Quantification and methodology issues in multivariate analysis of ToF‐SIMS data for mixed organic systems

Multivariate methods, such as principal component analysis (PCA) and multivariate curve resolution (MCR), are often employed to aid the analysis of large complex data sets such as time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) images. There is, however, much confusion over the most appropriate choice of method for any given application and the effects of data preprocessing, which is exacerbated by the confusing terminologies and the use of jargon in this field. In the present study, a simple model system consisting of a ToF‐SIMS image of an immiscible polymer blend is used to evaluate PCA and MCR in the accurate identification, localisation and quantification of the phase‐separated polymer domains, using four data preprocessing methods (no scaling, normalisation, variance scaling and Poisson scaling). This highlights significant issues and challenges in the quantitative multivariate analysis of mixed organic systems, including the discrimination of chemically significant features from experimental noise, the resolution of weak chemical contributions and potential bias introduced by data preprocessing. Multivariate analysis using Poisson scaling, identified as the most suitable data preprocessing method for both PCA and MCR, demonstrates a marked improvement upon traditional (manual) analysis and provides valuable additional information that is difficult to detect using traditional analysis. Using these results, we present recommendations for the optimum use of multivariate analysis by analysts and provide guidance on selecting the most appropriate methods. Confusing terminology is also clarified. © Crown copyright 2008. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd.     

Extraction of hidden information of ToF‐SIMS data using different multivariate analyses

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) is a powerful tool for determining surface information of complex systems such as polymers and biological materials. However, the interpretation of ToF‐SIMS raw data is often difficult. Multivariate analysis has become effective methods for the interpretation of ToF‐SIMS data. Some of multivariate analysis methods such as principal component analysis and multivariate curve resolution are useful for simplifying ToF‐SIMS data consisting of many components to that explained by a smaller number of components. In this study, the ToF‐SIMS data of four layers of three polymers was analyzed using these analysis methods. The information acquired by using each method was compared in terms of the spatial distribution of the polymers and identification. Moreover, in order to investigate the influence of surface contamination, the ToF‐SIMS data before and after Ar cluster ion beam sputtering was compared. As a result, materials in the sample of multiple components, including unknown contaminants, were distinguished. Copyright © 2014 John Wiley & Sons, Ltd.     

Multivariate statistical analysis of non‐mass‐selected ToF‐SIMS data

Cluster LMIGs are now regarded as the standard primary ion guns on time‐of‐flight secondary ion mass spectrometers (ToF‐SIMS). The ToF‐SIMS analyst typically selects a bombarding species (cluster size and charge) to be used for material analysis. Using standard data collection protocols where the analyst uses only a single primary bombarding species, only a fraction of the ion‐beam current generated by the LMIG is used. In this work, we demonstrate for the first time that it is possible to perform ToF‐SIMS analysis when all of the primary ion intensity (clusters) are used; we refer to this new data analysis mode as non‐mass‐selected (NMS) analysis. Since each of the bombarding species has a different mass‐to‐charge ratio, they strike the sample at different times, and as a result, each of the bombarding species generates a spectrum. The resulting NMS ToF‐SIMS spectrum contains contributions from each of the bombarding species that are shifted in time. NMS spectra are incredibly complicated and would be difficult, if not impossible, to analyze using univariate methodology. We will demonstrate that automated multivariate statistical analysis (MVSA) tools are capable of rapidly converting the complicated NMS data sets into a handful of chemical components (represented by both spectra and images) that are easier to interpret since each component spectrum represents a unique and simpler chemistry. Copyright © 2008 John Wiley & Sons, Ltd.     

