Chemometric Analysis of Multisensor Hyperspectral Images of

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Chemometric Analysis of Multisensor Hyperspectral Images of Precipitated Atmospheric Particulate Matter Johannes Ofner,*,† Katharina A. Kamilli,‡ Elisabeth Eitenberger,† Gernot Friedbacher,† Bernhard Lendl,† Andreas Held,‡ and Hans Lohninger† †

Institute of Chemical Technologies and Analytics, TU Wien, Getreidemarkt 9, 1060 Vienna, Austria Atmospheric Chemistry, University of Bayreuth, Dr. Hans-Frisch-Straße A1.2, 95448 Bayreuth, Germany

Downloaded by SYRACUSE UNIV on August 31, 2015 | http://pubs.acs.org Publication Date (Web): August 27, 2015 | doi: 10.1021/acs.analchem.5b02272

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ABSTRACT: 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 μm2. The two hyperspectral data sets and the highresolution 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.

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formation and transformation of atmospheric particulate matter in more detail.5 In contrast to the analysis of overall filter and impactor samples, single particle techniques gain access to a more comprehensive understanding of physical, chemical, and physicochemical properties. For example, single tropospheric aerosol particles6 and coatings of atmospheric elemental carbon7 have been analyzed using mass spectrometry. Various methods have successfully been combined for single particle analysis of soot.8 Image-based analysis of deposited atmospheric particulate matter combines the advantages of single-particle techniques with overall bulk analysis. A detailed analysis of individual segregated aerosol particles with respect to their particle-based unique chemical composition and structure is possible. Optical and atomic-force imaging techniques gain access to a morphological interpretation of particulate matter. Köllensperger et al. studied the morphology and size-distribution of environmental samples using atomic force microscopy (AFM).9 Cryo-transmission electron microscopy was applied for morphological studies of submicrometer aerosol in order to distinguish between ammonium sulfate and organics.10 Attenuated-total-reflectance Fourier transform-infrared spectroscopy (ATR-FT-IR) was successfully combined with energy-

ource apportionment as well as describing chemical formation and transformation of atmospheric particulate matter are of utmost importance for atmospheric research in order to quantify the aerosols contribution to climate change and radiative forcing.1 Bulk analytical methods, which originate from the classical methods of chemical structure determination, of filter and impactor samples have been used to answer these questions so far. While various kinds of atmospheric particles exhibit a heterogeneous constitution, which is dominated by layers and coatings, the related information on the composition and chemical structure of single atmospheric particles and their size-dependent properties is lost by applying bulk-analytical methods. Physical properties (e.g., atmospheric albedo and radiative transfer), chemical reactivity (e.g., like photochemical aging), and physicochemical properties (e.g., like the ability to form cloud-condensation or ice nuclei and hygroscopicity) strongly depend on structure, layers, and coatings of atmospheric particulate matter. Several techniques have been applied to increase the understanding of these properties. Thermaldesorption proton-transfer-mass spectrometry2 and the socalled aerosol mass spectrometer3 are used to characterize sizesegregated secondary organic aerosol (SOA). Nondestructive methods such as vibrational spectroscopy allow the analysis of organic and inorganic species and even coatings.4 Furthermore, a combined approach of analytical techniques like vibrational spectroscopy, mass spectroscopy, and high-resolution imaging (scanning electron microscopy (SEM)) permits one to study © XXXX American Chemical Society

Received: June 16, 2015 Accepted: August 15, 2015

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DOI: 10.1021/acs.analchem.5b02272 Anal. Chem. XXXX, XXX, XXX−XXX

