deconvolution procedure and a ratiometric approach, both of which showed the different contributions of DNA, protein, lipid, and carbohydrate cell components. These changes were particularly significant for cells fixed 48 and 72 h after exposure. Lipid changes were related to variations in membrane fluidity, and evidence of DNA damage was highlighted. The analysis of the Amide III band also indicated changes that could be related to different enzyme contributions in DNA repair.

Automated analysis of microplastics based on vibrational spectroscopy: are we measuring the same metrics?

The traditional manual analysis of microplastics has been criticized for its labor-intensive, inaccurate identification of small microplastics, and the lack of uniformity. There are already three automated analysis strategies for microplastics based on vibrational spectroscopy: laser direct infrared (LDIR)-based particle analysis, Raman-based particle analysis, and focal plane array-Fourier transform infrared (FPA-FTIR) imaging. We compared their performances in terms of quantification, detection limit, size measurement, and material identification accuracy and speed by analyzing the same standard and environmental samples. LDIR-based particle analysis provides the fastest analysis speed, but potentially questionable material identification and quantification results. The number of particles smaller than 60 μm recognized by LDIR-based particle analysis is much less than that recognized by Raman-based particle analysis. Misidentification could occur due to the narrow tuning range from 1800 to 975 cm^-1 and dispersive artifact distortion of infrared spectra collected in reflection mode. Raman-based particle analysis has a submicrometer detection limit but should be cautiously used in the automated analysis of microplastics in environmental samples because of the strong fluorescence interference. FPA-FTIR imaging provides relatively reliable quantification and material identification for microplastics in environmental samples greater than 20 μm but might provide an imprecise description of the particle shapes. Optical photothermal infrared (O-PTIR) spectroscopy can detect submicron-sized environmental microplastics (0.5-5 μm) intermingled with a substantial amount of biological matrix; the resulting spectra are searchable in infrared databases without the influence of fluorescence interference, but the process would need to be fully automated.

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.

Computer-Assisted Analysis of Microplastics in Environmental Samples Based on μFTIR Imaging in Combination with Machine Learning

The problem of automating the data analysis of microplastics following a spectroscopic measurement such as focal plane array (FPA)-based micro-Fourier transform infrared (FTIR), Raman, or QCL is gaining ever more attention. Ease of use of the analysis software, reduction of expert time, analysis speed, and accuracy of the result are key for making the overall process scalable and thus allowing nonresearch laboratories to offer microplastics analysis as a service. Over the recent years, the prevailing approach has been to use spectral library search to automatically identify spectra of the sample. Recent studies, however, showed that this approach is rather limited in certain contexts, which led to developments for making library searches more robust but on the other hand also paved the way for introducing more advanced machine learning approaches. This study describes a model-based machine learning approach based on random decision forests for the analysis of large FPA-μFTIR data sets of environmental samples. The model can distinguish between more than 20 different polymer types and is applicable to complex matrices. The performance of the model under these demanding circumstances is shown based on eight different data sets. Further, a Monte Carlo cross validation has been performed to compute error rates such as sensitivity, specificity, and precision.

Steam disinfection releases micro(nano)plastics from silicone-rubber baby teats as examined by optical photothermal infrared microspectroscopy

Silicone-rubber baby teats used to bottle-feed infants are frequently disinfected by moist heating. However, infant exposure to small microplastics (<10 μm) potentially released from the heated teats by hydrothermal decomposition has not been studied, owing to the limitations of conventional spectroscopy in visualizing microplastic formation and in characterizing the particles at the submicrometre scale. Here both the surfaces of silicone teats subjected to steam disinfection and the wash waters of the steamed teats were analysed using optical-photothermal infrared microspectroscopy. This new technique revealed submicrometre-resolved steam etching on and chemical modification of the teat surface. Numerous flake- or oil-film-shaped micro(nano)plastics (MNPs) (in the size range of 0.6-332 μm) presented in the wash waters, including cyclic and branched polysiloxanes or polyimides, which were generated by the steam-induced degradation of the base polydimethylsiloxane elastomer and the polyamide resin additive. The results indicated that by the age of one year, a baby could ingest >0.66 million elastomer-derived micro-sized plastics (MPs) (roughly 81% in 1.5-10 μm). Global MP emission from teat disinfection may be as high as 5.2 × 10¹³ particles per year. Our findings highlight an entry route for surface-active silicone-rubber-derived MNPs into both the human body and the environment. The health and environmental risks of the particles are as yet unknown.

