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.

Chemical imaging has become a workhorse in analytical chemistry due to advanced method development, improved imaging speed, lower detection limits, and increased computational power. All

major analytical methods have been extended for chemical imaging purposes, resulting in improved understanding of the heterogeneity and complexity of samples of interest. Successful chemical

structure determination (CSD) combines different complementary analytical techniques applied to the same sample to generate a comprehensive analytical understanding of the sample under

investigation. While the combination of complementary analytical methods to solve scientific questions regarding complex samples or chemical systems is a fundamental working principle in

analytical chemistry, the application of this approach in chemical imaging has been rarely reported.

‘Multimodal imaging,’ ‘correlative imaging,’ and ‘data fusion’ are common catchphrases that refer to the topic of combined image-based analysis. In these approaches, the data from each

technique is analysed separately and the resulting images are combined. Correlative microscopy using light and electron microscopy is discussed in detail by Hayat1. Modern sample preparation

techniques have been developed to assist this correlative approach2, 3. The advantages of image fusion of imaging mass spectrometry and microscopy are described by Van de Plas et al.4.

Approaches toward data correlation to address biological questions are reported by Cunha et al.5, focusing on available software systems for multimodal imaging and giving the example of

correlating secondary ion mass spectrometric (SIMS) images with those obtained by transmission electron microscopy. A similar approach is reported for energy dispersive X-ray (EDX)

spectroscopy and electron microscopy6. Outside of the biological and life-science communities, data fusion became a topic of interest in surface science when time-of-flight-SIMS (ToF-SIMS)

images were combined with scanning electron microscopic (SEM) images and analysed using multivariate statistics7 and when images from X-ray photoelectron spectroscopy and atomic force

microscopy (AFM) were fused8. AFM has also been successfully fused with data obtained from laser scanning microscopy9. All these studies provide additional scientific output by combining a

chemical imaging technique with high-resolution imaging (HRI) and/or topographic imaging techniques such as SEM or AFM. A different approach to correlative imaging, however, is the fusion of

different modalities of the same fundamental physico-chemical measurement principle of imaging techniques, that is, by fusing near-infrared, mid-infrared, and Raman micro-spectroscopic

(RMS) images with subsequent multivariate data analysis10. A similar multimodal approach was applied to imaging tissues using SIMS and matrix-assisted laser desorption/ionization (MALDI)11.

Another correlative technique is reported for imaging chromosomes by cryo-fluorescence and soft X-ray tomography (SXT)12. By the application of SXT, a three-dimensional reconstruction of the

correlated dataset could be achieved. Recently, correlative 3-dimensional micro-spectroscopy of a single catalyst particle has been reported13. The detailed methodical approach is of this

technique is summarized in a recent review14.

Data fusion of single chemical images with HRI or topographic images or imaging of a sample by using different modalities of a single technique providing similar information, for instance,

vibrational spectroscopy (infrared and RMS) or mass spectrometry (SIMS and MALDI), do not fulfil the requirements of CSD because hardly any complementary chemical information is generated.

SIMS and RMS images have been correlated to visualize cell-scale molecular distributions15. Here, the complementary information of mass spectrometric and vibrational imaging are combined and

compared at the image level. This is also reported for the combined use of laser-induced breakdown spectroscopy and laser ablation inductively coupled plasma mass spectrometry16. Contrary

to these examples, fusion on the level of recorded datasets obtained from elemental (EDX) and vibrational (Raman) micro-spectroscopy (RMS) was reported in 201417. The advantages of the

combined multivariate analytical approach for this so-called multisensor hyperspectral imaging (MSHSI) dataset were recently compared to side-by-side interpretation of images18.

To generate an approach to image-based CSD that is both comprehensive and complementary, imaging data obtained via different spectroscopic techniques must first be combined and then jointly

analysed. Therefore, the imaging techniques must be non-destructive, must exhibit comparable spatial resolutions, and must return complementary chemical information. Additionally, the sample

area of interest must be accessible by all these techniques. Elemental, vibrational, and mass spectrometric imaging can fulfil these requirements using EDX imaging applied by an electron

microscope (where SEM also supplies the HRI), RMS imaging, and ToF-SIMS imaging (in the following paragraphs referred to as SIMS). RMS and EDX imaging are both non-destructive techniques;

thus, multisensor fusion of these datasets with a SIMS dataset is possible when SIMS imaging is applied as the last technique. With spatial resolutions down to 50 nm for EDX, down to 100 nm

for SIMS, and down to 200 nm for RMS, these techniques are comparable.

