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.     

ToF-SIMS Analysis of Adsorbed Proteins: Principal Component Analysis of the Primary Ion Species Effect on the Protein Fragmentation Patterns

In time-of-flight secondary ion mass spectrometry (ToF-SIMS), the choice of primary ion used for analysis can influence the resulting mass spectrum. This is because different primary ion types can produce different fragmentation pathways. In this study, analysis of single-component protein monolayers were performed using monatomic, tri-atomic, and polyatomic primary ion sources. Eight primary ions (Cs(+), Au(+), Au(3) (+), Bi(+), Bi(3) (+), Bi(3) (++), C(60) (+)) were used to examine to the low mass (m/z < 200) fragmentation patterns from five different proteins (bovine serum albumin, bovine serum fibrinogen, bovine immunoglobulin G and chicken egg white lysozyme) adsorbed onto mica surfaces. Principal component analysis (PCA) processing of the ToF-SIMS data showed that variation in peak intensity caused by the primary ions was greater than differences in protein composition. The spectra generated by Cs(+), Au(+) and Bi(+) primary ions were similar, but the spectra generated by monatomic, tri-atomic and polyatomic primary ion ions varied significantly. C(60) primary ions increased fragmentation of the adsorbed proteins in the m/z < 200 region, resulting in more intense low m/z peaks. Thus, comparison of data obtained by one primary ion species with that obtained by another primary ion species should be done with caution. However, for the spectra generated using a given primary ion beam, discrimination between the spectra of different proteins followed similar trends. Therefore, a PCA model of proteins created with a given ion source should only be applied to datasets obtained using the same ion source. The type of information obtained from PCA depended on the peak set used. When only amino acid peaks were used, PCA was able to identify the relationship between proteins by their amino acid composition. When all peaks from m/z 12-200 were used, PCA separated proteins based on a ratio of C(4)H(8)N(+) to K(+) peak intensities. This ratio correlated with the thickness of the protein films and Bi(1) (+) primary ions produced the most surface sensitive spectra.     

ANOVA–principal component analysis and ANOVA–simultaneous component analysis: a comparison

ANOVA–simultaneous component analysis (ASCA) is a recently developed tool to analyze multivariate data. In this paper, we enhance the explorative capability of ASCA by introducing a projection of the observations on the principal component subspace to visualize the variation among the measurements. We compare the significance of experimental effects for ASCA and ANOVA–principal component analysis (PCA), a similar tool to explore multivariate data, by using permutation tests. Furthermore, we quantify the quality of the loadings estimate obtained with ASCA and compare this with the loadings estimate obtained with ANOVA–PCA. Copyright © 2011 John Wiley & Sons, Ltd.     

Classification of time-of-flight secondary ion mass spectrometry spectra from complex Cu–Fe sulphides by principal component analysis and artificial neural networks

Artificial neural network (ANN) and a hybrid principal component analysis-artificial neural network (PCA-ANN) classifiers have been successfully implemented for classification of static time-of-flight secondary ion mass spectrometry (ToF-SIMS) mass spectra collected from complex Cu–Fe sulphides (chalcopyrite, bornite, chalcocite and pyrite) at different flotation conditions. ANNs are very good pattern classifiers because of: their ability to learn and generalise patterns that are not linearly separable; their fault and noise tolerance capability; and high parallelism. In the first approach, fragments from the whole ToF-SIMS spectrum were used as input to the ANN, the model yielded high overall correct classification rates of 100% for feed samples, 88% for conditioned feed samples and 91% for Eh modified samples. In the second approach, the hybrid pattern classifier PCA-ANN was integrated. PCA is a very effective multivariate data analysis tool applied to enhance species features and reduce data dimensionality. Principal component (PC) scores which accounted for 95% of the raw spectral data variance, were used as input to the ANN, the model yielded high overall correct classification rates of 88% for conditioned feed samples and 95% for Eh modified samples.     

