TOF-SIMS analysis of polystyrene/polybutadiene blend using chemical derivatization and multivariate analysis

Chemical imaging with high spatial resolution is one of the features of TOF-SIMS. However, degradation of the sample due to primary ion bombardment becomes problematic when the analysis area is small. Although polystyrene (PS) and polybutadiene (PB) separately show relatively distinct spectra, observation of their phase separation in PS/PB blends is difficult when the analysis area is small because degradation of both polymers and especially PS leads to disappearance of their characteristic peaks, resulting in low chemical image contrast. We therefore investigated the application of various forms of multivariate analysis (MVA) to the TOF-SIMS image data to improve the chemical image contrast. PCA, MCR, and the other forms of MVA provided improvement in contrast, but the images were still obscure and observation of phase separation remained difficult. Chemical derivatization using osmium tetroxide was also investigated, and found to give clear images of phase separation in the PS/PB blend. In quantitative determinations with MVA and chemical derivatization, PLS demonstrated the best predictive capability and chemical derivatization resulted in large deviations from both the bulk chemical composition and the determinations with MVA, particularly in regions of low PB content.     

Towards quantitative chemical imaging with ToF-SIMS

Methods for processing ToF-SIMS spectra and images have advanced significantly in recent years to include multivariate analysis methods. Multivariate methods can reduce noise, identify and quantify chemical components, and segment images into discrete chemical phases. To date, these methods have focused on the analysis of single images; however, quantitative or semi-quantitative methods for comparison of multiple images collected across multiple samples have lagged in development. This study evaluates simple noise reduction and scaling methods to facilitate semi-quantitative comparison of images collected across several samples with varying acquisition conditions. Down-binning, Poisson-scaling, and nearest-neighbor smoothing methods improved signal-to-noise in image datasets, with nearest-neighbor smoothing providing the greatest improvement. Image scaling methods including pixel-by-pixel (PbP) normalization and scalar multiplication were found to improve the relative quantification of images. While PbP normalization methods performed well for relatively flat samples, such methods were not suitable for samples with significant topography. Scaling methods using scalar multiplication of individual secondary ion images and histogram analyses facilitated semi-quantitative comparison of these samples.     

Laser activation-modification of semiconductor surfaces (LAMSS) of 1-alkenes on silicon: A ToF-SIMS, chemometrics, and AFM analysis

Laser-activation-modification of semiconductor surfaces (LAMSS) was carried out on silicon with a series of 1-alkenes. These laser spots were studied by time of flight secondary ion mass spectrometry (ToF-SIMS). The resulting spectra were analyzed using the multivariate curve resolution (MCR) method within the Automated eXpert Spectral Image Analysis (AXSIA) toolkit, and also by MCR and cluster analysis using commercially available toolboxes for Matlab: the PLS_Toolbox and the MIA_Toolbox, respectively. AXSIA based MCR generally finds three components for the spectral images: one for the background and two for the laser-activated spots, for both the positive and negative ion images. The negative ion component spectra from the spots show increased carbon and hydrogen signals compared to oxygen. They also show reduced chlorine and fluorine (contamination) peaks. In order to compare AXSIA-MCR results from different images, the AXSIA component spectra of different spots were further analyzed by principal components analysis (PCA). PCA of all of the negative ion components shows that component 1 is chemically distinct from components 2 and 3. PCA of all of the positive ion components yields the same result. The loadings plots of this PCA analysis confirm that component 1 generally contains fragments expected from the substrate, while components 2 and 3 contain fragments expected from an overlayer composed of alkyl chains in the spots. A comparison of the two MCR analyses suggests that roughly the same information can be obtained from AXSIA, which is not commercially available, and the PLS_Toolbox. Cluster analysis of the data also clearly separates the spots from the backgrounds. A key finding from these analyses is that the degree of surface functionalization in a LAMSS spot appears to decrease radially from the center of the spot. Finally, a comparison of atomic force microscopy (AFM) of the spots versus the AXSIA analysis of the ToF-SIMS data produced another important result, which is that the surface morphology is only weakly correlated with the LAMSS chemistry.     