The present study demonstrates image-based CSD by merging EDX, RMS, and SIMS imaging datasets into a combined MSHSI datacube; subsequently, multivariate statistics are applied (Fig. 1) to

generate a comprehensive analytical representation of the image. Spectral descriptor (SPDC)17 based principal component analysis (PCA)19, k-means cluster analysis20 and hierarchical cluster

analysis (HCA)21, as well as vertex component analysis (VCA)22, are applied to analyse the combined MSHSI datasets18. The application of multivariate MSHSI to image-based CSD will be

introduced via the combined analysis of copper sulphide particles deposited on a purified aluminium substrate. Subsequently, with the aim of demonstrating the generic applicability of this

new methodology to samples from the fields of life science, materials science, and geoscience, additional examples using, respectively, tumour cells, technical ceramics, and an environmental

aerosol sample are given.

Basic concept of multisensor hyperspectral imaging with subsequent multivariate statistics to generate an analytical representation of a sample for image-based chemical structure

determination.

The multisensor hyperspectral dataset was analysed using PCA on the basis of the defined SPDCs (more details are given in the Methods section). The first principal component describes 65.77%

of the overall dataset variance. Based on the PCA of standardized SPDCs, an HCA of the loadings of the first principal component was carried out (Fig. 2, upper part). A detailed description

of all applied multivariate statistical methods can be found at Ofner et al.18. The colours in Fig. 2 and the following figures represent the different unravelled species. The colour-coding

links the detected species in the SEM or optical images with the related dendrograms of the clustering algorithms and the extracted mean spectra. Due to the fact, that all demonstrated

spectra origin from multivariate analysis (especially k-means clustering, HCA or VCA) and are standardized mean spectra, no meaningful units can be given for the intensities of the ordinates

of the spectra. Therefore, labels and units are not shown.

Superposition of the high-resolution SEM image with the CuS particle cluster of the HCA of the loadings of the PCA, dendrogram of the PCA-HCA indicating the background (black) and CuS

particle cluster (green), and the extracted cluster spectra from the k-means clustering.

The dendrogram of the HCA of the loadings of the PCA (Fig. 2, upper part) significantly differentiates the background cluster (SIMS m/z 26.98 of aluminium and the EDX X-ray Kα emission of

aluminium) and the particle cluster. This cluster contains all three Raman bands of copper sulphide (917 cm−1, 473 cm−1, and 270 cm−1), the copper isotopes from the ToF-SIMS dataset (m/z

64.93 and m/z 62.93), and the related X-ray emissions from the EDX of the SEM (S Kα and Cu Kα).

The superposition of the SEM image and the sub-cluster image of the PCA-HCA of the MSHSI datacube indicates that not all particles are related to this sub-cluster of copper sulphide. Because

the first principal component describes only 65.77% of the overall dataset, additional chemical information is assumed to be hidden in the MSHSI. A more detailed examination reveals the

presence of oxygen (EDX), as well as impurities of Na (SIMS m/z 22.99), K (SIMS m/z 38.97), Ca (SIMS m/z 39.97), and other elements, originating from the sample preparation process. However,

the major component of interest, copper sulphide, could be clearly identified within the first principal component.