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Analytical Chemistry

chemical constituents.29 The sum of segregated particles exhibits inorganic salts, elemental carbon, and semivolatile organic carbon. Particles were sampled above the surface of Lake Orr (33°09′00″ S, 119°09′50″ E). A mobile aerosol chamber was set up (described in detail by Kamilli et al.29). Atmospheric particulate matter was sampled on aluminum foils inside this chamber using a Sioutas cascade impactor (SKC; Pennsylvania). One stage of the impactor was chosen for the multisensor analysis. Aerodynamic diameters of segregated particles range from 0.5 to 2.5 μm. Hyperspectral Imaging. Sections of the aluminum foils were prepared on SEM sample holders. First, Raman imaging of selected areas of the sections was performed using a HORIBA LabRam 800HR Raman microspectrometer with a 300 lines per mm grating and an Open-Electrode-CCD. Raman excitation was performed at 532 nm using a frequency-doubled NdYAG DPSS laser (OXXIUS) with a nominal power of 50 mW, operated at 25%. The acquisition of an area of 100 × 100 μm2 at a lateral resolution of 1 μm was done at 0.2 s per spot. Postprocessing of the acquired Raman data was performed using the software package LabSpec 6 (HORIBA). Subsequently, HRI imaging was performed by SEM. A Quanta FEI 200 scanning electron microscope was operated at an acceleration voltage of 10 kV and a magnification of 2600. EDX imaging of the selected sample spot was performed by the same instrument using an EDAX EDX-detector and an acceleration voltage of 20 kV. Elemental distribution maps of elements of interest were exported from the EDX data set using the software package TEAM Maps Review 3.51 (EDAX Inc.). Preprocessing and Multisensor Fusion. The individual hyperspectral data sets were imported into the software package ImageLab (www.imagelab.at) followed by several preprocessing steps of the HSI cubes: (1) The Raman data were first analyzed for spikes using the spike detection and removal tool of ImageLab (expected spike half width: 2 data values, threshold for removal 3.0). After removing the spikes the spectra were smoothed (Savitzky-Golay third-order polynomial smoothing with a window width of 7 data points). Finally pixels, where overdriving of the analog/digital converter of the instrument occurred, were identified and replaced. The identification of such pixels was done by detecting portions of the spectra which are free of noise. These nine (out of 10201) problematic pixels have been replaced by an interpolation of the neighboring (unproblematic) pixels. (2) For the EDX data set the preprocessing consisted only of a spatial smoothing step by applying a binomial convolution kernel of size 7 × 7 pixels to each of the individual images. Finally, both HSI data sets were coregistered to the same HRI obtained from the SEM. The HSI to HRI registration was performed by a bilinear affine transformation based on 10 (Raman) and 6 (EDX) tie points. The fusion of the two HSI data sets and the HRI was done by using the multisensor tool of ImageLab. The Raman data set was chosen as the master data set, and the EDX HSI cube was adjusted to the master. Details on the fusing process of MSHSI are given by Lohninger and Ofner.23 The resulting MSHSI cube exhibits a lateral resolution of 1 μm. Spectral Descriptors. The multivariate analysis of the MSHSI cube is based on the concept of so-called spectral descriptors (SPDCs) which have been introduced by Lohninger and Varmuza in the context of mass spectrometry.30 SPDCs allow one to cope with the “curse of dimensionality”31 in multivariate data analysis and to improve the structure of the

dispersive electron probe X-ray (EDX) microanalysis for studying atmospheric aerosol particles and microsized dust particles.11,12 Fluorescence imaging was recently applied to atmospheric biological aerosol particles.13 Hyperspectral imaging techniques enhance the significance of chemical images of submicrometer particles due to their spectral resolution. Dark-field microscopy-based hyperspectral imaging was successfully applied for semiquantitative analysis of nanoparticles.14 Confocal imaging techniques such as Raman microspectroscopy (RMS) enhance the potential of hyperspectral imaging due to their submicrometer lateral resolution: Batonneau et al. studied urban tropospheric aerosol particles with aerodynamic diameters between 1 and 10 μm.15 Heterogeneous chemistry of inorganic microparticles was uncovered by Falgayrac et al.16 Bioaerosol detection was demonstrated by Tripathi and Jabbour.17 Chemometric interpretation of confocal Raman images was performed by Sobanska et al., applying multivariate curve resolution (MCR) to study the chemistry of single aerosol particles.18 A novel concept is the combination of several different imaging-based methods. High-resolution images (HRI) can be combined with various hyperspectral imaging (HSI) techniques. Vibrational spectroscopy such as RMS supports the interpretation of elemental images (e.g., obtained from EDX) by providing detailed information on chemical bonding and thus enhances the feasibility of image-based chemical structure determination. Nelson et al. combined RMS and SEM to characterize ambient fine particulate matter.19 These techniques were also combined with sum-frequency generation spectroscopy by Ault et al.20 Individual dust particles were studied by Sobanska et al. applying the combination of RMS and SEM.21 In 2014, these authors published a combined approach of AFM, environmental scanning electron microscopy (ESEM) with EDX, RMS, and time-of-flight secondary ion mass spectrometry (TOF-SIMS).22 However, all acquired chemical images of the same sample spot have been analyzed side by side without fusing the hyperspectral data cubes to a single data set. Lohninger and Ofner introduced the concept of multisensor hyperspectral imaging (MSHSI) based on spectral descriptors for image based chemical structure determination.23 The concept of MSHSI can overcome the limitations of side-byside analysis of HSI data sets and brings the advantages of combining HSI cubes of lower resolutions with HRI images. The so-called MSHSI cube allows a joint analysis and interpretation of the acquired HSI data sets. The present study demonstrates the joint analysis of RMS and SEM-EDX HSI and HRI data in the form of a MSHSI data set subjecting it to principal component analysis (PCA),24 hierarchical cluster analysis (HCA),25 k-means clustering,26 and vertex component analysis (VCA).27 The application of MSHSI to precipitated atmospheric particulate matter gains access to a comprehensive understanding of the complex composition of aerosols but is only one example of the potential of combined chemometric analysis of multisensor hyperspectral data sets.