Using time-of-flight secondary ion mass spectrometry and multivariate statistical analysis to detect and image octabenzyl-polyhedral oligomeric silsesquioxane in polycarbonate

Silsesquioxane, with an empirical formula of RSiO_3/2, has the potential to combine the mechanical properties of plastics with the oxidative stability of ceramics in one material [D.W. Scott, J. Am. Chem. Soc. 68 (1946) 356; K.J. Shea, D.A. Loy, Acc. Chem. Res. 34 (2001) 707; K.-M. Kim, D.-K. Keum, Y. Chujo, Macromolecules 36 (2003) 867; M.J. Abad, L. Barral, D.P. Fasce, R.J.J. William, Macromolecules 36 (2003) 3128]. The high sensitivity, surface specificity, and ability to detect and image high mass additives make time-of-flight secondary ion mass spectrometry (ToF-SIMS) a powerful surface analytical instrument for the characterization of polymer composite surfaces in an analytical laboratory [J.C. Vickerman, D. Briggs (Eds.), ToF-SIMS Surface Analysis by Mass Spectrometry, Surface Spectra/IMPublications, UK, 2001; X. Vanden Eynde, P. Bertand, Surf. Interface Anal. 27 (1999) 157; P.M. Thompson, Anal. Chem. 63 (1991) 2447; S.J. Simko, S.R. Bryan, D.P. Griffis, R.W. Murray, R.W. Linton, Anal. Chem. 57 (1985) 1198; S. Affrossman, S.A. O’Neill, M. Stamm, Macromolecules 31 (1998) 6280]. In this paper, we compare ToF-SIMS spectra of control samples with spectra generated from polymer nano-composites based on octabenzyl-polyhedral oligomeric silsesquioxane (BnPOSS) as well as spectra (and images) generated from multivariate statistical analysis (MVSA) of the entire spectral image. We will demonstrate that ToF-SIMS is able to detect and image low concentrations of BnPOSS in polycarbonate. We emphasize the use of MVSA tools for converting the massive amount of data contained in a ToF-SIMS spectral image into a smaller number of useful chemical components (spectra and images) that fully describe the ToF-SIMS measurement.     

ToF-SIMS PC-DFA analysis of prostate cancer cell lines

Three closely related cancer cell lines have been analysed with ToF-SIMS using a C₆₀⁺ primary ion beam. Principal component-discriminant function analysis (PC-DFA) has been applied for spectral classification. Various spectral pre-processing methods are discussed and assessed for optimum discrimination of this data set. The sum-normalised PC-DFA spectral model produced sensitivities as high as 83.3% and specificities as high as 100% at the 99% confidence limit. At this confidence limit only one errant spectrum was misclassified. The resulting loadings plots suggest that a range of lipid and amino-acid related signals are responsible for the cell line discrimination.     

Comparison of chemometric methods in the analysis of pharmaceuticals with hyperspectral Raman imaging

Chemical imaging method of vibrational spectroscopy, which provides both spectral and spatial information, creates a three‐dimensional (3D) dataset with a huge amount of data. When the components of the sample are unknown or their reference spectra are not available, the classical least squares (CLS) method cannot be applied to create visualized distribution maps. Raman image datasets can be evaluated even in such cases using multivariate (chemometric) methods for extracting the needed hidden information. The capability of chemometrics‐assisted Raman mapping is evaluated through the analysis of pharmaceutical tablets (considered as unknown) with the aim of estimating the pure component spectra based on the collected Raman image. Six chemometric methods, namely, principal component analysis (PCA), maximum autocorrelation factors (MAF), sample–sample 2D correlation spectroscopy (SS2D), self‐modeling mixture analysis (SMMA), multivariate curve resolution–alternating least squares (MCR‐ALS), and positive matrix factorization (PMF), were compared. SMMA was found to be the best choice to determine the number of components. MCR‐ALS and PMF provided the pure component spectra with the highest quality. MCR‐ALS was found to be superior to PMF in the estimation of Raman scores (which correspond to the concentrations) and yielded almost the same results as CLS (using the real reference spectra). Thus, the combination of Raman mapping and chemometrics could be successfully used to characterize unknown pharmaceuticals, identify their ingredients, and obtain information about their structures. This may be useful in the struggles against illegal and counterfeit products and also in the field of pharmaceutical industry when contaminants are to be identified. Copyright © 2011 John Wiley & Sons, Ltd.     

ToF-SIMS studies of Bacillus using multivariate analysis with possible identification and taxonomic applications

In this paper we discuss the application of ToF-SIMS with an Au₃⁺ primary ion beam, combined with principal components analysis (PCA) and discriminant function analysis (DFA) for the identification of individual strains of two Bacillus species. The ToF-SIMS PC-DFA methodology is capable of distinguishing bacteria at the strain level based on analysis of surface chemical species. By classifying the data using hierarchical cluster analysis (HCA) we are able to show quantitative separation of species and of these strains. This has taxonomic implications in the areas of rapid identification of pathogenic microbes isolated from the clinic, food and environment.     