To extract the combined component spectra, k-means clustering standardized SPDCs of the MSHSI datacube, choosing two expected clusters, was performed (Fig. 2, lower part). The extracted

cluster spectra exhibit the expected features. The EDX spectrum reveals the presence of Kα emissions of copper and sulphur for the green cluster, in addition to a slight increase of oxygen,

which indicates the presence of copper oxide. The extracted Raman spectrum exactly replicates the Raman spectrum of covellite (CuS)26. The SIMS spectrum identifies the two copper isotopes

63Cu and 65Cu at their atomic weights of 62.93 and 64.93 and their expected isotope ratios of 69.17% and 30.83%, respectively. Additionally, the SIMS spectrum of the CuS sub-cluster (Fig. 2,

green cluster) exhibits impurities of sodium (22.99 m/z) and potassium (38.96 m/z). The substrate (Fig. 2, black cluster) is characterized by an EDX Kα emission of aluminium, a nearly flat

Raman baseline with traces of the CuS Raman spectrum, which is caused by the multisensor fusion of the single datasets with varying spatial resolution of the methods, and higher abundance of

the mass 26.96 m/z (aluminium) in the SIMS spectrum.

The application of MSHSI to the simple example of CuS particles shows the advantages of this technique. While EDX allows rudimentary identification of the component elements, RMS reveals the

chemical bonding and allows attribution to CuS, while SIMS confirms the presence of copper by identifying the masses and ratios of the isotopes as well as providing additional information

on minor constituents, impurities, and contaminants. All this sample-specific information, which is necessary for definite chemical structure determination, is represented within a single

sub-cluster of the multivariate k-means clustering, which proves the linkage of the individual analytical methods.

Tumour cells treated with a bromine-containing prodrug were also imaged using MSHSI (Fig. 3). To analyse the combined MSHSI datacube of this sample, HCA of the PCA loadings based on

standardized SPDCs was performed, based on the concept of SPDCs (Fig. 3, upper part). The two sub-clusters of the HCA of the PCA show a distinction between the nucleus surrounded by the

rough endoplasmic reticulum for protein biosynthesis (Fig. 3, orange sub-cluster) and the cytoplasm of the cell (Fig. 3, blue sub-cluster). The superposition of the cluster image with the

optical image shows that both cells undergo cell division. The orange sub-cluster is defined by the SPDCs of the EDX nitrogen signal, the SIMS masses 26.00 and 42.00 m/z, which can be

attributed to CN− and CNO− fragments, and the Raman amide I band at 1688 cm−1 as well as a centroid spectral descriptor, which is assigned to the ν(C–H) stretch region and exhibits a

positive value at the location of the nucleus. The blue sub-cluster is characterized by a Raman band at 2890 cm−1, the ν(C–H) of –CH2– groups, which can be assigned to lipids, the EDX Lα

emission of bromine, and the ToF-SIMS masses 78.93 m/z and 80.93 m/z, which represent the two bromine isotopes 79Br and 81Br, respectively.

Superposition of a light-microscopic image of two tumour cells and results from the HCA of the PCA loadings and extracted cluster spectra from k-means clustering of the MSHSI dataset.

As an example of the application of image-based chemical structure determination to the field of materials science, a ceramic composite prepared by the polymer-precursor method was analysed.

The synthesis of this technical ceramic, which consists of ZrO2-reinforced mullite (3Al2O3·2SiO2), was based on the thermal conversion of methyl-polysilsesquioxane to highly reactive SiO2,

which subsequently reacted with Al2O3 to form a continuous mullite matrix. Additionally, ZrO2 (yttrium-stabilized) was incorporated as particulate reinforcement. The full synthesis procedure

and SEM image-based analytics have been described in previous studies31, 32.

Superposition of the SEM image of the ceramic mullite/ZrO2 composite with the image of the HCA of the PCA loadings (upper part) and with the extracted endmembers of the VCA (lower part),

which represents the diamond Raman spectrum (red) and the ZrSiO4 Raman spectrum (orange).

The overall sample is characterized by pores, which are artefacts of the polishing process. Analysing these holes in detail revealed an enhanced amount of carbon (EDX Kα). Therefore, an

additional species was expected. The EDX dataset only exhibited the information on the carbon content. Further, no significant masses could be found in the SIMS dataset for these parts of

the sample. VCA was applied on the RMS portion of the MSHSI dataset and a spectral endmember, corresponding to the holes in the sample, was extracted (Fig. 4, lower part, red RMS spectrum).