Infrared micro-spectroscopy coupled with multivariate and machine learning techniques for cancer classification in tissue: a comparison of classification method, performance, and pre-processing technique

A meta-analysis of various multivariate/Machine Learning (ML) classifiers trained on IR Micro-spectroscopy tissue datasets for cancer classification are directly compared using a calculated F1-Score metric alongside study pre-processing techniques.

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.

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.

Monitoring the effects of chemical stimuli on live cells with metasurface-enhanced infrared reflection spectroscopy

Infrared spectroscopy has found wide applications in the analysis of biological materials. A more recent development is the use of engineered nanostructures - plasmonic metasurfaces - as substrates for metasurface-enhanced infrared reflection spectroscopy (MEIRS). Here, we demonstrate that strong field enhancement from plasmonic metasurfaces enables the use of MEIRS as a highly informative analytic technique for real-time monitoring of cells. By exposing live cells cultured on a plasmonic metasurface to chemical compounds, we show that MEIRS can be used as a label-free phenotypic assay for detecting multiple cellular responses to external stimuli: changes in cell morphology, adhesion, and lipid composition of the cellular membrane, as well as intracellular signaling. Using a focal plane array detection system, we show that MEIRS also enables spectro-chemical imaging at the single-cell level. The described metasurface-based all-optical sensor opens the way to a scalable, high-throughput spectroscopic assay for live cells.

Using FTIR Imaging to Investigate Silk Fibroin-Based Materials

The secondary structures of silk fibroin (SF) are critical in the determination of the mechanical properties of the animal silks. Different characterization techniques, such as X-ray diffraction, nuclear magnetic resonance, Raman spectroscopy, and Fourier transform infrared (FTIR) technique, have been applied to study the secondary structure of animal silks. Among these techniques, FTIR is most widely used as it is sensitive to all secondary structures of proteins. Especially with the development of FTIR imaging, it is now possible to image the secondary structures of proteins at the micrometer scale, so as to understand the spatial distribution of proteins and the interaction of proteins with other materials at specific locations of interest. In this chapter, we present the methods and protocols of FTIR imaging to silk protein-based materials. We primarily introduce how to set up the instruments and accessories, as well as how to choose the appropriate imaging methods and sample preparation methods according to sample morphologies. The critical protocols for data analysis are also introduced in the last section.

Mid-infrared spectral classification of endometrial cancer compared to benign controls in serum or plasma samples

This study demonstrates a discrimination of endometrial cancer versus (non-cancerous) benign controls based on mid-infrared (MIR) spectroscopy of dried plasma or serum liquid samples. A detailed evaluation was performed using four discriminant methods (LDA, QDA, kNN or SVM) to execute the classification task. The discriminant methods used in the study comprised methods that are widely used in the statistics (LDA and QDA) and machine learning literature (kNN and SVM). Of particular interest, is the impact of discrimination when presented with spectral data from a section of the bio-fingerprint region (1430 cm-1 to 900 cm-1) in contrast to the more extended bio-fingerprint region used here (1800 cm-1 to 900 cm-1). Quality metrics used were the misclassification rate, sensitivity, specificity, and Matthew's correlation coefficient (MCC). For plasma (with spectral data ranging from 1430 cm-1 to 900 cm-1), the best performing classifier was kNN, which achieved a sensitivity, specificity and MCC of 0.865 ± 0.043, 0.865 ± 0.023 and 0.762 ± 0.034, respectively. For serum (in the same wavenumber range), the best performing classifier was LDA, achieving a sensitivity, specificity and MCC of 0.899 ± 0.023, 0.763 ± 0.048 and 0.664 ± 0.067, respectively. For plasma (with spectral data ranging from 1800 cm-1 to 900 cm-1), the best performing classifier was SVM, with a sensitivity, specificity and MCC of 0.993 ± 0.010, 0.815 ± 0.000 and 0.815 ± 0.010, respectively. For serum (in the same wavenumber range), QDA performed best achieving a sensitivity, specificity and MCC of 0.852 ± 0.023, 0.700 ± 0.162 and 0.557 ± 0.012, respectively. Our findings demonstrate that even when a section of the bio-fingerprint region has been removed, good classification of endometrial cancer versus non-cancerous controls is still maintained. These findings suggest the potential of a MIR screening tool for endometrial cancer screening.