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METHODOLOGY Sampling Site and Sampling. For this study, atmospheric particulate matter was sampled above a Western Australian salt lake. Western Australian salt lakes emit atmospheric particulate matter ranging from ultrafine particles28 (with a diameter of a few nanometers) up to coarse-mode particles (several micrometers). The composition and relationship of these particles appears to be very complex due to their varying origin, size, and B


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Figure 1. Explanation of the three types of spectral descriptors used in this work: (a) peak intensities, (b) peak area with the baseline subtracted, and (c) correlation to a triangular template peak (blue shading).

information in the data space. In this work, three different types of SPDCs have been chosen: peak intensities, peak areas, and correlation descriptors. Figure 1 depicts the definitions of these three types. While the first two types of descriptors (Figure 1a,b) are obvious, the template correlation (TC) descriptor (Figure 1c) needs to be explained. The TC descriptor calculates the product of the baseline-corrected peak area and the correlation between the spectrum and a triangular template peak whose form is controlled by the parameters a1, b1, and b2. More details and an example of this kind of descriptor as well as the discussion of its advantages can be found in Lohninger and Ofner.25 The SPDCs for the RMS cube were selected manually guided by the mean Raman spectrum of the hyperspectral cube. For the EDX cube, SPDCs were simply the individual elemental intensities. The list of the selected SPDCs, their allocation, and their spectroscopic background is given in Table 1. According to Table 1, SPDCs 01 and 02 (ν(C−H) stretch vibration) describe organic carbon species, which can be linked to secondary organic aerosol (SOA) or condensed organic matter. SPDCs 03 to 05 can be related to soot, where SPDC 04 is related to the G band and SPDC 05 to the D band.32 The SPDCs 06 to 11 represent the main Raman excitations of inorganic salts. SPDC 14 matches the Raman spectrum of elemental silicon and SPDCs 12, 13, 15, and 16 are specific to titanium dioxide. While SPDCs 01 to 16 are based on the RMS data, SPDCs 17 to 28 specify major elements in the EDX part of the data set. SPDC 29 is the backscatter signal of the EDX. Applied Chemometric Methods. Principal components (PC) of the MSHSI data cube were extracted using PCA24 on a SPDC-based approach. Every PC is described as a linear combination of the multisensor SPDCs. HCA25 was performed on the loadings of the PCA based on the orthogonal space, which is spanned by the chosen number of PCs. The number of PCs has been chosen by maximizing the ratio of the inter- to the intracluster distances. Thus, chemically correlated SPDCs can be linked to each other and be extracted as a subcluster of the HCA, indicating a single chemical compound. In contrast to the PCA-HCA combination, k-means26 clustering was applied to pixel-based MSHSI data cube, also based on the concept of MSHSI SPDCs. Spectral endmembers can be extracted from a HSI data set by applying VCA.27 VCA is an unsupervised extraction algorithm to unmix linear mixtures of spectral endmembers. The algorithm is based on the geometry of convex sets, where spectral endmembers are represented by the vertices of a simplex. VCA assumes the presence of spectroscopically pure pixels (which need not necessarily be free of noise). Pure pixels cannot be expected in hyperspectral data sets. This can cause