This endmember is characterized by a single Raman band at about 1332 cm−1 and correlated to high carbon content. Based on the literature, the red spots in Fig. 4 (lower part) can be assigned

to diamond34. This vibration is evoked by the interpenetrating cubic sublattice and can be assigned to a lattice vibration of the sp3 carbon bonds (ω(sp3))35. Diamond paste was used to

polish the sample, and the red spots indicate areas of diamond residue. The VCA algorithm also revealed an additional spectral endmember in the Raman datacube (Fig. 4, lower part, orange

spectrum). According to the literature36 and the RRUFF database26, as well as expectations based on the synthesis method31, this VCA endmember can be attributed to ZrSiO4, which forms at the

interface between the ZrO2 particles and the mullite matrix.

By applying PCA-HCA and VCA to the MSHSI dataset of the ceramic sample, four main chemical species could be extracted from the SEM image. The yttrium-stabilized ZrO2 is well represented by

the RMS, EDX, and SIMS sub-datasets. By superimposition with the SEM image, the white particles could be identified as 3Y-ZrO2. The mullite matrix is congruent with the gray areas in the SEM

image and is characterized by the EDX and SIMS sub-datasets of the MSHSI datacube. Additionally, the RMS sub-dataset revealed the presence of ZrSiO4 by PCA-HCA and diamond residues by VCA

endmember extraction from the RMS sub-dataset, which was also confirmed by the enhanced carbon signal in the EDX dataset.

Atmospheric aerosols are complex mixtures of analytes from natural or anthropogenic sources that are formed and transformed in the environment. A combined analytical approach to this class

of samples increases the value of the chemical analysis and enhances the understanding of the entire environmental system. During atmospheric sampling in the city of Vienna, a nearby

building that had been affected by a fire was torn down.

The sample was imaged and the datasets were fused into the MSHSI datacube. Because this sample exhibited a high number of different chemical species, SPDCs had to be defined by an iterative

process. Restrictive clustering algorithms such as k-means clustering would hamper the interpretation of the dataset because the expected number of clusters must be known in advance. An

estimation of the expected number of species based on analysis of the eigenvalue plot of the PCA revealed eight to ten different chemical species. To allow a top-down analysis of these

species, SPDC-based HCA (Ward’s method with Euclidean distances) of the overall dataset was performed.

The analysis of the HCA of the MSHSI dataset revealed at least eight sub-clusters, which can be assigned to different chemical species (Fig. 5). All sub-clusters exhibit strong internal

mixing of the single aerosol particles. The yellow sub-cluster shows typical urban aerosol composition and represents a minor part of the overall particle composition. The blue sub-cluster,

which is closely connected in terms of inter-cluster distance, can also be attributed to the urban background aerosol. The remaining sub-clusters can be attributed to the tearing down of the

burned building.

Superposition of the SEM image of the aerosol sample with the virtual image of the HCA of the MSHSI dataset and assignment of chemical species to the sub-clusters, based on extracted cluster

spectra.

The assignment of the individual sub-clusters (Fig. 5) to chemical species is achieved by analysing the sub-cluster spectra in parallel (Fig. 6).

Examples of extracted sub-cluster spectra from the HCA of the environmental aerosol sample and assignment of spectral features.

The green sub-cluster is dominated by the EDX elements Ca, Mg, O, and Si with contributions from Fe and Na. The RMS dataset exhibits a band at about 1085 cm−1 that can be assigned to calcite

or aragonite (CaCO3) through comparison with reference spectra26, 37. Additionally, the Raman spectrum includes a contribution from black carbon (soot)38, which can be attributed to the

fire at the demolished building. The SIMS spectrum supports this interpretation by exhibiting contributions from the masses (m/z) 27.97 (Si), 39.96 (Ca), 43.9 (SiO or CO2, as a fragment of

the carbonate), and 55.96 (Fe or CaO). The green sub-cluster thus exhibits features typical of the building demolition process. The silicates and the CaCO3 can be attributed to concrete.