Improved DOSY NMR data processing by data enhancement and combination of multivariate curve resolution with non-linear least square fitting

The quality of DOSY NMR data can be improved by careful pre-processing techniques. Baseline drift, peak shift, and phase shift commonly exist in real-world DOSY NMR data. These phenomena seriously hinder the data analysis and should be removed as much as possible. In this paper, a series of preprocessing operations are proposed so that the subsequent multivariate curve resolution can yield optimal results. First, the baseline is corrected according to a method by Golotvin and Williams. Next, frequency and phase shift are removed by a new combination of reference deconvolution (FIDDLE), and a method presented by Witjes et al. that can correct several spectra simultaneously. The corrected data are analysed by the combination of multivariate curve resolution with non-linear least square regression (MCR-NLR). The MCR-NLR method turns out to be more robust and leads to better resolution of the pure components than classic MCR.     

Surface characterization of plasma-modified resist patterns by ToF-SIMS analysis

We report the use of the time-of-flight secondary ion mass spectrometry (ToF-SIMS) technique to determine whether the patterned bank is suitable for inkjet printing, by evaluating the phobicity contrast between two regions, the glass substrate inside pixels and the surface of the resist bank. We first examined the effect of plasma treatment on the ink spreading behavior inside pixels. The phobicity contrast was optimized by removing residues inside the pixels and by providing high phobicity on the bank surface. We show that ToF-SIMS spectra and mass-resolved images are effective tools in examining the existence of organic contaminants inside pixels and predicting the actual inkjet printing behavior. The ToF-SIMS technique will find promising applications that are related to surface characteristics where conventional contact angle measurement is hard to apply due to geometrical and technical restrictions.     

ToF-SIMS PCA analysis of Myrtus communis L.

Nowadays there is a growing interest of researchers for the application of sophisticated analytical techniques in conjunction with statistical data analysis methods to the characterization of natural products to assure their authenticity and quality, and for the possibility of direct analysis of food to obtain maximum information. In this work, time-of-flight secondary ion mass spectrometry (ToF-SIMS) in conjunction with principal components analysis (PCA) are applied to study the chemical composition and variability of Sardinian myrtle (Myrtus communis L.) through the analysis of both berries alcoholic extracts and berries epicarp. ToF-SIMS spectra of berries epicarp show that the epicuticular waxes consist mainly of carboxylic acids with chain length ranging from C20 to C30, or identical species formed from fragmentation of long-chain esters. PCA of ToF-SIMS data from myrtle berries epicarp distinguishes two groups characterized by a different surface concentration of triacontanoic acid. Variability in antocyanins, flavonols, α-tocopherol, and myrtucommulone contents is showed by ToF-SIMS PCA analysis of myrtle berries alcoholic extracts.     

Comparison of multivariate linear regression methods in micro‐Raman spectrometric quantitative characterization

Chemical imaging was used in this study as a powerful analytical tool to characterize pharmaceuticals in solid form. The majority of analyses are evaluated with bilinear modelling using only the pure component spectra or just the chemical images themselves to estimate the concentrations in each pixel, which are far from true quantitative determination. Our aim was to create more accurate concentration images using regression methods. For the first time in chemical imaging, variable selections with interval partial least squares (PLS) and with genetic algorithms (PLS‐GA) were applied to increase the efficiency of the models. These were compared to numerous bilinear modelling and multivariate linear regression methods such as univariate regression, classical least squares (CLS), multivariate curve resolution–alternating least squares (MCR‐ALS), principal component regression (PCR) and partial least squares (PLS). Two component spray‐dried pharmaceuticals were used as a model. The paper is shown that, in contrast to the usual way of using either external validation or cross‐validation, both should be performed simultaneously in order to get a clear picture of the prediction errors and to be able to select the appropriate models. Using PLS with variable selection, the root mean square errors were reduced to 3% per pixel by keeping only those peaks that are truly necessary for the estimation of concentrations. It is also shown that interval PLS can point out the best peak for univariate regression, and can thereby be of great help even when regulations allow only univariate models for product quality testing. Variable selection, besides yielding more accurate overall concentrations across a Raman map, also reduces the deviation among pixel concentrations within the images, thereby increasing the sensitivity of homogeneity studies. Copyright © 2015 John Wiley & Sons, Ltd.     

