Elemental mapping of fluorine by means of molecular laser induced breakdown spectroscopy.

The growing importance of fluoropolymers in high-tech applications and green technologies results in the rising need for their characterization. In contrast to conventional methods used for this task, laser-induced breakdown spectroscopy (LIBS) provides the advantage of a spatially resolved analysis. Nevertheless, the high excitation energy of fluorine results in low sensitivity of the atomic F(I) lines, which limits the feasibility of its LIBS-based analysis. This work presents a novel approach for quantitative mapping of fluorine in fluoropolymer samples. It bases on monitoring of molecular emission bands (CuF or CaF) arising from fluorine containing molecules. These species were generated during later stages of the LIBS plasma by a recombination of fluorine atoms originating from fluoropolymer sample with a molecule-forming partner (Cu or Ca) stemming from a surface coating. This approach enables F detection limits in the parts per million (µg g-1) range and elemental imaging using single shot measurements. The elements required for molecular formation are deposited on the sample surface prior to analysis. We evaluate two techniques - spray coating and sputter coating - with regards to their effects on sensitivity and spatial resolution in elemental mapping. Overall, both methods proved to be suitable for a spatially resolved analysis of fluorine: whereas sputter-coating of copper yielded a better sensitivity, spray coating of calcium provided a higher spatial resolution.

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.

Identification of 20 polymer types by means of laser-induced breakdown spectroscopy (LIBS) and chemometrics.

Over the past few years, laser-induced breakdown spectroscopy (LIBS) has earned a lot of attention in the field of online polymer identification. Unlike the well-established near-infrared spectroscopy (NIR), LIBS analysis is not limited by the sample thickness or color and therefore seems to be a promising candidate for this task. Nevertheless, the similar elemental composition of most polymers results in high similarity of their LIBS spectra, which makes their discrimination challenging. To address this problem, we developed a novel chemometric strategy based on a systematic optimization of two factors influencing the discrimination ability: the set of experimental conditions (laser energy, gate delay, and atmosphere) employed for the LIBS analysis and the set of spectral variables used as a basis for the polymer discrimination. In the process, a novel concept of spectral descriptors was used to extract chemically relevant information from the polymer spectra, cluster purity based on the k-nearest neighbors (k-NN) was established as a suitable tool for monitoring the extent of cluster overlaps and an in-house designed random forest (RDF) experiment combined with a cluster purity-governed forward selection algorithm was employed to identify spectral variables with the greatest relevance for polymer identification. Using this approach, it was possible to discriminate among 20 virgin polymer types, which is the highest number reported in the literature so far. Additionally, using the optimized experimental conditions and data evaluation, robust discrimination performance could be achieved even with polymer samples containing carbon black or other common additives, which hints at an applicability of the developed approach to real-life samples.

Spatially resolved polymer classification using laser induced breakdown spectroscopy (LIBS) and multivariate statistics.

Synthetic polymers and plastics have become one of the most important materials in our modern world and everyday life with all kinds of applications mainly due to their wide range of excellent and tuneable properties. Several novel materials consisting of multiple different synthetic polymers or composite materials like natural-fiber-reinforced polymer composites have already been reported in literature. Additionally, materials consisting of multiple synthetic polymers already found their way in our daily lives (e.g. double-sided adhesive tape). With emerging materials consisting of different structured synthetic polymers, the need for analytical methods characterizing these kinds of sample also arises. Conventionally, analytical techniques such as FT-IR or Raman spectroscopy are used for polymer classification. Although, these techniques offer laterally resolved investigations they lack the possibility of analyzing depth profiles. In this work, we present laser induced breakdown spectroscopy (LIBS) as a novel and powerful analytical method for spatially resolved polymer classification. As a feasibility study, two exemplary structured synthetic polymer samples (2D structured and multilayer system) have been analyzed using LIBS and the spatial distribution of 5 different synthetic polymers, namely acrylonitrile butadiene styrene (ABS), polylactic acid (PLA), polyethylene (PE), polyacrylate (PAK) and polyvinylchloride (PVC) have been successfully classified using multivariate statistical approaches (principal component analysis (PCA) and k-means clustering). Spatially resolved classification results were validated by comparing the obtained distribution of the 2D structured sample to the elemental distribution of a contamination present in one of the synthetic polymers. Classification of the polymeric multilayer system was validated by comparing the obtained results to a microscopic cross-section. It was shown that LIBS cannot only be used to investigate 2D structured polymer samples but also for direct analysis of depth profiles. Besides synthetic polymer classification, LIBS provides simultaneous analysis of the elemental composition of the sample, which increases the total amount of information accessible with only one measurement.

