Analysis of Allergenic Pollen by FTIR Microspectroscopy

Prediction of Deoxynivalenol Contamination in Wheat via Infrared Attenuated Total Reflection Spectroscopy and Multivariate Data Analysis

The climate crisis further exacerbates the challenges for food production. For instance, the increasingly unpredictable growth of fungal species in the field can lead to an unprecedented high prevalence of several mycotoxins, including the most important toxic secondary metabolite produced by Fusarium spp., i.e., deoxynivalenol (DON). The presence of DON in crops may cause health problems in the population and livestock. Hence, there is a demand for advanced strategies facilitating the detection of DON contamination in cereal-based products. To address this need, we introduce infrared attenuated total reflection (IR-ATR) spectroscopy combined with advanced data modeling routines and optimized sample preparation protocols. In this study, we address the limited exploration of wheat commodities to date via IR-ATR spectroscopy. The focus of this study was optimizing the extraction protocol for wheat by testing various solvents aligned with a greener and more sustainable analytical approach. The employed chemometric method, i.e., sparse partial least-squares discriminant analysis, not only facilitated establishing robust classification models capable of discriminating between high vs low DON-contaminated samples adhering to the EU regulatory limit of 1250 μg/kg but also provided valuable insights into the relevant parameters shaping these models.

Sparse wavelengths data in mid-infrared spectroscopy: Modelling approaches and channel sampling

Infrared instruments with smaller and cost-effective components such as bandpass filters, single channel detectors, and laser-based light sources are being developed to provide cheaper and faster analysis of biological samples. Such instruments often provide measurements in form of sparse data, which include a collection of single-frequency channels or a collection of channels covering very narrow spectral ranges, called here multi-frequency channels. To keep costs low, the number of channels needs to be kept at a minimum. However, modelling and preprocessing of sparse data needs enough channels to perform the task. The aim of this study therefore was to understand the effect of channels sampling on data modelling results and find optimal modelling algorithm for different type of sparse data. The sparse data was simulated using Fourier Transform Infrared spectra of milk and fungi. Regression models were established to predict fatty acid composition by partial least squares regression (PLSR), multiple linear regression (MLR) and random forest (RF) methods. We observe that PLSR algorithm is very well suited for sparse data such as multi-frequency channels: excellent calibration models were obtained with only three channels comprising three wavenumbers each. The results were comparable to results obtained with full spectra. MLR and RF in turn provided similarly good results using data with single-frequency channels requiring nine channels in total.

Influence of common palynological extraction treatments on ultraviolet absorbing compounds (UACs) in sub-fossil pollen and spores observed in FTIR spectra

Introduction

Biological life, atmospheric circulation and the Earth’s climate may be influenced by UV-B radiation. In plants, Ultraviolet Absorbing Compounds (UACs) are an indicator of UV-B exposure, and the abundance of UACs in pollen and spores of embryophytes is measurable using Fourier Transform Infrared (FTIR) micro-Spectroscopy. However, understanding the influence of common chemical pre-treatments on sub-fossil pollen and spores with a view to UV-B reconstruction still requires investigation.

Methods

Here, peat samples collected from a Late Holocene raised bog were treated with different chemicals (HCl, KOH, and acetolysis) for varying treatment times (up to 210 min). Pollen or spores of three common taxa (Alnus, Calluna and Sphagnum) were isolated and FTIR spectra obtained on individual grains. The spectra were compared to modern pollen and spore samples collected nearby.

Results

Spectra of modern and sub-fossil samples show several visible differences related to lipid and protoplast contents. The results of chemical treatments on sub-fossil pollen and spores reveal that HCl produced limited changes, while KOH and acetolysis altered several peaks, including the UAC-related aromatic peak at 1516 cm−1. We observe that all treatments modify the FTIR spectra to some degree, from weakest (HCl) to strongest (acetolysis). With respect to reduction of UAC peak area and treatment time, we observe in some cases a significant log-decay relationship, notably for KOH treatment on Calluna pollen and acetolysis on Sphagnum spores. Compared to untreated control samples, UAC peak area in Alnus, Calluna and Sphagnum reduced by 68%, 69% and 60% respectively, after only 3 min of acetolysis treatment. After 60 minutes of acetolysis treatment UAC peaks were reduced by 77%, 84% and 88%.