A soft-segmentation visualization scheme for magnetic resonance images

Prevalent visualization tools exploit gray value distribution in images through modified histogram equalization and matching technique, referred to as the window width/window level-based method, to improve visibility and enhance diagnostic value. The window width/window level tool is extensively used in magnetic resonance (MR) images to highlight tissue boundaries during image interpretation. However, the identification of different regions and distinct boundaries between them based on gray-level distribution and displayed intensity levels is extremely difficult because of the large dynamic range of tissue intensities inherent in MR images. We propose a soft-segmentation visualization scheme to generate pixel partitions from the histogram of MR image data using a connectionist approach and then generate selective visual depictions of pixel partitions using pseudo color based on an appropriate fuzzy membership function. By applying the display scheme in clinical examples in this study, we could demonstrate additional overlapping regions between distinct tissue types in healthy and diseased areas (in the brain) that could help improve the tissue characterization ability of MR images.     

Validation of fuzzy logic method for automated mass spectral classification for mineral imaging

Imaging mass spectrometry requires the acquisition and interpretation of hundreds to thousands of individual spectra in order to map the mineral phases within heterogeneous geomatrices. A fuzzy logic inference engine (FLIE) was developed to automate data interpretation. To evaluate the strengths and limitations of FLIE, the chemical images obtained using FLIE were compared with those developed using two chemometric methods: principle component analysis (PCA) and cluster analysis (K-Means). Two heterogeneous geomatrices, a low-grade chalcopyrite ore and basalt, were imaged using a laser-desorption Fourier transform mass spectrometer. Similar mineral distribution patterns in the chalcopyrite ore sample were obtained by the three data analysis methods with most of the differences occurring at the interfaces between mineral phases. PCA missed one minor mineral phase in the chalcopyrite ore sample and did not clearly differentiate among the mineral classes of the basalt. K-Means cluster analysis differentiated among the various mineral phases in both samples, but improperly grouped some spectra in the chalcopyrite sample that only contained unanticipated high mass peaks. Unlike the chemometric methods, FLIE was able to classify spectra as unknowns for those spectra that fell below the confidence level threshold. A nearest neighbor approach, included in FLIE, was used to classify the unknowns to form a visually complete image; however, the unknowns identified by FLIE can be informative because they highlight potential problems or overlooked results. In conclusion, this study validated the fuzzy logic-based approach used in our laboratory and reveald some limitations in the three techniques that were evaluated.     

Differentiation between human normal colon mucosa and colon cancer tissue using ToF-SIMS imaging technique and principal component analysis

Human normal colon mucosa and colon cancer tissue were studied using the time-of-flight secondary ion mass spectroscopy (ToF-SIMS) and principal component analysis (PCA) techniques. The surfaces of the tissues were successfully cleaned by C₆₀²⁺ cluster-ion beams before the ToF-SIMS images were obtained. A PCA on the spectra and images were performed to compare differences in the peaks and images of normal and cancer tissues. Significant differences in principal component 1 (PC 1) score values for normal and cancer tissues were observed, and each PC 1 loadings had a specific peak profile of proteins. In addition, the PC images obtained from the ToF-SIMS images for normal and cancer tissues were clearly distinguishable, and the amino acid fragments associated with normal and cancer tissues were found to have originated from the lamina propria region and the epithelium cells, respectively. Based on the PCA results, structural distortion of the crypts in the cancer colon tissue could be attributed to the proliferation of the cancerous epithelium cells. This work shows that the application of the ToF-SIMS imaging technique with PCA could be a useful method of obtaining valuable information for cancer analysis.     

Multiscale top-hat selection transform based infrared and visual image fusion with emphasis on extracting regions of interest

To effectively combine regions of interest in original infrared and visual images, an adaptively weighted infrared and visual image fusion algorithm is developed based on the multiscale top-hat selection transform. First, the multiscale top-hat selection transform using multiscale structuring elements with increasing sizes is discussed. Second, the image regions of the original infrared and visual images at each scale are extracted by using the multiscale top-hat selection transform. Third, the final fusion regions are constructed from the extracted multiscale image regions. Finally, the final fusion regions are combined into a base image calculated from the original images to form the final fusion result. The combination of the final fusion regions uses the adaptive weight strategy, and the weights are adaptively obtained based on the importance of the extracted features. In the paper, we compare seven image fusion methods: wavelet pyramid algorithm (WP), shift invariant discrete wavelet transform algorithm (SIDWT), Laplacian pyramid algorithm (LP), morphological pyramid algorithm (MP), multiscale morphology based algorithm (MSM), center-surround top-hat transform based algorithm (CSTHT), and the proposed multiscale top-hat selection transform based algorithm. These seven methods are compared over five different publicly available image sets using three metrics of spatial frequency, mean gradient, and Q. The results show that the proposed algorithm is effective and may be useful for the applications related to the infrared and visual image fusion.     