Multivariate analysis of ToF‐SIMS data using mass segmented peak lists

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) provides detailed molecular insight into the surface chemistry of a diverse range of material types. Extracting useful and specific information from the mass spectra and reducing the dimensionality of very large datasets are a challenge that has not been fully resolved. Multivariate analysis has been widely deployed to assist in the interpretation of ToF‐SIMS data. Principal component analysis is a popular approach that requires the generation of peak lists for every spectrum. Peak list sizes and the resulting data matrices are growing, complicating manual peak selection and analysis. Here we report the generation of very large ToF‐SIMS peak lists using up‐binning, the mass segmentation of spectral data in the range 0 to 300 m/z in 0.01 m/z intervals. Time‐of‐flight secondary ion mass spectrometry data acquired from a set of 4 standard polymers (polyethylene terephthalate, polytetrafluoroethylene, poly(methyl methacrylate), and low‐density polyethylene) are used to demonstrate the efficacy of this approach. The polymer types are discriminated to a moderate extent by principal component analysis but are easily skewed with saturated species or contaminants present in ToF‐SIMS data. Artificial neural networks, in the form of self‐organising maps, are introduced and provide a non‐linear approach to classifying data and focussing on similarities between samples. The classification outcome achieved is excellent for different polymer types and for spectra from a single polymer type generated by using different primary ions. This method offers great promise for the investigation of more complex systems including polymer classes and blends and mixtures of biological materials.     

Multivariate analysis of extremely large ToFSIMS imaging datasets by a rapid PCA method

Principal component analysis (PCA) and other multivariate analysis methods have been used increasingly to analyse and understand depth profiles in X‐ray photoelectron spectroscopy (XPS), Auger electron spectroscopy (AES) and secondary ion mass spectrometry (SIMS). These methods have proved equally useful in fundamental studies as in applied work where speed of interpretation is very valuable. Until now these methods have been difficult to apply to very large datasets such as spectra associated with 2D images or 3D depth‐profiles. Existing algorithms for computing PCA matrices have been either too slow or demanded more memory than is available on desktop PCs. This often forces analysts to ‘bin’ spectra on much more coarse a grid than they would like, perhaps even to unity mass bins even though much higher resolution is available, or select only part of an image for PCA analysis, even though PCA of the full data would be preferred. We apply the new ‘random vectors’ method of singular value decomposition proposed by Halko and co‐authors to time‐of‐flight (ToF)SIMS data for the first time. This increases the speed of calculation by a factor of several hundred, making PCA of these datasets practical on desktop PCs for the first time. For large images or 3D depth profiles we have implemented a version of this algorithm which minimises memory needs, so that even datasets too large to store in memory can be processed into PCA results on an ordinary PC with a few gigabytes of memory in a few hours. We present results from ToFSIMS imaging of a citrate crystal and a basalt rock sample, the largest of which is 134GB in file size corresponding to 67 111 mass values at each of 512 × 512 pixels. This was processed into 100 PCA components in six hours on a conventional Windows desktop PC. © 2015 The Authors. Surface and Interface Analysis published by John Wiley & Sons Ltd.     

Denoising and multivariate analysis of time‐of‐flight SIMS images

Time‐of‐flight SIMS (ToF‐SIMS) imaging offers a modality for simultaneously visualizing the spatial distribution of different surface species. However, the utility of ToF‐SIMS datasets may be limited by their large size, degraded mass resolution and low ion counts per pixel. Through denoising and multivariate image analysis, regions of similar chemistries may be differentiated more readily in ToF‐SIMS image data. Three established denoising algorithms—down‐binning, boxcar and wavelet filtering—were applied to ToF‐SIMS images of different surface geometries and chemistries. The effect of these filters on the performance of principal component analysis (PCA) was evaluated in terms of the capture of important chemical image features in the principal component score images, the quality of the principal component score images and the ability of the principal components to explain the chemistries responsible for the image contrast. All filtering methods were found to improve the performance of PCA for all image datasets studied by improving capture of image features and producing principal component score images of higher quality than the unfiltered ion images. The loadings for filtered and unfiltered PCA models described the regions of chemical contrast by identifying peaks defining the regions of different surface chemistry. Down‐binning the images to increase pixel size and signal was the most effective technique to improve PCA performance. Copyright © 2003 John Wiley & Sons, Ltd.     