A graph-based clustering method with special focus on hyperspectral imaging.

A common trait of the more established clustering algorithms such as K-Means and HCA is their tendency to focus mainly on the bulk features of the data which causes minor features to be attributed to larger clusters. For hyperspectral imaging this has the consequence that substances which are covered by only a few pixels tend to be overlooked and thus cannot be separated. If small lateral features such as particles are the research objective this might be the reason why cluster analysis fails. Therefore we propose a novel graph-based clustering algorithm dubbed GBCC which is sensitive to small variations in data density and scales its clusters according to the underlying structures. The analysis of the proposed method covers a comparison to K-Means, DBSCAN and KNSC using a 2D artificial dataset. Further the method is evaluated on a multisensor image of atmospheric particulate matter composed of Raman and EDX data as well as an FTIR image of microplastics.

Multisensor Imaging-From Sample Preparation to Integrated Multimodal Interpretation of LA-ICPMS and MALDI MS Imaging Data.

Laterally resolved chemical analysis (chemical imaging) has increasingly attracted attention in the Life Sciences during the past years. While some developments have provided improvements in lateral resolution and speed of analysis, there is a trend toward the combination of two or more analysis techniques, so-called multisensor imaging, for providing deeper information into the biochemical processes within one sample. In this work, a human malignant pleural mesothelioma sample from a patient treated with cisplatin as a cytostatic agent has been analyzed using laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) and matrix-assisted laser desorption/ionization mass spectrometry (MALDI MS). While LA-ICPMS was able to provide quantitative information on the platinum distribution along with the distribution of other elemental analytes in the tissue sample, MALDI MS could reveal full information on lipid distributions, as both modes of polarity, negative and positive, were used for measurements. Tandem MS experiments verified the occurrence of distinct lipid classes. All imaging analyses were performed using a lateral resolution of 40 µm, providing information with excellent depth of details. By analyzing the very same tissue section, it was possible to perfectly correlate the obtained analyte distribution information in an evaluation approach comprising LA-ICPMS and MALDI MS data. Correlations between platinum, phosphorus, and lipid distributions were found by the use of advanced statistics. The present proof-of-principle study demonstrates the benefit of data combination for outcomes beyond one method imaging modality and highlights the value of advanced chemical imaging in the Life Sciences.

Fourier Transform Infrared (FT-IR) and Laser Ablation Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) Imaging of Cerebral Ischemia: Combined Analysis of Rat Brain Thin Cuts Toward Improved Tissue Classification.

Microspectroscopic techniques are widely used to complement histological studies. Due to recent developments in the field of chemical imaging, combined chemical analysis has become attractive. This technique facilitates a deepened analysis compared to single techniques or side-by-side analysis. In this study, rat brains harvested one week after induction of photothrombotic stroke were investigated. Adjacent thin cuts from rats' brains were imaged using Fourier transform infrared (FT-IR) microspectroscopy and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). The LA-ICP-MS data were normalized using an internal standard (a thin gold layer). The acquired hyperspectral data cubes were fused and subjected to multivariate analysis. Brain regions affected by stroke as well as unaffected gray and white matter were identified and classified using a model based on either partial least squares discriminant analysis (PLS-DA) or random decision forest (RDF) algorithms. The RDF algorithm demonstrated the best results for classification. Improved classification was observed in the case of fused data in comparison to individual data sets (either FT-IR or LA-ICP-MS). Variable importance analysis demonstrated that both molecular and elemental content contribute to the improved RDF classification. Univariate spectral analysis identified biochemical properties of the assigned tissue types. Classification of multisensor hyperspectral data sets using an RDF algorithm allows access to a novel and in-depth understanding of biochemical processes and solid chemical allocation of different brain regions.