Probing the orientation of surface-immobilized immunoglobulin G by time-of-flight secondary ion mass spectrometry

Static time-of-flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface analysis technique for the characterization of protein films because of its chemical selectivity and surface sensitivity. In this study, static ToF-SIMS and principal component analysis (PCA), a multivariate data analysis method, were combined to probe the orientation of surface-immobilized immunoglobulin G (IgG). IgG orientation can enhance its ability to detect its antigen in immunoassay techniques. The IgG used in this work is the mouse monoclonal anti-human chorionic gonadotropin (anti-hCG). Anti-hCG films on different well-defined substrates have been studied using its F(ab')2 and Fc fragments as references. Atomic force microscopy was used to characterize these protein films before static ToF-SIMS analysis. The results from PCA of ToF-SIMS spectra were related to the antibody primary amino acid composition and its three-dimensional structure.     

Investigations of matrix-assisted laser desorption/ionization sample preparation by time-of-flight secondary ion mass spectrometry

Matrix-assisted laser desorption/ionization (MALDI) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) analyses are compared to gain insight into some of the details of sample preparation for MALDI analysis of synthetic polymers. ToF-SIMS imaging of MALDI samples shows segregation of the cationization agent from the matrix crystals. The amount of observed segregation can be controlled by the sample preparation technique. Electrospray sample deposition minimizes segregation. Comparing ToF-SIMS and MALDI mass spectra from the same samples confirms that ToF-SIMS is significantly more surface sensitive than MALDI. This comparison shows that segregation of the oligomers of a polymer sample can occur during MALDI sample preparation. Our data indicate that MALDI is not as sensitive to those species dominating the sample surface as to species better incorporated into the matrix crystals. Finally, we show that matrix-enhanced SIMS can be an effective tool to analyze synthetic polymers, although the sample preparation conditions may be different than those optimized for MALDI.     

Quantifiable correlation of ToF-SIMS and XPS data from polymer surfaces with controlled amino acid and peptide content

Peptide-coated surfaces are widely employed in biomaterial design, but quantifiable correlation between surface composition and biological response is challenging due to, for example, instrumental limitations, a lack of suitable model surfaces or limitations in quantitatively correlating data from different surface analytical techniques. Here, we first establish a reference material that allows control over amino acid content. Reversible addition-fragmentation chain-transfer (RAFT) polymerisation is used to prepare a copolymer containing alkyne and furan units with well-defined chain length and composition. Huisgen Cu(I)-catalysed azide-alkyne cycloaddition reaction is used to attach the model azido-polyethyleneglycol-amide-modified pentafluoro-l -phenylalanine to the polymer. Different compositional ratios of the polymer provide a surface with varying amino acid content that is analysed by X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). Nitrogen-related signals are compared with fluorine signals from both techniques. Fluorine and nitrogen signals from both techniques are found to be related to the copolymer compositions, but the homopolymer data deviate from this trend. The approach is then translated to a heparin-binding peptide that supports cell adhesion. Human embryonic stem cells cultured on copolymer surfaces presenting different amounts of heparin-binding peptide show strong cell growth while maintaining pluripotency after 72 h of culture. The early cell adhesion at 24 h can be correlated to the logarithm of the normalised CH₄N⁺ ion intensity from ToF-SIMS data, which is established as a suitable and generalisable marker ion for amino acids and peptides. This work contributes to the ability to use ToF-SIMS in a more quantitative manner for the analysis of amino acid and peptide surfaces.     

Imaging and differentiation of mouse embryo tissues by ToF-SIMS

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) equipped with a gold ion gun was used to image mouse embryo sections and differentiate tissue types (brain, spinal cord, skull, rib, heart and liver). Embryos were paraffin-embedded and then deparaffinized. The robustness and repeatability of the method was determined by analyzing ten tissue slices from three different embryos over a period of several weeks. Using principal component analysis (PCA) to reduce the spectral data generated by ToF-SIMS, histopathologically identified tissue types of the mouse embryos can be differentiated based on the characteristic differences in their mass spectra. These results demonstrate the ability of ToF-SIMS to determine subtle chemical differences even in fixed histological specimens.     