The accuracy and precision of the advanced Poisson dead‐time correction and its importance for multivariate analysis of high mass resolution ToF‐SIMS data

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) data collected in single ion counting mode suffers from dead‐time effects that lead to potentially confusing artefacts when common multivariate analysis (MVA) methods are applied to the data. These artefacts can be eliminated by applying an advanced Poisson dead‐time correction that accounts for the signal intensity in the dead‐time window preceding each time channel. Because this correction is nonlinear, it changes the noise distribution in the data. In this work, the accuracy of this dead‐time correction and the noise characteristics of the corrected data have been analysed for spectra with small numbers of primary ion pulses. A simple but accurate equation for estimating the standard deviation in the advanced dead‐time corrected data has been developed. Based on these results, a scaling procedure to enable successful MVA of advanced dead‐time corrected ToF‐SIMS data has been developed. The improvements made possible by using the advanced dead‐time correction and our recommended scaling are presented for principal components analysis of a ToF‐SIMS image of aerosol particles on polytetrafluoroethylene. Recommendations are made for using the advanced dead time correction and scaling ToF‐SIMS data in order optimize the outcomes of MVA. Copyright © 2014 John Wiley & Sons, Ltd.     

Simple statistically based alternatives to MAF for ToF‐SIMS spectral image analysis

The maximum autocorrelation factors technique (MAF) is becoming increasingly popular for the multivariate analysis of spectral images acquired with time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) instruments. In this article, we review the conditions under which the underlying chemical information can be separated from the large‐scale, non‐uniform noise characteristic of ToF‐SIMS data. Central to this pursuit is the ability to assess the covariance structure of the noise. Given a set of replicate images, the noise covariance matrix can be estimated in a straightforward way using standard statistical tools. Acquiring replicate images, however, is not always possible, and MAF solves a subtly different problem, namely, how to approximate the noise covariance matrix from a single image when replicates are not available. This distinction is important; the MAF approximation is not an unbiased statistical estimate of the noise covariance matrix, and it differs in a highly significant way from a true estimate for ToF‐SIMS data. Here, we draw attention to the fact that replicate measurements are made during the normal course of acquiring a ToF‐SIMS spectral image, rendering the MAF procedure unnecessary. Furthermore, in the common case that detector dead‐time effects permit no more than one ion of any specific species to be detected on a single primary ion shot, the noise covariance matrix can be estimated in a particularly simple way, which will be reported. Copyright © 2011 John Wiley & Sons, Ltd.     

Quantitative SIMS analysis of SiC

We performed a systematic study of ion‐implanted 6H‐SiC standards to find the optimal regimes for SIMS analysis. Relative sensitivity factors (RSFs) were acquired for operating conditions typical of practical SIMS applications. The experimental SiC RSFs were compared with those found for silicon: 1 the matrix effect was insignificant in most cases. It was found that the SiO^? cluster ion cannot represent correctly the real oxygen distribution in SiC. The physics of the effect is discussed. Copyright © 2003 John Wiley & Sons, Ltd.     

Identification of a lipid‐related peak set to enhance the interpretation of TOF‐SIMS data from model and cellular membranes

Principal component analysis (PCA) of time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) data enables differentiating structurally similar molecules according to linear combinations of multiple peaks in their spectra. However, in order to use PCA to correctly identify variations in lipid composition between samples, the discrimination achieved must be based on chemical differences that are related to the lipid species, and not sample‐associated contamination. Here, we identify the positive‐ion TOF‐SIMS peaks that are related to phosphatidylcholine lipid headgroups and tail groups by PCA of spectra acquired from lipid isotopologs. We demonstrate that restricting PCA to a contaminant‐free lipid‐related peak set reduces the variability in the spectra acquired from lipid samples that is due to contaminants, which enhanced differentiating different lipid standards, but adversely affected the contrast in PC scores images of phase‐separated lipid membranes. We also show that PCA of a restricted data set consisting of the peaks related to lipids and amino acids increases the likelihood that the discrimination of TOF‐SIMS data acquired from intact cells is based on differences in the lipids and proteins on the cell surface, and not sample‐specific contamination without compromising sample discrimination. We expect that the lipid‐related peak database established herein will facilitate interpreting the TOF‐SIMS data and PCA results from studies of both model and cellular membranes, and enhance identifying the origins of the peaks that contribute to discriminating different types of cells. Copyright © 2011 John Wiley & Sons, Ltd.     