Image-Based Chemical Structure Determination.

Chemical imaging is a powerful tool for understanding the chemical composition and nature of heterogeneous samples. Recent developments in elemental, vibrational, and mass-spectrometric chemical imaging with high spatial resolution (50-200 nm) and reasonable timescale (a few hours) are capable of providing complementary chemical information about various samples. However, a single technique is insufficient to provide a comprehensive understanding of chemically complex materials. For bulk samples, the combination of different analytical methods and the application of statistical methods for extracting correlated information across different techniques is a well-established and powerful concept. However, combined multivariate analytics of chemical images obtained via different imaging techniques is still in its infancy, hampered by a lack of analytical methodologies for data fusion and analysis. This study demonstrates the application of multivariate statistics to chemical images taken from the same sample via various methods to assist in chemical structure determination.

High throughput inclusion body sizing: Nano particle tracking analysis.

The expression of pharmaceutical relevant proteins in Escherichia coli frequently triggers inclusion body (IB) formation caused by protein aggregation. In the scientific literature, substantial effort has been devoted to the quantification of IB size. However, particle-based methods used up to this point to analyze the physical properties of representative numbers of IBs lack sensitivity and/or orthogonal verification. Using high pressure freezing and automated freeze substitution for transmission electron microscopy (TEM) the cytosolic inclusion body structure was preserved within the cells. TEM imaging in combination with manual grey scale image segmentation allowed the quantification of relative areas covered by the inclusion body within the cytosol. As a high throughput method nano particle tracking analysis (NTA) enables one to derive the diameter of inclusion bodies in cell homogenate based on a measurement of the Brownian motion. The NTA analysis of fixated (glutaraldehyde) and non-fixated IBs suggests that high pressure homogenization annihilates the native physiological shape of IBs. Nevertheless, the ratio of particle counts of non-fixated and fixated samples could potentially serve as factor for particle stickiness. In this contribution, we establish image segmentation of TEM pictures as an orthogonal method to size biologic particles in the cytosol of cells. More importantly, NTA has been established as a particle-based, fast and high throughput method (1000-3000 particles), thus constituting a much more accurate and representative analysis than currently available methods.

Tip-Enhanced Raman Spectroscopy of Atmospherically Relevant Aerosol Nanoparticles.

Atmospheric aerosol nanoparticles play a major role in many atmospheric processes and in particular in the global climate system. Understanding their formation by homogeneous or heterogeneous nucleation as well as their photochemical aging and atmospheric transformation is of utmost importance to evaluate their impact on atmospheric phenomena. Single particle analysis like tip-enhanced Raman spectroscopy (TERS) opens access to a deeper understanding of these nanoparticles. Atmospherically relevant nanoparticles, formed above a simulated salt lake inside an aerosol smog-chamber, were analyzed using TERS. TERS spectra of 11 nanoparticles were studied in detail. First results of TERS on atmospherically relevant aerosol nanoparticles reveal the presence of inorganic seed particles, a chemical diversity of equally sized particles in the nucleation mode, and chemical transformation during photochemical aging. Therefore, single particle analysis by optical near-field spectroscopy such as TERS of atmospheric nanoparticles will significantly contribute to elucidate atmospheric nucleation, photochemical aging, and chemical transformation processes by uncovering single particle based properties.

Chemometric analysis of multisensor hyperspectral images of precipitated atmospheric particulate matter.