Discussion

Due to the potential for taxon-specific effects and significant reductions in UAC peak area even within short treatment times, our recommendation for future applications in palaeoecological studies on palynomorph chemistry is to avoid chemical digestions in the pollen extraction process in favour of separation methods including micro-sieving and density separation.

The Use of Constituent Spectra and Weighting in Extended Multiplicative Signal Correction in Infrared Spectroscopy

Extended multiplicative signal correction (EMSC) is a widely used preprocessing technique in infrared spectroscopy. EMSC is a model-based method favored for its flexibility and versatility. The model can be extended by adding constituent spectra to explicitly model-known analytes or interferents. This paper addresses the use of constituent spectra and demonstrates common pitfalls. It clarifies the difference between analyte and interferent spectra, and the importance of orthogonality between model spectra. Different normalization approaches are discussed, and the importance of weighting in the EMSC is demonstrated. The paper illustrates how constituent analyte spectra can be estimated, and how they can be used to extract additional information from spectral features. It is shown that the EMSC parameters can be used in both regression tasks and segmentation tasks.

Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm⁻¹, followed by peak normalization at 850 cm⁻¹ and preprocessing by MSC.

Application of Atmospheric Solids Analysis Probe Mass Spectrometry for the Taxonomic Analysis of Pollen

Globally, both managed and wild pollination services are unable to meet current rates of crop production and pollination demand. Wild pollination services could be improved through the reforestation of agricultural land margins, however plant–pollinator networks remain poorly understood and the collection of key floral traits a complex process. Herein, we consider the merits of pollen as a floral trait and the application of a rapid pollen comparison method in assessing whether pollen traits are conserved at a taxonomic level. Reporting the previously unstudied, pollen fingerprints of 18 Australian plant species, these are compared against the seed crop Daucus carota L. and two naturalised Brassica hybrids. Applying atmospheric solids analysis probe mass spectrometry (ASAP-MS) for rapid pollen fingerprinting, pollens are compared through non-metric multidimensional scaling (NMDS), Jaccard index correlation and hierarchical clustering. Demonstrating the merits of this analytical method for the grouping of potential revegetation flora, we identify key pollen similarities and differences that could correlate with wild pollinator preferences.

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.

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.

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.

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.

Grayscale representation of infrared microscopy images by extended multiplicative signal correction for registration with histological images

Fourier-transform infrared (FTIR) microspectroscopy is rounding the corner to become a label-free routine method for cancer diagnosis. In order to build infrared-spectral based classifiers, infrared images need to be registered with Hematoxylin and Eosin (H&E) stained histological images. While FTIR images have a deep spectral domain with thousands of channels carrying chemical and scatter information, the H&E images have only three color channels for each pixel and carry mainly morphological information. Therefore, image representations of infrared images are needed that match the morphological information in H&E images. In this paper, we propose a novel approach for representation of FTIR images based on extended multiplicative signal correction highlighting morphological features that showed to correlate well with morphological information in H&E images. Based on the obtained representations, we developed a strategy for global-to-local image registration for FTIR images and H&E stained histological images of parallel tissue sections.

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.