A representation learning approach for recovering scatter-corrected spectra from Fourier-transform infrared spectra of tissue samples

Infrared spectra obtained from cell or tissue specimen have commonly been observed to involve a significant degree of scattering effects, often Mie scattering, which probably overshadows biochemically relevant spectral information by a nonlinear, nonadditive spectral component in Fourier transform 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. Using Bayesian machine learning approaches, our approach unveils model uncertainty that coincides with a band shift in the amide I region that occurs when scattering is removed computationally based on an established physical model. Furthermore, our proposed method overcomes the trade-off between computation time and the corrected spectrum being biased towards an artificial reference spectrum.

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.

Fourier Transform Infrared Spectroscopy in Oral Cancer Diagnosis

Oral cancer is one of the most common cancers worldwide. Despite easy access to the oral cavity and significant advances in treatment, the morbidity and mortality rates for oral cancer patients are still very high, mainly due to late-stage diagnosis when treatment is less successful. Oral cancer has also been found to be the most expensive cancer to treat in the United States. Early diagnosis of oral cancer can significantly improve patient survival rate and reduce medical costs. There is an urgent unmet need for an accurate and sensitive molecular-based diagnostic tool for early oral cancer detection. Fourier transform infrared spectroscopy has gained increasing attention in cancer research due to its ability to elucidate qualitative and quantitative information of biochemical content and molecular-level structural changes in complex biological systems. The diagnosis of a disease is based on biochemical changes underlying the disease pathology rather than morphological changes of the tissue. It is a versatile method that can work with tissues, cells, or body fluids. In this review article, we aim to summarize the studies of infrared spectroscopy in oral cancer research and detection. It provides early evidence to support the potential application of infrared spectroscopy as a diagnostic tool for oral potentially malignant and malignant lesions. The challenges and opportunities in clinical translation are also discussed.

FT-IR Transflection Micro-Spectroscopy Study on Normal Human Breast Cells after Exposure to a Proton Beam

Fourier transform infrared micro-spectroscopy (μ-FT-IR) is nowadays considered a valuable tool for investigating the changes occurring in human cells after exposure to ionizing radiation. Recently, considerable attention has been devoted to the use of this optical technique in the study of cells exposed to proton beams, that are being increasingly adopted in cancer therapy. Different experimental configurations are used for proton irradiation and subsequent spectra acquisition. To facilitate the use of μ-FT-IR, it may be useful to investigate new experimental approaches capable of speeding up and simplifying the irradiation and measurements phases. Here, we propose the use of low-e-substrates slides for cell culture, allowing the irradiation and spectra acquisition in transflection mode in a fast and direct way. In recent years, there has been a wide debate about the validity of these supports, but many researchers agree that the artifacts due to the presence of the electromagnetic standing wave effects are negligible in many practical cases. We investigated human normal breast cells (MCF-10 cell line) fixed immediately after the irradiation with graded proton radiation doses (0, 0.5, 2, and 4 Gy). The spectra obtained in transflection geometry showed characteristics very similar to those present in the spectra acquired in transmission geometry and confirm the validity of the chosen approach. The analysis of spectra indicates the occurrence of significant changes in DNA and lipids components of cells. Modifications in protein secondary structure are also evidenced.