The cyan blue sub-cluster exhibits features in the EDX spectrum similar to those of the green sub-cluster, with an enhanced contribution from carbon. However, the Raman spectrum is

supplemented by a dominant band in the spectral region of ν(C–H) stretching vibrations, which indicates the presence of organic carbon, and a sharp band at 1065 cm−1. Additionally, the

presence of black carbon is indicated. Analysing the SIMS spectrum showed the presence of 22.98 (Na), 38.98 (NaO, K), 39.96 (Ca), 43.9 (CO2 from carbonates), 50.98 (NaCO), and 55.96 (CaO,

Fe) m/z. Hence, the presence of some form of sodium carbonate is likely. Comparison of the Raman band at 1065 cm−1 with the RRUFF database26 shows that it can likely be attributed to trona,

a water-bearing sodium hydrogen carbonate with the molecular formula Na3(HCO3)(CO3)·H2O that is used as a fire-extinguishing agent. This sub-cluster can thus be attributed to demolition

waste and the fire-extinguishing agent.

The EDX spectrum of the red sub-cluster is dominated by Fe and O. The Raman spectrum exhibits a band at 290 cm−1 along with bands of CaCO3 and black carbon. In addition to the SIMS masses

identified in the green sub-cluster, 55.93 (Fe), 71.93 (FeO), and 87.93 (FeO2) m/z are also visible. Comparison with the RRUFF database26 of the Raman spectrum and correlating the three

MSHSI spectra of the red sub-cluster indicate that this sub-cluster exhibits contributions from hematite, a fingerprint of the structural steel and the technical building equipment.

The purple, pink, and orange sub-clusters are also related to building demolition debris. Therefore, their analysis is not described or shown in detail in Fig. 6. Still, the decreasing

inter-cluster distance to the yellow and blue sub-clusters indicate that mineral dust also forms part of the urban background aerosol.

The remaining sub-clusters (yellow and blue) are significantly separated from the other clusters, indicating a different chemical background.

The EDX part of the yellow sub-cluster reveals the presence of Cl, Na, and O, with contributions of S. The Raman spectrum is dominated by the three distinct Raman bands of NaNO3 at 725,

1070, and 1390 cm−1, as well as the contributions from black carbon and organic carbon (ν(C–H)). The presence of NaNO3 is also confirmed by the presence of 22.99 (Na), 45.99 (NO2), 63.99

(NO3), and 84.98 (NaNO3) m/z in the SIMS spectrum. Additionally, the presence of NaCl, which is not visible in the RMS and SIMS datasets, can be extracted from the EDX dataset. The presence

of CaSO4 is indicated by the Raman band at 1017 cm−1 and the sulphur signal from the EDX dataset. This sub-cluster represents the expected typical aerosol composition from the city of Vienna

based on the season.

The blue sub-cluster is characterized by high potassium content in the EDX spectrum and the SIMS spectrum, indicated by the masses 38.96 (39K) and 40.96 (41K) m/z. The SIMS spectrum also

displays the masses 77.93 m/z (K2) and 93.92 m/z (K2O). The Raman spectrum exhibits a high noise level. However, the contribution from black carbon and the Raman band of CaCO3 are still

visible. Additionally a band at about 280 cm−1 is present, which can be assigned to K–O containing species26. Hence, the blue sub-cluster can be attributed to ash, pointing to the influence

of wood combustion, which is known to be present in Vienna.

The application of HCA to the MSHSI dataset of the environmental aerosol sample assisted the identification of single precipitated particles and their attribution to different sources. The

different contributions from the demolition of a building were clarified by analysing the combined EDX, Raman, and SIMS spectra. Several of the species were not visible in all three spectra,

such as the silicates, K2O, or the organic carbon. Therefore, the analysis of this dataset in parallel improves the analytical interpretation and enables a detailed understanding of the

sample composition. However, some species, such as CaCO3 and Fe2O3, could only be identified by analysing at least two or even all three of the spectra.

The decisive advantages of image-based CDS by multivariate MSHSI are demonstrated via the combined analysis of copper sulphide particles. EDX, RMS, and SIMS SPDCs unravelled the chemical

composition of these particles by merging the CuS signals from each of the three different methods within the same sub-cluster of the PCA-HCA dendrogram. The combinational analysis of the

complementary imaging techniques was also verified via the subsequent k-means clustering.