Surface studies of halloysite nanotubes by XPS and ToF‐SIMS

This report provides detailed experimental results of thermal and surface characterization on untreated and surface‐treated halloysite nanotubes (HNTs) obtained from two geographic areas. Surface characterization techniques, including XPS and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) were used. ToF‐SIMS surface analysis experiments were performed with both atomic and cluster ion beams. Higher ion yields and more high‐mass ions were obtained with the cluster ion beams. Static ToF‐SIMS spectra were analyzed with principal component analysis (PCA). Morphological diversities were observed in the samples although they mainly contained tubular structures. Thermogravimetric data indicated that aqueous hydrogen peroxide solution could remove inorganic salt impurities, such as alkali metal salts. The amount of grafting of benzalkonium chloride of HNT surface was determined by thermogravimetic analysis. PCA of ToF‐SIMS spectra could distinguish the samples mined from different geographical locations as well as among surface‐treated and untreated samples. Copyright © 2010 John Wiley & Sons, Ltd.     

Accounting for Poisson noise in the multivariate analysis of ToF‐SIMS spectrum images

Recent years have seen the introduction of many surface characterization instruments and other spectral imaging systems that are capable of generating data in truly prodigious quantities. The challenge faced by the analyst, then, is to extract the essential chemical information from this overwhelming volume of spectral data. Multivariate statistical techniques such as principal component analysis (PCA) and other forms of factor analysis promise to be among the most important and powerful tools for accomplishing this task. In order to benefit fully from multivariate methods, the nature of the noise specific to each measurement technique must be taken into account. For spectroscopic techniques that rely upon counting particles (photons, electrons, etc.), the observed noise is typically dominated by ‘counting statistics’ and is Poisson in nature. This implies that the absolute uncertainty in any given data point is not constant, rather, it increases with the number of counts represented by that point. Performing PCA, for instance, directly on the raw data leads to less than satisfactory results in such cases. This paper will present a simple method for weighting the data to account for Poisson noise. Using a simple time‐of‐flight secondary ion mass spectrometry spectrum image as an example, it will be demonstrated that PCA, when applied to the weighted data, leads to results that are more interpretable, provide greater noise rejection and are more robust than standard PCA. The weighting presented here is also shown to be an optimal approach to scaling data as a pretreatment prior to multivariate statistical analysis. Published in 2004 by John Wiley & Sons, Ltd.     

Reduction of matrix effects in TOF‐SIMS analysis by metal‐assisted SIMS (MetA‐SIMS)

We investigated reduction of the matrix effect in time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS) analysis by the deposition of a small amount of metal on the sample surfaces (metal‐assisted SIMS or MetA‐SIMS). The metal used was silver, and the substrates used were silicon wafers as electroconductive substrates and polypropylene (PP) plates as nonelectroconductive substrates. Irganox 1010 and silicone oil on these substrates were analyzed by TOF‐SIMS before and after silver deposition. Before silver deposition, the secondary ion yields from the substances on the silicon wafer and PP plate were quite different due to the matrix effect from each substrate. After silver deposition, however, both ion yields were enhanced, particularly the sample on the PP plate, and little difference was seen between the two substrates. It was therefore found that the deposition of a small amount of metal on the sample surface is useful for reduction of the matrix effect. By reducing the matrix effect using this technique, it is possible to evaluate from the ion intensities the order of magnitude of the quantities of organic materials on different substrates. In addition, this reduction technique has clear utility for the imaging of organic materials on nonuniform substrates such as metals and polymers. MetA‐SIMS is thus a useful analysis tool for solving problems with real‐world samples. Copyright © 2005 John Wiley & Sons, Ltd.     