Chemical Analysis of Pollen by FT-Raman and FTIR Spectroscopies

Pollen studies are important for the assessment of present and past environment, including biodiversity, sexual reproduction of plants and plant-pollinator interactions, monitoring of aeroallergens, and impact of climate and pollution on wild communities and cultivated crops. Although information on chemical composition of pollen is of importance in all of those research areas, pollen chemistry has been rarely measured due to complex and time-consuming analyses. Vibrational spectroscopies, coupled with multivariate data analysis, have shown great potential for rapid chemical characterization, identification and classification of pollen. This study, comprising 219 species from all principal taxa of seed plants, has demonstrated that high-quality Raman spectra of pollen can be obtained by Fourier transform (FT) Raman spectroscopy. In combination with Fourier transform infrared spectroscopy (FTIR), FT-Raman spectroscopy is obtaining comprehensive information on pollen chemistry. Presence of all the main biochemical constituents of pollen, such as proteins, lipids, carbohydrates, carotenoids and sporopollenins, have been identified and detected in the spectra, and the study shows approaches to measure relative and absolute content of these constituents. The results show that FT-Raman spectroscopy has clear advantage over standard dispersive Raman measurements, in particular for measurement of pollen samples with high pigment content. FT-Raman spectra are strongly biased toward chemical composition of pollen wall constituents, namely sporopollenins and pigments. This makes Raman spectra complementary to FTIR spectra, which over-represent chemical constituents of the grain interior, such as lipids and carbohydrates. The results show a large variability in pollen chemistry for families, genera and even congeneric species, revealing wide range of reproductive strategies, from storage of nutrients to variation in carotenoids and phenylpropanoids. The information on pollen's chemical patterns for major plant taxa should be of outstanding value for various studies in plant biology and ecology, including aerobiology, palaeoecology, forensics, community ecology, plant-pollinator interactions, and climate effects on plants.

Extended multiplicative signal correction for FTIR spectral quality test and pre-processing of infrared imaging data

Spectral quality control is an important step in the analysis of infrared spectral data, however, often neglected in scientific literature. A frequently used quality test that was originally developed for infrared spectra of bacteria is provided by OPUS software from Bruker Optik GmbH. In this study, the OPUS quality test is applied to a large number of spectra of bacteria, yeasts and moulds and hyperspectral images of microorganisms. It is shown that the use of strict thresholds for parameters of the OPUS quality test leads to discarding too many spectra. A strategy for optimizing parameters thresholds of the OPUS quality test is provided and a novel approach for spectral quality testing based on extended multiplicative signal correction (EMSC) is suggested. For all the data sets considered in our study, the EMSC quality test is shown to be the best among different alternatives of OPUS quality test provided.

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.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) shows adaptation of grass pollen composition

MALDI time-of-flight mass spectrometry (MALDI-TOF MS) has become a widely used tool for the classification of biological samples. The complex chemical composition of pollen grains leads to highly specific, fingerprint-like mass spectra, with respect to the pollen species. Beyond the species-specific composition, the variances in pollen chemistry can be hierarchically structured, including the level of different populations, of environmental conditions or different genotypes. We demonstrate here the sensitivity of MALDI-TOF MS regarding the adaption of the chemical composition of three Poaceae (grass) pollen for different populations of parent plants by analyzing the mass spectra with partial least squares discriminant analysis (PLS-DA) and principal component analysis (PCA). Thereby, variances in species, population and specific growth conditions of the plants were observed simultaneously. In particular, the chemical pattern revealed by the MALDI spectra enabled discrimination of the different populations of one species. Specifically, the role of environmental changes and their effect on the pollen chemistry of three different grass species is discussed. Analysis of the group formation within the respective populations showed a varying influence of plant genotype on the classification, depending on the species, and permits conclusions regarding the respective rigidity or plasticity towards environmental changes.

Multivariate Analysis of MALDI Imaging Mass Spectrometry Data of Mixtures of Single Pollen Grains

Mixtures of pollen grains of three different species (Corylus avellana, Alnus cordata, and Pinus sylvestris) were investigated by matrix-assisted laser desorption/ionization time-of-flight imaging mass spectrometry (MALDI-TOF imaging MS). The amount of pollen grains was reduced stepwise from > 10 to single pollen grains. For sample pretreatment, we modified a previously applied approach, where any additional extraction steps were omitted. Our results show that characteristic pollen MALDI mass spectra can be obtained from a single pollen grain, which is the prerequisite for a reliable pollen classification in practical applications. MALDI imaging of laterally resolved pollen grains provides additional information by reducing the complexity of the MS spectra of mixtures, where frequently peak discrimination is observed. Combined with multivariate statistical analyses, such as principal component analysis (PCA), our approach offers the chance for a fast and reliable identification of individual pollen grains by mass spectrometry. Graphical Abstract ᅟ.