Interrogation of IDH1 Status in Gliomas by Fourier Transform Infrared Spectroscopy

Mutations in the isocitrate dehydrogenase 1 (IDH1) gene are found in a high proportion of diffuse gliomas. The presence of the IDH1 mutation is a valuable diagnostic, prognostic and predictive biomarker for the management of patients with glial tumours. Techniques involving vibrational spectroscopy, e.g., Fourier transform infrared (FTIR) spectroscopy, have previously demonstrated analytical capabilities for cancer detection, and have the potential to contribute to diagnostics. The implementation of FTIR microspectroscopy during surgical biopsy could present a fast, label-free method for molecular genetic classification. For example, the rapid determination of IDH1 status in a patient with a glioma diagnosis could inform intra-operative decision-making between alternative surgical strategies. In this study, we utilized synchrotron-based FTIR microanalysis to probe tissue microarray sections from 79 glioma patients, and distinguished the positive class (IDH1-mutated) from the IDH1-wildtype glioma, with a sensitivity and specificity of 82.4% and 83.4%, respectively. We also examined the ability of attenuated total reflection (ATR)-FTIR spectroscopy in detecting the biomolecular events and global epigenetic and metabolic changes associated with mutations in the IDH1 enzyme, in blood serum samples collected from an additional 72 brain tumour patients. Centrifugal filtration enhanced the diagnostic ability of the classification models, with balanced accuracies up to ~69%. Identification of the molecular status from blood serum prior to biopsy could further direct some patients to alternative treatment strategies.

Deep convolutional neural network recovers pure absorbance spectra from highly scatter-distorted spectra of cells

Infrared spectroscopy of cells and tissues is prone to Mie scattering distortions, which grossly obscure the relevant chemical signals. 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. However, the algorithm is computationally expensive and the correction of large infrared imaging datasets requires weeks of computations. 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. 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. One DSAE was trained on the spectra, which were corrected with different reference spectra and validated on independent test data. The DSAE outperformed the ME-EMSC correction in terms of speed, robustness, and noise levels. We confirm that the same chemical information is contained in the DSAE corrected spectra as in the spectra corrected with ME-EMSC.

Influence of interference effects on the spectral quality and histological classification by FT-IR imaging in transflection geometry

Infrared (IR) imaging can be used for fast, accurate and non-destructive pathology recognition of biopsies when supported by machine learning algorithms. Transflection mode of measurements has the potential to be translated into the clinic due to economic reasons of large-scale imaging with the need for inexpensive substrates. Unfortunately, in this mode spectral distortions originating from light interference appear. Due to this fact transmission measurement mode is more frequently used in pathology recognition. Nevertheless, this measurement mode also is not devoid of spectral distortion effects like scattering. However, this effect is better understood and there are preprocessing algorithms to minimize it. In this work, we investigated the influence of interference effects on spectral quality of pancreatic tissues measured in transmission and transflection mode with Fourier tranform IR (FT-IR) microscopy using samples embedded with and without paraffin. The removal of paraffin leads to an altered magnitude of interference in transflection and provides a platform for a detailed analysis of its effect on the spectra of biological material, since the same sample is measured with different interference conditions. Moreover, the potential of transflection mode measurements in histological classification of analyzed samples was investigated and compared with classification results for transmission mode.

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.

Detection of macromolecular inversion-induced structural changes in osteosarcoma cells by FTIR microspectroscopy

Fourier transform infrared (FTIR) microspectroscopy provides a biochemical fingerprint of the cells. In this study, chemical changes in 143B osteosarcoma cells were investigated using FTIR analysis of cancer cells after their treatment with polymeric invertible micellar assemblies (IMAs) and curcumin-loaded IMAs and compared with untreated osteosarcoma cells. A comprehensive principal component analysis (PCA) was applied to analyze the FTIR results and confirm noticeable changes in cell surface chemical structures in the fingerprint regions of 1480-900 cm^-1. The performed clustering shows visible differences for all investigated groups of cancer cells. It is demonstrated that a combination of FTIR microspectroscopy with PCA can be an efficient approach in determining interactions of osteosarcoma cells and drug-loaded polymer micellar assemblies. Graphical abstract.