Automated correlation and classification of secondary ion mass spectrometry images using a k-means cluster method

We present a novel method for correlating and classifying ion-specific time-of-flight secondary ion mass spectrometry (ToF-SIMS) images within a multispectral dataset by grouping images with similar pixel intensity distributions. Binary centroid images are created by employing a k-means-based custom algorithm. Centroid images are compared to grayscale SIMS images using a newly developed correlation method that assigns the SIMS images to classes that have similar spatial (rather than spectral) patterns. Image features of both large and small spatial extent are identified without the need for image pre-processing, such as normalization or fixed-range mass-binning. A subsequent classification step tracks the class assignment of SIMS images over multiple iterations of increasing n classes per iteration, providing information about groups of images that have similar chemistry. Details are discussed while presenting data acquired with ToF-SIMS on a model sample of laser-printed inks. This approach can lead to the identification of distinct ion-specific chemistries for mass spectral imaging by ToF-SIMS, as well as matrix-assisted laser desorption ionization (MALDI), and desorption electrospray ionization (DESI).     

ToF-SIMS analysis of poly(l-lysine)-graft-poly(2-methyl-2-oxazoline) ultrathin adlayers

Understanding of the interfacial chemistry of ultrathin polymeric adlayers is fundamentally important in the context of establishing quantitative design rules for the fabrication of nonfouling surfaces in various applications such as biomaterials and medical devices. In this study, seven poly(l-lysine)-graft-poly(2-methyl-2-oxazoline) (PLL–PMOXA) copolymers with grafting density (number of PMOXA chains per lysine residue) 0.09, 0.14, 0.19, 0.33, 0.43, 0.56, and 0.77, respectively, were synthesized and characterized by means of nuclear magnetic resonance spectroscopy (NMR). The copolymers were then adsorbed on Nb₂O₅ surfaces. Optical waveguide lightmode spectroscopy method was used to monitor the surface adsorption in situ of these copolymers and provide information on adlayer masses that were then converted into PLL and PMOXA surface densities. To investigate the relationship between copolymer bulk architecture (as shown by NMR data) and surface coverage as well as surface architecture, time-of-flight secondary ion mass spectrometry (ToF-SIMS) analysis was performed. Furthermore, ToF-SIMS method combined with principal component analysis (PCA) was used to verify the protein resistant properties of PLL–PMOXA adlayers, by thorough characterization before and after adlayer exposure to human serum. ToF-SIMS analysis revealed that the chemical composition as well as the architecture of the different PLL–PMOXA adlayers indeed reflects the copolymer bulk composition. ToF-SIMS results also indicated a heterogeneous surface coverage of PLL–PMOXA adlayers with high grafting densities higher than 0.33. In the case of protein resistant surface, PCA results showed clear differences between protein resistant and nonprotein-resistant surfaces. Therefore, ToF-SIMS results combined with PCA confirmed that the PLL–PMOXA adlayer with brush architecture resists protein adsorption. However, low increases of some amino acid signals in ToF-SIMS spectra were detected after the adlayer has been exposed to human serum.

Fourier-transform infrared (FTIR) spectroscopy for monitoring and determining the degree of crystallisation of polyhydroxyalkanoates (PHAs)

FTIR spectroscopy has been used to monitor and determine the degree of crystallisation in a sample of polyhydroxybutyrate-co-14%valerate (PHB–co-14%HV). Time series spectra of solution-cast films of the polymer revealed spectral changes attributed to the onset of crystallisation. Curve fitting was used to obtain an absolute measure of crystallinity. Mean centred principal-component analysis (PCA) revealed that 99.9% of the spectral variance could be attributed to factor 1. The loadings plot for factor 1 contained features attributable to crystalline and amorphous phases. These features were opposite in sign, indicating that changes in the spectra with the onset of crystallisation are simultaneous and opposite in direction, i.e. as the crystalline band increases the amorphous band decreases. Cross-peaks in asynchronous 2D correlation maps indicate there are likely to be very minor components that are changing out of phase. The presence of these minor components is supported by examination of the loadings of higher factors in the PCA model. PCA has been shown to be suitable for determining the number of dynamic spectral features and has enabled relative and objective monitoring of crystallisation kinetics.     