Extended Multiplicative Signal Correction Based Model Transfer for Raman Spectroscopy in Biological Applications

The chemometric analysis of Raman spectra of biological materials is hampered by spectral variations due to the instrumental setup that overlay the subtle biological changes of interest. Thus, an established statistical model may fail when applied to Raman spectra of samples acquired with a different device. Therefore, model transfer strategies are essential. Herein we report a model transfer approach based on extended multiplicative signal correction (EMSC). As opposed to existing model transfer methods, the EMSC based approach does not require group information on the secondary data sets, thus no extra measurements are required. The proposed model-transfer approach is a preprocessing procedure and can be combined with any method for regression and classification. The performance of EMSC as a model transfer method was demonstrated with a data set of Raman spectra of three Bacillus bacteria spore species ( B. mycoides, B. subtilis, and B. thuringiensis), which were acquired on four Raman spectrometers. A three-group classification by partial least-squares discriminant analysis (PLS-DA) with leave-one-device-out external cross-validation (LODCV) was performed. The mean sensitivities of the prediction on the independent device were considerably improved by the EMSC method. Besides the mean sensitivity, the model transferability was additionally benchmarked by the newly defined numeric markers: (1) relative Pearson's correlation coefficient and (2) relative Fisher's discriminant ratio. We show that these markers have led to consistent conclusions compared to the mean sensitivity of the classification. The advantage of our defined markers is that the evaluation is more effective and objective, because it is independent of the classification models.

A novel approach to study the morphology and chemistry of pollen in a phylogenetic context, applied to the halophytic taxon Nitraria L.(Nitrariaceae)

Nitraria is a halophytic taxon (i.e., adapted to saline environments) that belongs to the plant family Nitrariaceae and is distributed from the Mediterranean, across Asia into the south-eastern tip of Australia. This taxon is thought to have originated in Asia during the Paleogene (66-23 Ma), alongside the proto-Paratethys epicontinental sea. The evolutionary history of Nitraria might hold important clues on the links between climatic and biotic evolution but limited taxonomic documentation of this taxon has thus far hindered this line of research. Here we investigate if the pollen morphology and the chemical composition of the pollen wall are informative of the evolutionary history of Nitraria and could explain if origination along the proto-Paratethys and dispersal to the Tibetan Plateau was simultaneous or a secondary process. To answer these questions, we applied a novel approach consisting of a combination of Fourier Transform Infrared spectroscopy (FTIR), to determine the chemical composition of the pollen wall, and pollen morphological analyses using Light Microscopy (LM) and Scanning Electron Microscopy (SEM). We analysed our data using ordinations (principal components analysis and non-metric multidimensional scaling), and directly mapped it on the Nitrariaceae phylogeny to produce a phylomorphospace and a phylochemospace. Our LM, SEM and FTIR analyses show clear morphological and chemical differences between the sister groups Peganum and Nitraria. Differences in the morphological and chemical characteristics of highland species (Nitraria schoberi, N. sphaerocarpa, N. sibirica and N. tangutorum) and lowland species (Nitraria billardierei and N. retusa) are very subtle, with phylogenetic history appearing to be a more important control on Nitraria pollen than local environmental conditions. Our approach shows a compelling consistency between the chemical and morphological characteristics of the eight studied Nitrariaceae species, and these traits are in agreement with the phylogenetic tree. Taken together, this demonstrates how novel methods for studying fossil pollen can facilitate the evolutionary investigation of living and extinct taxa, and the environments they represent.