Table 1. List of Chosen SPDCs with Their Spectral Description, Allocation to the Related HSI Cube, and Their Chemical Meaning SPDC 01 02 03 04 05 06 07 08 09 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

type a

ABL TCc 2915 cm−1 ABL TC 1592 cm−1 TC 1349 cm−1 TC 1388 cm−1 TC 1068 cm−1 TC 1052 cm−1 TC 1011 cm−1 TC 994.4 cm−1 TC 726.6 cm−1 ABL TC 614.3 cm−1 TC 522.1 cm−1 ABL TC 446.9 cm−1 PKc C Kα PK Ca Kα PK Cl Kα PK Fe Kα PK K Kα PK Mg Kα PK N Kα PK Na Kα PK O Kα PK S Kα PK Si Kα PK Ti Kα PK BSd

spectral range −1

2750−3093 cm 2788−3032 cm−1 1135−1724 cm−1 1433−1702 cm−1 1119−1478 cm−1 1362−1417 cm−1 1042−1102 cm−1 1035−1075 cm−1 980−1048 cm−1 971−1021 cm−1 692−754 cm−1 551−699 cm−1 561−664 cm−1 494−558 cm−1 378−508 cm−1 389−504 cm−1 0.277 kV 3:690 kV 2.621 kV 6.398 kV 3.312 kV 1.253 kV 0.392 kV 1.041 kV 0.525 kV 2.307 kV 1.739 kV 4.507 kV

cube

allocation

RMS RMS RMS RMS RMS RMS RMS RMS RMS RMS RMS RMS RMS RMS RMS RMS EDX EDX EDX EDX EDX EDX EDX EDX EDX EDX EDX EDX EDX

SOA SOA soot soot soot NaNO3 NaNO3 Ca(NO3)2 CaSO4 Na2SO4 NaNO3 TiO2 TiO2 silicon TiO2 TiO2 C Ca Cl Fe K Mg N Na O S Si Ti backscatter

a

ABL: integral descriptor. cTC: triangle template peak descriptor. cPK: peak descriptor. dBackscatter signal.

endmember spectra, which are composed of a mixture of single component spectra. Thus, the results of VCA have to be crosschecked with the results of other chemometric methods or reference spectra. The algorithm performs several times faster than comparable algorithms (e.g., NFINDR and PPI) with less computational complexity and achieves reasonable results even for a nonpure pixel HSI data set.27 VCA was applied to the single HSI cubes (RMS and EDX) of the MSHSI data set without the use of SPDCs. Therefore, VCA allows the verification and completion of the chosen SPDCs. C


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Figure 2. Biplot of PC2 vs PC3. Both the descriptors specific to titanium dioxide and soot are clearly separated from the remaining descriptors. The corresponding scores have been plotted on top of the SEM image at the right, indicating particles covered by soot (orange) and a rutile particle (blue).

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CHEMOMETRIC ANALYSIS OF THE MULTISENSOR HYPERSPECTRAL CUBE Principal Component Analysis (PCA). The PCA of MSHSI was calculated using the overall set of SPDCs as listed in Table 1. According to the eigenvalue plot, 9 principal components (PC) exhibit eigenvalues above 1. In order to interpret the PCA results, the well-known biplots of several combinations of PCs were examined visually (Figure 2 as an example). Since the interpretation of the biplots becomes difficult for data sets containing more than a few constituents, the further analysis was based on an HCA of selected eigenvectors of the PCA. For this particular data set, the selection of seven PCs (covering a total variance of 64%) returned the best results according to the inter- to the intracluster distance ratio. The HCA of the eigenvector loadings showed eight clearly discernible groups of subclusters (subclusters A−H in Figure 3), which can be related to single chemical species. A titanium dioxide particle was found in subcluster A, which is characterized by the RMS SPDCs 12, 13, 15, and 16 as well as the EDX SPDC 28 for titanium. Subcluster B is dominated by the EDX SPDCs of sodium chloride −19 for Cl and 24 for Na. The backscattering signal (EDX SPDC 29) of the EDX also contributes to this subcluster. Impurities of the aluminum foil are represented by subcluster C, which exhibits contributions of silicon (RMS SPDC 14 and EDX SPDC 27) and iron (EDX SPDC 20). Calcium sulfate (CaSO4) is represented by the RMS SPDC 9 and the EDX SPDCs 18 for Ca and 26 for S in subcluster D. Subcluster E includes unspecific specimens exhibiting low spectral intensities. Secondary organic aerosol or condensed organic matter is represented by subcluster F, which is defined by the RMS SPDCs 1 and 2 and the EDX SPDC 17 for carbon. In case of sodium nitrate (NaNO3), three SPDCs of the RMS cube (SPDCs 6, 7, and 11) and two SPDCs of the EDX