Identifying muscle hemorrhage in rat cadavers with advanced decomposition by FT-IR microspectroscopy combined with chemometrics

The identification of muscle hemorrhage in a cadaver that is in an advanced stage of decomposition is an important but challenging task. Our study investigated whether Fourier transform infrared (FT-IR) microspectroscopy in conjunction with chemometrics could identify muscle hemorrhage using rat cadavers with advanced decomposition. In this study, an intramuscular blood injection method, instead of a mechanical injury method, was used to construct a muscle hemorrhage model, and the modeling idea of muscle hemorrhage identification was to discriminate and classify hemoglobin-leaking myofibrils from negative myofibrils. First, the optical images of hematoxylin/eosin (H&E) stained hemorrhagic muscle at different postmortem intervals (PMIs) were observed and showed that the morphological features of whole erythrocytes disappeared since the PMI of 4 d. Subsequently, principle component analysis (PCA) was performed and indicated that the biochemical differences in protein structures between fresh erythrocytes and myofibrils can be detected by the IR spectroscopic method. Ultimately, several classification models based on the partial least square discriminant analysis (PLS-DA) algorithm were successfully constructed for different PMIs and PMI ranges and achieved great prediction performances in external validations. This preliminary study demonstrates the feasibility of using FT-IR microspectroscopy combined with chemometrics as a potential approach for identifying muscle hemorrhage in cadavers with advanced decomposition for offering vital evidences in judicial process.

A de-waxing methodology for scanning probe microscopy

A de-waxing protocol that successfully removes paraffin from tissue microarray (TMA) cores of fixed tissue obtained from oral cancer is described. The success of the protocol is demonstrated by the comparison of Fourier transform infrared (FTIR) results obtained on paraffin-embedded and de-waxed tissue and the absence of any significant correlations between infrared scanning near-field optical microscopy (SNOM) images of de-waxed tissue obtained at the three main paraffin IR peaks. The success of the protocol in removing paraffin from tissue is also demonstrated by images obtained with scanning electron microscopy (SEM) and by energy dispersive spectra (EDS) of a de-waxed CaF2 disc which shows no significant contribution from carbon. The FTIR spectra of the de-waxed TMA core overlaps that obtained from OE19 oesophageal cancer cells which had never been exposed to paraffin.

Resolution Enhancement in Wide-Field IR Imaging and Time-Domain Spectroscopy Using Dielectric Microspheres

Wide-field imaging through dielectric microspheres has emerged in recent years as a simple and effective approach for generating super-resolution images at visible wavelengths. We present, to our knowledge, the first demonstration that dielectric microspheres can be used in a wide-field infrared (IR) microscope to enhance the far field resolution. We have observed a substantial improvement in resolution and magnification when images are collected through polystyrene microspheres. In addition, we demonstrate that spectroscopic imaging with a pulse-shaper based femtosecond mid-IR laser system is possible through the dielectric microspheres, which is a promising first step toward applying this technique to ultrafast IR imaging methods such as pump-probe and 2DIR microscopy.

Tutorial: multivariate classification for vibrational spectroscopy in biological samples

Vibrational spectroscopy techniques, such as Fourier-transform infrared (FTIR) and Raman spectroscopy, have been successful methods for studying the interaction of light with biological materials and facilitating novel cell biology analysis. Spectrochemical analysis is very attractive in disease screening and diagnosis, microbiological studies and forensic and environmental investigations because of its low cost, minimal sample preparation, non-destructive nature and substantially accurate results. However, there is now an urgent need for multivariate classification protocols allowing one to analyze biologically derived spectrochemical data to obtain accurate and reliable results. Multivariate classification comprises discriminant analysis and class-modeling techniques where multiple spectral variables are analyzed in conjunction to distinguish and assign unknown samples to pre-defined groups. The requirement for such protocols is demonstrated by the fact that applications of deep-learning algorithms of complex datasets are being increasingly recognized as critical for extracting important information and visualizing it in a readily interpretable form. Hereby, we have provided a tutorial for multivariate classification analysis of vibrational spectroscopy data (FTIR, Raman and near-IR) highlighting a series of critical steps, such as preprocessing, data selection, feature extraction, classification and model validation. This is an essential aspect toward the construction of a practical spectrochemical analysis model for biological analysis in real-world applications, where fast, accurate and reliable classification models are fundamental.