The chemometric analysis of multisensor hyperspectral data allows a comprehensive image-based analysis of precipitated atmospheric particles. Atmospheric particulate matter was precipitated on aluminum foils and analyzed by Raman microspectroscopy and subsequently by electron microscopy and energy dispersive X-ray spectroscopy. All obtained images were of the same spot of an area of 100 × 100 µm(2). The two hyperspectral data sets and the high-resolution scanning electron microscope images were fused into a combined multisensor hyperspectral data set. This multisensor data cube was analyzed using principal component analysis, hierarchical cluster analysis, k-means clustering, and vertex component analysis. The detailed chemometric analysis of the multisensor data allowed an extensive chemical interpretation of the precipitated particles, and their structure and composition led to a comprehensive understanding of atmospheric particulate matter.

Application of gold thin-films for internal standardization in LA-ICP-MS imaging experiments.

LA-ICP-MS imaging experiments are of growing interest within the field of biosciences. Revealing the distributions of major components as well as trace elements in biological samples can help to understand fundamental biological processes. However, highly variable sample conditions and changing instrumental parameters during measurement time aggravate reliable quantification especially in biological tissues. Normally matrix matched standards used for calibration are scarcely available and the manufacturing process thereof is rather complicated. Thus most experiments reported in the literature only delivered qualitative information on the analyte distributions. The use of appropriate internal standards facilitates the preparation of calibrations even without the utilization of matrix-matched standards. In the presented work an approach for providing reliable quantitative bio-images is proposed using gold thin-layers as an internal standard and patterns printed with commercially available inkjet printers as standards. The method development is based on copper from blue ink as the element of interest. It could be shown that gold standardization compensates instrumental drifts, matrix related ablation differences and day-to-day signal changes. Not only was the quality of the obtained images improved by gold standardization; while the relative standard deviation of the measurements was around 15% before standardization it could be decreased to less than 5% by gold standardization. Also quantitative information could be obtained for samples with unknown analyte concentrations. Depending on the used beam diameter limits of detection in the range of some hundreds ng g(-1) were achieved. The presented method is a promising and easy-to-handle alternative to matrix matched standards for signal quantification.

Experimental characterization of the human non-sequence-specific nucleic acid interactome.

BACKGROUND: The interactions between proteins and nucleic acids have a fundamental function in many biological processes, including gene transcription, RNA homeostasis, protein translation and pathogen sensing for innate immunity. While our knowledge of the ensemble of proteins that bind individual mRNAs in mammalian cells has been greatly augmented by recent surveys, no systematic study on the non-sequence-specific engagement of native human proteins with various types of nucleic acids has been reported. RESULTS: We designed an experimental approach to achieve broad coverage of the non-sequence-specific RNA and DNA binding space, including methylated cytosine, and tested for interaction potential with the human proteome. We used 25 rationally designed nucleic acid probes in an affinity purification mass spectrometry and bioinformatics workflow to identify proteins from whole cell extracts of three different human cell lines. The proteins were profiled for their binding preferences to the different general types of nucleic acids. The study identified 746 high-confidence direct binders, 139 of which were novel and 237 devoid of previous experimental evidence. We could assign specific affinities for sub-types of nucleic acid probes to 219 distinct proteins and individual domains. The evolutionarily conserved protein YB-1, previously associated with cancer and drug resistance, was shown to bind methylated cytosine preferentially, potentially conferring upon YB-1 an epigenetics-related function. CONCLUSIONS: The dataset described here represents a rich resource of experimentally determined nucleic acid-binding proteins, and our methodology has great potential for further exploration of the interface between the protein and nucleic acid realms.

Optimisation of a sample preparation procedure for the screening of fungal infection and assessment of deoxynivalenol content in maize using mid-infrared attenuated total reflection spectroscopy.