Image fusion combining SEM and ToF‐SIMS images

Image fusion allows for the combination of an image containing chemical information but low spatial resolution with a high‐spatial resolution image having little to no chemical information. The resulting hybrid image retains all the information from the chemically relevant original image, with improved spatial resolution allowing for visual inspection of the spatial correlations. In this research, images were obtained from two sample test grids: one of a copper electron microscope grid with a letter ‘A’ in the center (referred to below as the ‘A‐grid’), and the other a Tantalum and Silicon test grid from Cameca that had an inscribed letter ‘C’ (referred to below as the ‘Cameca grid’). These were obtained using scanning electron microscopy (SEM) and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). Image fusion was implemented with the Munechika algorithm. The edge resolution of the resulting hybrid image was calculated compared with the edge resolution obtained for both the individual ToF‐SIMS and SEM images. The challenges of combining complimentary datasets from different instrumental analytical methods are discussed as well as the advantages of having a hybrid image. The distance across the edge for hybrid images of the A‐Grid and the Cameca grid were determined to be 21 µm and 8 µm, respectively. When these values were compared to the original ToF‐SIMS, SEM and optical microscopy measurements, the fused image had a spatial resolution nearly equal to that obtained in the SEM image for both samples. Copyright © 2014 John Wiley & Sons, Ltd.     

Mitigating dead‐time effects during multivariate analysis of ToF‐SIMS spectral images

ToF‐SIMS spectra are formed by bombarding a surface with a pulse of primary ions and detecting the resultant ionized surface species using a time‐of‐flight mass spectrometer. Typically, the detector is a time‐to‐digital converter. Once an ion is detected using such detectors, the detector becomes insensitive to the arrival of additional ions for a period termed as the (detector) dead‐time. Under commonly used ToF‐SIMS data acquisition conditions, the time interval over which ions arising from a single chemical species reach the detector is on the order of the detector dead‐time. Thus, only the first ion reaching the detector at any given mass is counted. The event registered by the data acquisition system, then, is the arrival of one or more ions at the detector. This behavior causes ToF‐SIMS data to violate, in the general case, the assumption of linear additivity that underlies many multivariate statistical analysis techniques. In this article, we show that high‐mass‐resolution ToF‐SIMS spectral‐image data follow a generalized linear model, and we propose a data transformation and scaling procedure that enables such data sets to be successfully analyzed using standard methods of multivariate image analysis. Copyright © 2008 John Wiley & Sons, Ltd.     

Hidden information in principal component analysis of ToF‐SIMS data: On the use of correlation loadings for the identification of significant signals and structure elucidation

In this paper, an improved approach to interpret results of principal component analysis (PCA) of time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) spectra is presented. Signals are typically observed in different intensity ranges in a single ToF‐SIMS spectrum due to different sensitivity factors and surface concentrations. This can complicate the PCA interpretation, because loadings are reported to be strongly affected by these intensity changes. In contrast, it is shown here that correlation loadings are unaffected by these differences. In particular, correlation loadings were successfully used to identify signals with relatively low intensity but high significance. These signals may be overlooked when only loadings are used. This is particularly true in failure analysis, where ToF‐SIMS is used to screen for initially unknown signals that may be relevant for the characteristics/failure of a product. As a model study, the concept was applied to investigate ageing of Li‐ion batteries by ToF‐SIMS. In this data set, the significance of impurities that affect the quality of Li‐ion batteries was identified only by correlation loadings, whereas the loadings were found to overestimate the influence of other matrix signals. In addition, correlation loadings aid in the chemical identification and helped to successfully assign unknown peaks.     

Characterization of oxygen precipitates in Czochralski silicon by imaging SIMS

This paper presents a three-dimensional characterization of oxygen defects in Czochralski-silicon (CZ) by 3D-SIMS using a camera based imaging system. Different manufacturing processes yielding a variation in size and distribution of oxygen precipitates are monitored by imaging SIMS and conventional depth profiling. Emphasis is laid on the characterization of the precipitation behavior governed by different annealing processes. The limitations of 3D SIMS for oxygen defect imaging are studied. Sputter induced microroughening of the crater bottom, investigated by SEM, is shown to be strongly correlated with defect densities caused by oxygen precipitates.     