Optical Photothermal Infrared Microspectroscopy with Simultaneous Raman - A New Non-Contact Failure Analysis Technique for Identification of <10 μm Organic Contamination in the Hard Drive and other Electronics Industries

Optical Photothermal Infrared (O-PTIR) spectroscopy is a new technique for measuring submicron spatial resolution IR spectra with little or no sample preparation. This speeds up analysis times benefiting high-volume manufacturers through gaining insight into process contamination that occurs during development and on production lines. The ability to rapidly obtain far-field non-contact IR spectra at high spatial resolution facilitates the chemical identification of small organic contaminants that are not possible to measure with conventional Fourier transform infrared (FT-IR) microspectroscopy. The unique pump-probe system architecture also facilitates submicron simultaneous IR + Raman microscopy from the same spot with the same spatial resolution. With these unique capabilities, O-PTIR is finding utilization in the high-volume and high-value industries of high-tech componentry (memory storage, electronics, displays, etc.).

OCTAVVS: A Graphical Toolbox for High-Throughput Preprocessing and Analysis of Vibrational Spectroscopy Imaging Data

Modern vibrational spectroscopy techniques enable the rapid collection of thousands of spectra in a single hyperspectral image, allowing researchers to study spatially heterogeneous samples at micrometer resolution. A number of algorithms have been developed to correct for effects such as atmospheric absorption, light scattering by cellular structures and varying baseline levels. After preprocessing, spectra are commonly decomposed and clustered to reveal informative patterns and subtle spectral changes. Several of these steps are slow, labor-intensive and require programming skills to make use of published algorithms and code. We here present a free and platform-independent graphical toolbox that allows rapid preprocessing of large sets of spectroscopic images, including atmospheric correction and a new algorithm for resonant Mie scattering with improved speed. The software also includes modules for decomposition into constituent spectra using the popular Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) algorithm, augmented by region-of-interest selection, as well as clustering and cluster annotation.

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.

Biomolecular and bioanalytical applications of infrared spectroscopy - A review

Infrared (IR; or mid-infrared, MIR; 4000-400 cm^-1; 2500-25,000 nm) spectroscopy has become one of the most powerful and versatile tools at the disposal of modern bioscience. Because of its high molecular specificity, applicability to wide variety of samples, rapid measurement and non-invasivity, IR spectroscopy forms a potent approach to elucidate qualitative and quantitative information from various kinds of biological material. For these reasons, it became an established bioanalytical technique with diverse applications. This work aims to be a comprehensive and critical review of the recent accomplishments in the field of biomolecular and bioanalytical IR spectroscopy. That progress is presented on a wider background, with fundamental characteristics, the basic principles of the technique outlined, and its scientific capability directly compared with other methods being used in similar fields (e.g. near-infrared, Raman, fluorescence). The article aims to present a complete examination of the topic, as it touches the background phenomena, instrumentation, spectra processing and data analytical methods, spectra interpretation and related information. To suit this goal, the article includes a tutorial information essential to obtain a thorough perspective of bio-related applications of the reviewed methodologies. The importance of the fundamental factors to the final performance and applicability of IR spectroscopy in various areas of bioscience is explained. This information is interpreted in critical way, with aim to gain deep understanding why IR spectroscopy finds extraordinarily intensive use in this remarkably diverse and dynamic field of research and utility. The major focus is placed on the diversity of the applications in which IR biospectroscopy has been established so far and those onto which it is expanding nowadays. This includes qualitative and quantitative analytical spectroscopy, spectral imaging, medical diagnosis, monitoring of biophysical processes, and studies of physicochemical properties and dynamics of biomolecules. The application potential of IR spectroscopy in light of the current accomplishments and the future prospects is critically evaluated and its significance in the progress of bioscience is comprehensively presented.