Figure 3. Cluster analysis of the loadings of the first 7 principal components (covering 64% of the total amount of variation) reveals the eight groups of substances.

cube (SPDCs 23 for N and 25 for O) form subcluster G. Soot in subcluster H is only characterized by SPDCs belonging to the RMS cube (SPDCs 3 to 5). Each subcluster image was blended with the SEM image resulting in colored images, which indicate the assignment of the particles (Figure 4). The application of the PCA to the MSHSI cube allows a parallel detection of EDX-only active D


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Figure 4. Combined subcluster images from the HCA of the PCA loadings: (left) yellow, SOA; orange-brown, soot; cyan, Si/Fe in aluminum foil; blue, titanium dioxide; (right) green, sodium nitrate; pink, sodium chloride; red, calcium sulfate.

species (e.g., NaCl), RMS-only active species (e.g., soot), and RMS-EDX active species (e.g., NaNO3, CaSO4, SOA, or TiO2). The combined analysis of Raman and EDX spectra unveiled that some aggregated particles are coated by soot or other volatile organic phases (Figure 5). The example of an

Figure 5. (a) SEM image of an aggregated particle; (b) yellow, calcium sulfate; pink, sodium chloride; green, sodium nitrate; (c) orangebrown area indicates soot. Please note that the red spot is due to the color mixing of the brown and the pink areas and indicates sodium chloride beneath some (invisible) soot particle.

atmospheric aggregate, given in Figure 5, exhibits not only a heterogeneous composition, composed of inorganic species, but also coverage of organic carbon. The soot signature might also relate to SOA, which was burned by the Raman laser. However, a spatially resolved chemical analysis of heterogeneous internally mixed aerosol particles is visible. The part in the center of the agglomerate is not covered by the chemometric approach, while the pure EDX spectrum exhibits a strong silicon contribution, which might be related to silicates (Figure 5a−c). k-Means Clustering. On the basis of the SPDCs defined in Table 1, k-means clustering was performed assuming 10 clusters as the target number of clusters. The data has been qnormalized prior to clustering. As the previous PCA analysis revealed seven components, the extra number of clusters (10 instead of 7) was used to introduce some degrees of freedom to the clustering process. The results of the k-means clustering mainly resemble the results of PCA (Figure 6). Six main constituents were found; a few cluster centers were assigned to the same spot (i.e., titanium dioxide, which shows

Figure 6. k-means clustering indicating 6 main constituents: NaNO3 (green), NaCl (pink), soot (red), SOA (orange-brown), silicon in the aluminum foil (yellow), and TiO2 (cyan). Please note the square pattern of the clustering results which corresponds to the pixel resolution of the image and which cannot be smoothed or interpolated due to the inherent nature of k-means clustering. The circled spots 1 and 2 are an extreme example where the class assignment changes within the same apparent particle.

intense Raman spectra) thus confirming the initial PCA based analysis. Some of the spots could not be clearly resolved leading to clusters of combined assignments (i.e., NaNO3 and soot). Altogether, k-means proved to be less sensitive to constituents exhibiting only low concentrations and/or low frequencies of occurrence. The clusters summarized in Table 2 have been found. One particular drawback of clustering algorithms is their E


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Analytical Chemistry Table 2. Clusters Found by k-Means Clusteringa cluster

no. of pixels

background NaNO3 and NaCl Si in the aluminum foil SOA NaNO3 and soot soot TiO2

9088 295 193 112 101 32 + 18 (2 clusters) 11 + 6 + 5 (3 clusters)