Classification of secondary ion mass spectrometry (SIMS) micrographs to characterize chemical phases

This work demonstrates the potential of multivariate image analysis methods in the extraction of useful, problem dependent information from SIMS images. Specific algorithms have been developed to classify SIMS micrographs manually as well as automatically. A feature selection has been achieved by means of principal component analysis with a subsequent image classification.As an application example for these improved digital image processing tools chemical phases within a soldered industrial metal sample have been identified. This is of highly practical value as it was assumed that during the soldering process inhomogeneities occur along the joint site which cause a cracking of the brazed material under mechanical strain conditions.     

Adsorption of genetically engineered proteins studied by time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS). Part A: data acquisition and principal component analysis (PCA)

Recent progress in the adaptation of combinatorial biology selection protocols to materials science has created a new class of polypeptides with specific affinity to inorganics. Here, we use one of the genetically engineered proteins, a gold binding protein (GBP‐1), to assess quantitatively its binding specificity to Au, Ag and Pd surfaces by using time‐of‐flight secondary ion mass spectroscopy (TOF‐SIMS). The GBP‐1, originally selected using cell‐surface display techniques, consisting of 14 amino acids with a sequence of MHGKTQATSGTIQS, was used in this study. Three‐repeat and single‐repeat forms of GBP‐1 were prepared. In earlier studies, GBP‐1 was shown to bind to Au particles and self‐assemble on flat Au surfaces. Through the fingerprint analysis of these specific peptides, their role in binding can be investigated in terms of their contribution to surface interaction possibly forming the right molecular architecture for binding. To achieve this purpose, a large‐sized data matrix produced by TOF‐SIMS must be properly treated for analysis. In Part A, we use principal component analysis (PCA) to visualize the spectral variations for a variety of adsorption conditions and suggest possible contribution of the specific types of amino acids (binding site) to the interactions. Copyright © 2007 John Wiley & Sons, Ltd.     

ToF‐SIMS for the characterization of hyperbranched aliphatic polyesters: probing their molecular weight on surfaces based on principal component analysis (PCA)

A series of 2,2‐bis(hydroxymethyl)propionic acid (Bis‐MPA) hyperbranched aliphatic polyesters with different molecular weights (generations) is analysed for the first time by time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). The main negative and positive low‐mass fragments are identified in the fingerprint part of the spectra (m/z < 400) and are principally assigned to fragmentation of the Bis‐MPA repeating units. In addition, it is shown that the fragmentation pattern is highly affected by the functional end‐groups. This is illustrated for a phthalic acid end‐capped hyperbranched polymer and for an acetonide‐terminated dendrimer analog. Also, typical fragments assigned to the ethoxylated pentaerythritol core molecule are detected. These ions show decreasing intensities with increasing molecular weight. This intensity dependency on the generation is used to calibrate the molecular weight of hyperbranched polyesters on the surface. To obtain quantitative information, a principal component analysis (PCA) multivariate statistical method is applied to the ToF‐SIMS data. The influence of different normalization procedures prior to PCA calculation is tested, e.g. normalization to the total intensity, to the intensities of ions assigned to the Bis‐MPA repeating unit or to intensities of fragments due to the core molecule. It is shown that only one principal component (PC1) is needed to describe most of the variance between the samples. In addition, PC1 takes into account the generation effect. However, different relationships between the PC1 scores and the hyperbranched mass average molecular weights are observed depending on the normalization procedure used. Normalization of data set ion intensities by ion intensities from the core molecule allows linearization of the SIMS intensities versus the molecular weight and allows the hyperbranched polymers to be discriminated up to the highest generations. In addition, PCA applied to ToF‐SIMS data provides an extended interpretation of the spectra leading to further identification of the correlated mass peaks, such as those of the Bis‐MPA repeating unit (terminal, dendritic and linear) and those of the core molecule. Finally, the work presented demonstrates the extreme potential of the static ToF‐SIMS and PCA techniques in the analysis of dendritic molecules on solid surfaces. Copyright © 2003 John Wiley & Sons, Ltd.