Surface segregational behaviour studied as an effect of thickness by SIMS and AFM in annealed PS-PMMA blend and block copolymer thin films

The surface behaviour of a two-phase polymer mixture depends on the chemical structure of the polymer components, the interaction between the two polymers and the processing conditions. The microscopic morphology and the surface composition need to be known in order to fully utilize the thin film properties. The technique of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) is used to obtain the molecular surface composition of thin films of blends and block copolymers. The depth profiling tool of Nano-SIMS, a dynamic SIMS technique, helps to provide the chemical mapping of the surface in 2D and 3D. The surface morphology is investigated using AFM. Thin films of PS and PMMA diblock copolymers with molecular weight of 12K-12K and 10K-10K and blends of PS/PMMA (10K/10K) for thicknesses ranging from 5 nm to 50 nm are examined. For the blends, the ToF-SIMS spectra for all the thicknesses show the same behaviour of a high increase of PMMA on the surface after annealing. Nano-SIMS images reveal the formation of nanostructures on the annealed surfaces and AFM studies show these nanostructures to be droplets having distinct phase shift from the surrounding matrix. The droplet dimensions increase with the increase of the thickness of the film but the absolute intensity from the ToF-SIMS spectra for all the annealed films remains almost the same. For the copolymers, the ToF-SIMS spectra show that there is a decrease of PMMA on the surface for the annealed films when compared to the as-cast ones. AFM morphology reveals that, for different thicknesses, annealing induces different topographical features like droplets, holes, spinodal patterns, etc. but with no distinct phase shift between the patterns and the surrounding matrix. The two different copolymers of comparable molecular weight are found to exhibit very different topography even when the thickness of the films remained the same. The surface composition from the ToF-SIMS data, however, was not found to vary even when the topography was completely different.     

Possibilities and limitations of high-resolution mass spectrometry in life sciences

We have applied time-of-flight secondary ion mass spectrometry (ToF-SIMS) and laser post-ionization secondary neutral mass spectrometry (laser-SNMS) to examine the immobilization process of PNA and its hybridization capability to unlabeled complementary DNA fragments, to characterize immobilized proteins, and to image intrinsic elements and molecules with subcellular spatial resolution in different types of frozen non-dehydrated biological samples.The possibilities and limitations of ToF-SIMS and laser-SNMS for imaging elements and molecules in biological samples are discussed. Furthermore possibilities for enhancing the detection sensitivity by using polyatomic and cluster primary ions and different laser post-ionization schemes, as well as ways of obtaining 3D molecular images from biological samples are described.The data shows that both ToF-SIMS and laser-SNMS are capable of imaging elements and molecules in complex biological samples and that they are very valuable tools in advancing applications in life sciences. It was found that cluster-ion bombardment is very useful for enhancing the molecular yield, while laser-SNMS resulted in much higher detection sensitivity for elements and specific molecules and is particularly well suited for imaging ultra-trace element concentrations in biological samples.     

An Application for Aesthetic Quality Assessment in Photography with Interpretability Features

Automatic aesthetic quality assessment is a computer vision problem in which we quantify the attractiveness or the appealingness of a photograph. This is especially useful in social networks, where the amount of images generated each day requires automation for processing. This work presents Aesthetic Selector, an application able to identify images of high aesthetic quality, showing also relevant information about the decisions and providing the use of the most appropriate filters to enhance a given image. We then analyzed the main proposals in the aesthetic quality field, describing their strengths and weaknesses in order to determine the filters to be included in the application Aesthetic Selector. This proposed application was tested, giving good results, in three different scenarios: image selection, image finding, and filter selection. Besides, we carried out a study of distinct visualization tools to better understand the models’ behavior. These techniques also allow detecting which areas are more relevant within the images when models perform classification. The application also includes this interpretability module. Aesthetic Selector is an innovative and original program, because in the field of aesthetic quality in photography, there are no applications that identify high-quality images and also because it offers the capability of showing information about which parts of the image have affected this decision.