spectrometry of airborne particles.34,35 However, with increasing fuzziness, fuzzy clustering tends to overlook clusters of lowintensity spectra. Vertex Component Analysis (VCA). Vertex component analysis was used to extract endmembers from the MSHSI data cube. As the data contains considerable noise, principal component analysis was used to reduce the noise, allowing VCA to be more efficient in recognizing pure components. The obtained endmembers and corresponding Raman and/or EDX spectra S1 to S10 are displayed in Figure 7. Five pure endmembers (S6 to S10 in Figure 7) could be extracted from the RMS HSI cube (NaNO3, SOA, soot, TiO2, and elemental silicon), the extraction of EDX endmembers yielded five components (S1 to S5 in Figure 7), although one of them being not entirely pure (CaSO4). The extracted CaSO4 spectrum was considerably mixed with other components, thus the concentration map has to be interpreted carefully (i.e., by comparison with the Raman spectrum). Three constituents (NaNO3, TiO2 and Si) occurred in the endmembers extracted from both the RMS and the EDX data (e.g., NaNO3 as spectra 5 and 6 in Figure 7). The images obtained from the

a


NaNO3, NaCl, and soot could not be fully separated, which led to mixed clusters containing NaNO3 and NaCl and NaNO3 and soot. CaSO4 was not detected at all.

inability to resolve overlapping regions, thus forcing for each pixel a decision to which cluster it belongs. This may result in class assignments which alternate from pixel to pixel (see spots 1 and 2 in Figure 6) and which makes it considerably harder to interpret multisensor images correctly. A solution to this kind of problems of “crisp” clustering could be the use of fuzzy cmeans clustering,33 which was successfully applied to mass

Figure 7. Extracted endmember spectra obtained from the VCA: S1 to S5, EDX endmember; S6 to S10, RMS endmember. F


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Figure 8. Concentration maps of pure components obtained from the VCA of RMS and EDX data. (Image A) green, NaNO3 (EDX and Raman, S5, S6); blue, TiO2 (EDX and Raman, S3, S9); orange, soot (Raman, S8); yellow, SOA (Raman, S7). (Image B) green, Si (EDX, S4); pink, NaCl (EDX, S1); yellow, CaSO4 (impure, EDX, S2). (Image C) red, Si (EDX, S4); green, Si (Raman, S10).

Endmember extraction using VCA of the individual data cubes within the MSHSI worked fine and allowed a confirmation of the results of the two other methods. Endmembers could be extracted from both subcubes of the MSHSI. While the concentration maps of Si/Fe showed significant differences in the VCA of the Raman and the EDX cubes (see discussion above), all other concentration maps of substances which deliver a signal both in EDX and Raman (i.e., NaNO3 and TiO2) exhibited an excellent alignment. VCA is therefore well suited for spectral based species detection. The algorithm assists selection and verification of SPDCs. However, while VCA persuades according to processing time and quality of extracted endmember spectra, there is still the need for selection of the assumed number of endmembers and spectral identification and allocation of obtained endmember spectra. In contrast, a more easy access to the chemical information and interpretation is provided by the SPDC-based approach of the PCA-HCA combination. This method gains access to a future automated analysis and quantification of MSHSI data sets. The multisensor fusion of hyperspectral images and their blending with high-resolution SEM images proved to be a valuable tool for the elucidation of chemical composition of particulate matter. Only very small particles at the limit of the spatial resolution of the used techniques could not be assigned unequivocally to a specific chemical composition. As almost all particles of the sample could be allocated to chemical species, the quantification of the amount of different species would be the logical next step of the investigation. Further, this imagebased analysis of atmospheric particulate matter allows for a decrease in the limit of detection and, therefore, to analyze particulate matter even at low mass loadings of the available sample. The analysis of particulate matter is only one example of the potential of multisensor hyperspectral imaging. The chemometric analysis of MSHSI data sets gains access to a comprehensive understanding of various samples, where single methods or even side-by-side analysis using multiple methods do not satisfy.

corresponding endmember densities showed excellent correlations for NaNO3 and TiO2 (Figure 8). In the case of Si, the endmember density maps of RMS and EDX do not correlate (Figure 8c). This lack of fit for silicon (which forms an impurity of the aluminum foil used as the sampling substrate) can be explained by different penetration and excitation depths of the Raman laser and the electron beam of the SEM. While the characteristic Raman signal is obtained from the surface of the foil, the characteristic X-ray spectrum results from the bulk. Hence, different layers of the foil are depicted resulting in varying the chemical information obtained from these layers. However, for precipitated particles, the same origin of the Raman excitation and the characteristic X-ray photons can be expected.