Additional benefits of this method are shown by the three subsequent practical examples. For the cell sample, the spatial allocation of the bromine-containing drug (EDX and SIMS) as well as

the correlation of the SIMS fragments of lipids with the –CH2 band of the RMS could be extracted from the complex MSHSI dataset. In the case of the technical ceramic sample, the 3Y-ZrO2

could be identified by all three analytical methods, while the chemical information about the mullite matrix (3Al2O3·2SiO2) was accessible by only EDX and SIMS. The presence of diamond and

ZrSiO4 could only be extracted from the RMS dataset by VCA. Chemical analysis of the environmental aerosol sample required the combined interpretation of the cluster spectra from all three

techniques: silicates (EDX), black and organic carbon (EDX and RMS), ash (EDX and SIMS), sodium nitrate (EDX, RMS, and SIMS), and sodium hydrogen carbonate (RMS and SIMS).

Multivariate analysis of a fused multisensor hyperspectral imaging dataset, obtained from complementary chemical imaging techniques, provides a unique method of image-based chemical

structure determination, where the linkage of analytical information across various individual techniques is proven by the combined statistical approach. Hence, joint clustering of different

elemental, vibrational, and mass spectrometric features allows direct interpretation of the investigated chemical compound or structure. Applying and combining complementary techniques of

comparable spatial resolutions minimizes the risk of overlooking analytical information. Furthermore, cross-correlating different methods helps to prevent the over-interpretation of

insufficient data. Hence, MSHSI provides a more focused method of image-based chemical structure determination. By applying three-dimensional or temporal-correlated imaging techniques,

multivariate analysis of fused higher-dimensional or dynamical multisensor hyperspectral imaging datasets will deepen the understanding of complex materials and processes.

For imaging of CuS and atmospheric particles, aluminium targets, obtained by sputtering highly purified aluminium onto microscope cover slips39, were used as substrates.

CuS particles were purchased from Alfa-Aesar (99.8%, Alfa-Aesar, Ward Hill, MA, USA, CAS No. 1317-40-4). The liquid suspension consisting of the CuS particles and highly purified water

(Milli-Q Millipore System) was nebulized using an aerosol generator (TOPAS aerosol generator ATM 220). A Sioutas cascade impactor with a Leland Legacy pump (both SKC, PA, USA) was deployed

at a flow rate of 9 L min−1 to precipitate the nebulized CuS particles onto the aluminium targets. The whole procedure was carried out so as to ensure a suitable spatial distribution of the

CuS particles on the aluminium surface.

For the imaging of the cell samples, P31 tumour cells were grown in RPMI medium with 10% FBS and 1% penicillin/streptomycin on gold-coated silicon wafers (~24 h). Then the cells were treated

with evofosfamide (50 µM). The cells were rinsed with deionised water and dried under vacuum conditions.

The ceramics sample was synthesized as described by Konegger et al.31, 32. A polished cross-section of the sample was analysed using the spectroscopic techniques without any further

processing.

The aerosol sample was taken on 20 November 2016 at the Getreidemarkt in Vienna at the TU Wien. The sampling onto the described aluminium-coated glass cover slips was also done using a

Sioutas cascade impactor in combination with a Leland Legacy pump at a flow rate of 9 L min−1. Sampling time was 1.5 h, resulting in a total sampled air volume of 810 L. For the combined

imaging, particles sampled on the second stage, with an aerodynamic particle diameter cut-off between 2.5 and 1 µm, were chosen.

Raman imaging was performed using a WITec alpha 300RSA + Raman microscope. This instrument is equipped with four Raman lasers (a 488 nm DPSS, a 532 nm frequency-doubled NdYAG, a 632.8 nm

HeNe, and a 785 nm diode laser) and two lens-based spectrographs. The spectrograph used for the UV/VIS spectral region is equipped with a 300, 600, and 1200 lines per mm grating and an Andor

Newton electron multiplying charge-coupled device (EMCCD). The spectrograph used for the NIR spectral region is equipped with a 300 and 600 lines per mm grating and an Andor iDus Deep

Depletion CCD. Raman images were recorded using the software package WITec Control 4.1. The parameters for Raman imaging of the four samples are summarized in Table 1.