A sample preparation procedure for the determination of deoxynivalenol (DON) using attenuated total reflection mid-infrared spectroscopy is presented. Repeatable spectra were obtained from samples featuring a narrow particle size distribution. Samples were ground with a centrifugal mill and analysed with an analytical sieve shaker. Particle sizes of <100, 100-250, 250-500, 500-710 and 710-1000 microm were obtained. Repeatability, classification and quantification abilities for DON were compared with non-sieved samples. The 100-250 microm fraction showed the best repeatability. The relative standard deviation of spectral measurements improved from 20 to 4.4% and 100% of sieved samples were correctly classified compared with 79% of non-sieved samples. The DON level in analysed fractions was a good estimate of overall toxin content.

SURFCOMP: a novel graph-based approach to molecular surface comparison.

Analysis of the distributions of physicochemical properties mapped onto molecular surfaces can highlight important similarities or differences between compound classes, contributing to rational drug design efforts. Here we present an approach that uses maximal common subgraph comparison and harmonic shape image matching to detect locally similar regions between two molecular surfaces augmented with properties such as the electrostatic potential or lipophilicity. The complexity of the problem is reduced by a set of filters that implement various geometric and physicochemical heuristics. The approach was tested on dihydrofolate reductase and thermolysin inhibitors and was shown to recover the correct alignments of the compounds bound in the active sites.

A comparative study of mid-infrared diffuse reflection (DR) and attenuated total reflection (ATR) spectroscopy for the detection of fungal infection on RWA2-corn.

An investigation into the rapid detection of mycotoxin-producing fungi on corn by two mid-infrared spectroscopic techniques was undertaken. Corn samples from a single genotype (RWA2, blanks, and contaminated with Fusarium graminearum) were ground, sieved and, after appropriate sample preparation, subjected to mid-infrared spectroscopy using two different accessories (diffuse reflection and attenuated total reflection). The measured spectra were evaluated with principal component analysis (PCA) and the blank and contaminated samples were classified by cluster analysis. Reference data for fungal metabolites were obtained with conventional methods. After extraction and clean-up, each sample was analyzed for the toxin deoxynivalenol (DON) by gas chromatography with electron capture detection (GC-ECD) and ergosterol (a parameter for the total fungal biomass) by high-performance liquid chromatography with diode array detection (HPLC-DAD). The concentration ranges for contaminated samples were 880-3600 microg/kg for ergosterol and 300-2600 microg/kg for DON. Classification efficiency was 100% for ATR spectra. DR spectra did not show as obvious a clustering of contaminated and blank samples. Results and trends were also observed in single spectra plots. Quantification using a PLS1 regression algorithm showed good correlation with DON reference data, but a rather high standard error of prediction (SEP) with 600 microg/kg (DR) and 490 microg/kg (ATR), respectively, for ergosterol. Comparing measurement procedures and results showed advantages for the ATR technique, mainly owing to its ease of use and the easier interpretation of results that were better with respect to classification and quantification.

Development of a method for the determination of Fusarium fungi on corn using mid-infrared spectroscopy with attenuated total reflection and chemometrics.

A novel method, which enables the determination of fungal infection with Fusarium graminearum on corn within minutes, is presented. The ground sample was sieved and the particle size fraction between >250 and 100 microm was used for mid-infrared/attenuated total reflection (ATR) measurements. The sample was pressed onto the ATR crystal, and reproducible pressure was applied. After the spectra were recorded, they were subjected to principle component analysis (PCA) and classified using cluster analysis. Observed changes in the spectra reflected changes in protein, carbohydrate, and lipid contents. Ergosterol (for the total fungal biomass) and the toxin deoxynivalenol (DON; a secondary metabolite) of Fusarium fungi served as reference parameters, because of their relevance for the examination of corn based food and feed. The repeatability was highly improved by sieving prior to recording the spectra, resulting in a better clustering in PCA score/score plots. The developed method enabled the separation of samples with a toxin content of as low as 310 microg/kg from noncontaminated (blank) samples. Investigated concentration ranges were 880-3600 microg/kg for ergosterol and 310-2596 microg/kg for DON. The percentage of correctly classified samples was up to 100% for individual samples compared with a number of blank samples.