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CONCLUSION AND OUTLOOK The chemometric analysis of fused hyperspectral data cubes allowed a detailed and well-grounded assignment of chemical species and their relationship to each other. The fusion of HSI data cubes and HRIs allows a more detailed image-based chemometric analysis. The application of PCA to the MSHSI data cube uncovered the presence of seven important species. By performing a HCA of the PCA loadings, seven chemical constituents could be recognized in the fused data set. Soot is characterized by SPDCs solely from the RMS part of the MSHSI, NaCl, and the Si/Fe background from the EDX part. NaNO3, CaSO4, TiO2, and SOA exhibited contributions of both cubes and hence are fully defined within the fused MSHSI data cube. The combinational approach of PCA-HCA based on SPDCs allowed a subcluster based identification of multisensor active chemical species. Only six of the seven species can be found by k-means clustering of the MSHSI. NaNO3, TiO2, soot, SOA, silicon, and NaCl can be identified as large particles within a designated cluster. K-means proved to be less sensitive to constituents exhibiting only low concentrations and a low frequency of occurrence, which makes it particularly unsuitable for the detection of rare particles occurring only once or twice in a sample (unless the spectral signature of this component is unique and prominent, contrasting itself from the rest of the sample spectra). However, k-means clustering provides a fast overview on species distributions based on SPDCs and can be used to verify PCA-HCA results on a pixel based approach.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Phone +43 (0)1 5880115177. Notes

The authors declare no competing financial interest. G


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(21) Sobanska, S.; Hwang, H.; Choël, M.; Jung, H.-J.; Eom, H.-J.; Kim, H.; Barbillat, J.; Ro, C.-U. Anal. Chem. 2012, 84 (7), 3145−3154. (22) Sobanska, S.; Falgayrac, G.; Rimetz-Planchon, J.; Perdrix, E.; Brémard, C.; Barbillat, J. Microchem. J. 2014, 114, 89−98. (23) Lohninger, H.; Ofner, J. Spectrosc. Eur. 2014, 26 (5), 6−10. (24) Jolliffe, I. T. Principal Component Analysis, 2nd ed.; 2002. (25) Kaufman, L.; Rousseeuw, P. J. Finding Groups in Data: An Introduction to Cluster Analysis; John Wiley & Sons, Inc.: Hoboken, NJ, 1990. (26) MacQueen, J. B. 5th Berkeley Symp. Math. Stat. Probab. 1967 1967, 1 (233), 281−297. (27) Nascimento, J. M. P.; Dias, J. M. B. IEEE Trans. Geosci. Remote Sens. 2005, 43 (4), 898−910. (28) Junkermann, W.; Hacker, J.; Lyons, T.; Nair, U. Atmos. Chem. Phys. 2009, 9 (17), 6531−6539. (29) Kamilli, K. A.; Ofner, J.; Krause, T.; Sattler, T.; Schmitt-Kopplin, P.; Atlas, E.; Eitenberger, E.; Friedbacher, G.; Lendl, B.; Lohninger, H.; Schöler, H. F.; Held, A. Atmos. Chem. Phys. Discuss. 2015, in preparation. (30) Lohninger, H.; Varmuza, K. Anal. Chem. 1987, 59, 236−244. (31) Bellman, R. E. Adaptive Control Processes: A Guided Tour; Princeton University Press: Princeton, NJ, 1961. (32) Ferrari, A. C.; Basko, D. M. Nat. Nanotechnol. 2013, 8 (4), 235− 246. (33) Bezdek, J. C.; Ehrlich, R.; Full, W. Comput. Geosci. 1984, 10, 191−203. (34) Hinz, K. P.; Greweling, M.; Drews, F.; Spengler, B. J. Am. Soc. Mass Spectrom. 1999, 10 (7), 648−660. (35) Held, A.; Hinz, K. P.; Trimborn, A.; Spengler, B.; Klemm, O. J. Aerosol Sci. 2002, 33 (4), 581−594.

The authors provide the multisensor data set and the spectral descriptors used in this investigation. Further information can be found in the ImageLab data repository (http://www. imagelab.at/en_data_repository.html), see data set “DS005: Atmospheric Particulate Matter”.

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ACKNOWLEDGMENTS The authors would like to thank the German Research Foundation (DFG) for funding of the field measurement campaign within the research unit HALOPROC (DFG Grant RU 763).


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