Electron microscopy and EDX imaging was applied using a FEI Quanta 200 electron microscope with an EDAX EDX detector. Scanning electron microscope images were obtained in secondary and

backscattered electron mode. EDX imaging was done at a resolution of 1024 × 768 pixels and an acceleration voltage of 20 kV. EDX images were accumulated for about one hour to achieve

reasonable signal-to-noise ratios of the elemental distributions. The parameters for EDX imaging of the four samples are summarized in Table 2.

Single imaging datasets were combined into multisensor hyperspectral datasets using the software package Imagelab (Epina GmbH, Austria). All datasets were imported and aligned to a common

background image for each of the four samples. Finally, the individual hyperspectral datasets were combined to give a multisensor hyperspectral datacube as described by Lohninger and

Ofner17.

The final spatial resolution of the fused datasets is depending on the spatial resolutions of every single method and on the lateral offset as well as the rotation of the single datasets to

each other. In general, a maximum decrease by the square-root of 2 of the worst spatial resolution can be expected (at maximum rotation). However, by ensuring a minimized rotational

misalignment, spatial resolutions of the fused datasets can be expected to be hardly worse compared to the worst resolution of a single technique (CuS particles: 250 nm; Tumour cells: 260 

nm; Ceramic Composite: 225 nm; Environmental aerosol: 275 nm).

SPDCs for the individual elements from the EDX and masses from the SIMS data cubes were defined by applying single intensity descriptors of the signals. For the RMS data cube, triangle

template peaks, baseline-corrected and –uncorrected integral descriptors of selected bands as well as centroid descriptors (for the ν(C-H) spectral region) and ratio descriptors of two bands

(ZrO2-bands in the ceramics dataset) were defined. Further details on the definition of spectral descriptors, spectral preprocessing, and standardization and application of multivariate

statistics to the multisensor hyperspectral datasets, as well as introductions to the different statistical methods, can be found in preceding studies17, 18.

The authors provide the multisensor data sets and the spectral descriptors used in this study. Further information can be found in the ImageLab data repository

(http://www.imagelab.at/en_data_repository.html datasets DS009 - DS012).

The authors would like to thank the Hochschuljubiläumsstiftung of the City of Vienna (project H-297306/2014) for funding. The authors also thank the Analytical Instrumentation Center of the

TU Wien for access to the infrastructure. F.B. thanks the MEIBio doctoral project of TU Wien for providing a scholarship in the period 2013 to 2017. The authors acknowledge the TU Wien

University Library for financial support through its Open Access Funding Programme.

Institute of Chemical Technologies and Analytics, TU Wien, Getreidemarkt 9, 1060, Vienna, Austria

Johannes Ofner, Florian Brenner, Karin Wieland, Elisabeth Eitenberger, Thomas Konegger, Anne Kasper-Giebl, Herbert Hutter, Gernot Friedbacher, Bernhard Lendl & Hans Lohninger

Institute of Solide State Physics, TU Wien, Wiedner Hauptstrasse 8, 1040, Vienna, Austria

National Korányi Institute of Pulmonology, Budapest, Hungary

Division of Thoracic Surgery, Department of Surgery, Comprehensive Cancer Center, Medical University of Vienna, Vienna, Austria

Department of Thoracic Surgery, Semmelweis University and National Institute of Oncology, Budapest, Hungary

Department of Biomedical Imaging and Image-guided Therapy, Division of Molecular and Gender Imaging, Medical University of Vienna, Vienna, Austria

J.O., F.B. and H.L. conceived the study, processed the data, and wrote the manuscript. J.O. and B.L. did the Raman micro-spectroscopic imaging. J.O., E.E. and G.F. performed the electron

microscopy and the EDX measurements. F.B. and H.H. performed the ToF-SIMS imaging and delivered the data. J.K. and C.E.-S. provided the sampling substrates. T.K. synthesized and provided the

ceramics sample. S.T. and B.D. provided the tumour cells. A.K.-G. and K.W. assisted with the interpretation of the data and provided access to the sampling equipment. All coauthors

discussed the results and commented on the manuscript.

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