ToF‐SIMS analysis of a polymer microarray composed of poly(meth)acrylates with C6 derivative pendant groups

Surface analysis plays a key role in understanding the function of materials, particularly in biological environments. Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) provides highly surface sensitive chemical information that can readily be acquired over large areas and has, thus, become an important surface analysis tool. However, the information‐rich nature of ToF‐SIMS complicates the interpretation and comparison of spectra, particularly in cases where multicomponent samples are being assessed. In this study, a method is presented to assess the chemical variance across 16 poly(meth)acrylates. Materials are selected to contain C₆ pendant groups, and ten replicates of each are printed as a polymer microarray. SIMS spectra are acquired for each material with the most intense and unique ions assessed for each material to identify the predominant and distinctive fragmentation pathways within the materials studied. Differentiating acrylate/methacrylate pairs is readily achieved using secondary ions derived from both the polymer backbone and pendant groups. Principal component analysis (PCA) is performed on the SIMS spectra of the 16 polymers, whereby the resulting principal components are able to distinguish phenyl from benzyl groups, mono‐functional from multi‐functional monomers and acrylates from methacrylates. The principal components are applied to copolymer series to assess the predictive capabilities of the PCA. Beyond being able to predict the copolymer ratio, in some cases, the SIMS analysis is able to provide insight into the molecular sequence of a copolymer. The insight gained in this study will be beneficial for developing structure–function relationships based upon ToF‐SIMS data of polymer libraries. © 2016 The Authors Surface and Interface Analysis Published by John Wiley & Sons Ltd.     

Understanding the molecular information contained in principal component analysis of vibrational spectra of biological systems

K-means clustering followed by Principal Component Analysis (PCA) is employed to analyse Raman spectroscopic maps of single biological cells. K-means clustering successfully identifies regions of cellular cytoplasm, nucleus and nucleoli, but the mean spectra do not differentiate their biochemical composition. The loadings of the principal components identified by PCA shed further light on the spectral basis for differentiation but they are complex and, as the number of spectra per cluster is imbalanced, particularly in the case of the nucleoli, the loadings under-represent the basis for differentiation of some cellular regions. Analysis of pure bio-molecules, both structurally and spectrally distinct, in the case of histone, ceramide and RNA, and similarly in the case of the proteins albumin, collagen and histone, show the relative strong representation of spectrally sharp features in the spectral loadings, and the systematic variation of the loadings as one cluster becomes reduced in number. The more complex cellular environment is simulated by weighted sums of spectra, illustrating that although the loading becomes increasingly complex; their origin in a weighted sum of the constituent molecular components is still evident. Returning to the cellular analysis, the number of spectra per cluster is artificially balanced by increasing the weighting of the spectra of smaller number clusters. While it renders the PCA loading more complex for the three-way analysis, a pair wise analysis illustrates clear differences between the identified subcellular regions, and notably the molecular differences between nuclear and nucleoli regions are elucidated. Overall, the study demonstrates how appropriate consideration of the data available can improve the understanding of the information delivered by PCA.     

Differentiating diterpene resin acids using ToF‐SIMS and principal component analysis: new tools for assessing the geochemistry of amber

Amber is a polymerized plant resin having remarkable preservation potential in the geological record. Numerous analytical techniques have been applied to the study of amber organic chemistry in order to extract paleobotanical information. However, only exploratory work has been conducted using time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS), despite its immense potential due to the high mass resolution and range that can be analyzed concurrently. Detailed assessments of ion fragmentation patterns are prerequisite, given that amber is comprised of a challenging range of terpenoids, carboxylic acids, alcohols, and associated esters. In recent work, we demonstrated the applicability and efficiency of ToF‐SIMS as a tool to investigate amber chemical composition. However, only two diterpene resin acid standards were considered in this preliminary study, namely abietic acid and communic acid. We now extend this work by documenting the ToF‐SIMS spectra of ten additional diterpene resin acids and ask whether ToF‐SIMS analysis can distinguish subtle differences within a larger set of diterpenoids. Both positive and negative ToF‐SIMS spectra were produced, although negative polarity appears particularly promising for differentiating diterpene resin acids. Principal component analysis (PCA) was used to distill the data and verified that purified diterpenes have distinct ToF‐SIMS spectra that can be applied to amber chemotaxonomy as well as to the analysis of modern resins of known botanical origin. While this work is pertinent to the study of the composition and histories of ambers, the mass spectra of the 12 diterpene standards could prove valuable to any system where diterpenoid chemistry plays a role. Copyright © 2014 John Wiley & Sons, Ltd.     