Regulation of fungal decomposition at single-cell level

Filamentous fungi play a key role as decomposers in Earth's nutrient cycles. In soils, substrates are heterogeneously distributed in microenvironments. Hence, individual hyphae of a mycelium may experience very different environmental conditions simultaneously. In the current work, we investigated how fungi cope with local environmental variations at single-cell level. We developed a method based on infrared spectroscopy that allows the direct, in-situ chemical imaging of the decomposition activity of individual hyphal tips. Colonies of the ectomycorrhizal Basidiomycete Paxillus involutus were grown on liquid media, while parts of colonies were allowed to colonize lignin patches. Oxidative decomposition of lignin by individual hyphae growing under different conditions was followed for a period of seven days. We identified two sub-populations of hyphal tips: one with low decomposition activity and one with much higher activity. Active cells secreted more extracellular polymeric substances and oxidized lignin more strongly. The ratio of active to inactive hyphae strongly depended on the environmental conditions in lignin patches, but was further mediated by the decomposition activity of entire mycelia. Phenotypic heterogeneity occurring between genetically identical hyphal tips may be an important strategy for filamentous fungi to cope with heterogeneous and constantly changing soil environments.

Super-Resolution Infrared Imaging of Polymorphic Amyloid Aggregates Directly in Neurons

Loss of memory during Alzheimer's disease (AD), a fatal neurodegenerative disorder, is associated with neuronal loss and the aggregation of amyloid proteins into neurotoxic β-sheet enriched structures. However, the mechanism of amyloid protein aggregation is still not well understood due to many challenges when studying the endogenous amyloid structures in neurons or in brain tissue. Available methods either require chemical processing of the sample or may affect the amyloid protein structure itself. Therefore, new approaches, which allow studying molecular structures directly in neurons, are urgently needed. A novel approach is tested, based on label-free optical photothermal infrared super-resolution microspectroscopy, to study AD-related amyloid protein aggregation directly in the neuron at sub-micrometer resolution. Using this approach, amyloid protein aggregates are detected at the subcellular level, along the neurites and strikingly, in dendritic spines, which has not been possible until now. Here, a polymorphic nature of amyloid structures that exist in AD transgenic neurons is reported. Based on the findings of this work, it is suggested that structural polymorphism of amyloid proteins that occur already in neurons may trigger different mechanisms of AD progression.

Noise-free simulation of an FT-IR imaging hyperspectral dataset of pancreatic biopsy core bound by experiment

A noise-free hyperspectral FT-IR imaging dataset of a pancreatic tissue core was simulated based on experimental data that allows to test the performance of various data analysis and processing algorithms. A set of experimental noise levels was also added and used for denoising approaches comparison, which due to the noise-free reference signal enables to truly observe signal distortion caused by different approaches.

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.

Is Infrared Spectroscopy Ready for the Clinic?

Fourier transform-infrared spectroscopy (FT-IR) represents an attractive molecular diagnostic modality for translation to the clinic, where comprehensive chemical profiling of biological samples may revolutionize a myriad of pathways in clinical settings. Principally, FT-IR provides a rapid, cost-effective platform to obtain a molecular fingerprint of clinical samples based on vibrational transitions of chemical bonds upon interaction with infrared light. To date, considerable research activities have demonstrated competitive to superior performance of FT-IR strategies in comparison to conventional techniques, with particular promise for earlier, accessible disease diagnostics, thereby improving patient outcomes. However, amidst the changing healthcare landscape in times of aging populations and increased prevalence of cancer and chronic disease, routine adoption of FT-IR within clinical laboratories has remained elusive. Hence, this perspective shall outline the significant clinical potential of FT-IR diagnostics and subsequently address current barriers to translation from the perspective of all stakeholders, in the context of biofluid, histopathology, cytology, microbiology, and biomarker discovery frameworks. Thereafter, future perspectives of FT-IR for healthcare will be discussed, with consideration of recent technological advances that may facilitate future clinical translation.