Hierarchical classification of microorganisms based on high-dimensional phenotypic data

The classification of microorganisms by high-dimensional phenotyping methods such as FTIR spectroscopy is often a complicated process due to the complexity of microbial phylogenetic taxonomy. A hierarchical structure developed for such data can often facilitate the classification analysis. The hierarchical tree structure can either be imposed to a given set of phenotypic data by integrating the phylogenetic taxonomic structure or set up by revealing the inherent clusters in the phenotypic data. In this study, we wanted to compare different approaches to hierarchical classification of microorganisms based on high-dimensional phenotypic data. A set of 19 different species of molds (filamentous fungi) obtained from the mycological strain collection of the Norwegian Veterinary Institute (Oslo, Norway) is used for the study. Hierarchical cluster analysis is performed for setting up the classification trees. Classification algorithms such as artificial neural networks (ANN), partial least-squared discriminant analysis and random forest (RF) are used and compared. The 2 methods ANN and RF outperformed all the other approaches even though they did not utilize predefined hierarchical structure. To our knowledge, the RF approach is used here for the first time to classify microorganisms by FTIR spectroscopy.

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.

A high-throughput FTIR spectroscopy approach to assess adaptive variation in the chemical composition of pollen

The two factors defining male reproductive success in plants are pollen quantity and quality, but our knowledge about the importance of pollen quality is limited due to methodological constraints. Pollen quality in terms of chemical composition may be either genetically fixed for high performance independent of environmental conditions, or it may be plastic to maximize reproductive output under different environmental conditions. In this study, we validated a new approach for studying the role of chemical composition of pollen in adaptation to local climate. The approach is based on high-throughput Fourier infrared (FTIR) characterization and biochemical interpretation of pollen chemical composition in response to environmental conditions. The study covered three grass species, Poa alpina, Anthoxanthum odoratum, and Festuca ovina. For each species, plants were grown from seeds of three populations with wide geographic and climate variation. Each individual plant was divided into four genetically identical clones which were grown in different controlled environments (high and low levels of temperature and nutrients). In total, 389 samples were measured using a high-throughput FTIR spectrometer. The biochemical fingerprints of pollen were species and population specific, and plastic in response to different environmental conditions. The response was most pronounced for temperature, influencing the levels of proteins, lipids, and carbohydrates in pollen of all species. Furthermore, there is considerable variation in plasticity of the chemical composition of pollen among species and populations. The use of high-throughput FTIR spectroscopy provides fast, cheap, and simple assessment of the chemical composition of pollen. In combination with controlled-condition growth experiments and multivariate analyses, FTIR spectroscopy opens up for studies of the adaptive role of pollen that until now has been difficult with available methodology. The approach can easily be extended to other species and environmental conditions and has the potential to significantly increase our understanding of plant male function.

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.

Analysis of morphological and molecular composition changes in allergenic Artemisia vulgaris L. pollen under traffic pollution using SEM and FTIR spectroscopy

Nowadays, pollen allergy becomes an increasing problem for human population. Common mugwort (Artemisia vulgaris L.) is one of the major allergenic plants in Europe. In this study, the influence of air pollution caused by traffic on the structure and chemical composition of common mugwort pollen was investigated. Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and curve-fitting analysis of amide I profile was applied to assess the morphological and structural changes of mugwort pollen grains collected from sites with different vehicle pollution levels. Microscopic observations support the conclusion, that the higher the car traffic, the smaller the pollen grains. The obtained results clearly show that air pollution had an impact on different maximum absorbance values of individual functional groups composing the chemical structure of pollen. Moreover, air pollution induced structural changes in macromolecules of mugwort pollen. In pollen collected from the unpolluted site, the content of sporopollenin (850 cm^-1) was the highest, whereas polysaccharide concentration (1032 cm^-1) was the lowest. Significant differences were observed in lipids. Pollen collected from the site with heavy traffic had the lowest content of lipids at 1709, 2071, and 2930 cm^-1. The largest differences were observed in the spectra regions corresponding to proteins. In pollen collected from unpolluted site, the highest level of β-sheet (1600 cm^-1) and α-helix (1650 cm^-1) was detected. The structural changes in proteins, observed in the second derivative of the FTIR spectrum and in the curve-fitting analysis of amide I profile, could be caused inter alia by air pollutants. Alterations in protein structure and in their content in the pollen may increase the sensitization and subsequent risk of allergy in predisposed people. The obtained results suggest that the changes in chemical composition of pollen may be a good indicator of air quality and that FTIR may be successfully applied in biomonitoring.