Surface molecular characterisation of different epoxy resin composites subjected to UV accelerated degradation using XPS and ToF-SIMS

Epoxy resin composites reinforced with E-glass (E), 3D glass (3D) and carbon fibre (CF) were subjected to an intense UV and high temperature accelerated degradation environment. X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS) were used to provide a molecular characterisation of the surface of the degraded composites. The response at the surface of the epoxy resin composites to oxidative degradation is influenced by the composite reinforcement type and characteristics. XPS results indicate that 3D resin composites exhibit more surface oxidation as a result of the accelerated degradation in comparison with E and CF composites. Principal components analysis (PCA) of the ToF-SIMS positive ion spectra showed that E and 3D resin composites suffered chain scission while CF composites suffered chain scission and cross-linking reactions as a result of the intense UV exposure. The extent of the surface oxidation, cross-linking/condensation reaction and loss of low molecular weight (lower than C₄H_x) aliphatic hydrocarbons may be indicated using PCA of both the ToF-SIMS positive and negative ion spectra. PCA also provides insight for proposing epoxy resin chain scission and oxidation reaction mechanisms.     

Chemical and structural characterisation of DGEBA-based epoxies by time of flight secondary ion mass spectrometry (ToF-SIMS) as a preliminary to polymer interphase characterisation

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has become a powerful tool in the field of surface analysis since it provides information about the top few monolayers of a sample, i.e. on the chemical composition of the sample surface. Thus, the general question arises whether a surface-sensitive technique like ToF-SIMS would be appropriate to detect systematic chemical and/or structural changes in organic bulk polymers caused by varying a chemical content of the initial components or by tracking, e.g. curing processes in such materials. It is shown that careful sample preparation and the use of multivariate methods permit the quantitative acquisition of chemical and structural information about bulk polymers from the secondary ion signals. The hardener concentration and a cross-linking coefficient in diglycidyl ether of bisphenol A based epoxies were determined by ToF-SIMS measurements on samples with different resin to hardener ratio and varying curing time. In future work, we will use the developed method to investigate the local composition of adhesively bonded joints. In particular, the mapping of the chemical and structural properties in the so-called interphase will then be of interest.     

Comparison of Bi(1), Bi(3) and C(60) primary ion sources for ToF-SIMS imaging of patterned protein samples

Imaging time-of-flight secondary ion mass spectrometry (ToF-SIMS) has been used to study protein bound to a photolithographically-patterned, commercial poly(ethylene glycol) (PEG)-based polymer film. The effect of different ion sources on the fragmentation pattern from this sample was analyzed with respect to the surface sensitivity of characteristic protein fragments and contrast in the ion images. The method demonstrates that, under similar fluence (below the static limit), Bi(3) (+) provides better surface sensitivity for low mass fragments and the best image contrast as compared to Bi(1) (+) and C(60) (+) cluster sources. Principal component analysis (PCA) was utilized to process depth profiles for this sample and shows that a primary ion fluence of approximately 20 × 10(12) ions/cm(2) is required to etch through the adsorbed protein layer.     

Inferring wettability of heterogeneous surfaces by ToF-SIMS

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) has been examined as a possible predictive tool for surface wettability. Heterogeneous surfaces were prepared with hydrophilic and hydrophobic regions of known surface coverage using self-assembled monolayers. The surface coverage of each component was then correlated with ToF-SIMS fragmentation of the hydrophobic and hydrophilic surface groups and static contact angle measurements. From these measurements, a clear relationship between the surface wettability and relative intensity of characteristic secondary ions was identified. Moreover, our results for planar surfaces can be extrapolated to predict the wettability of particulate samples for which direct contact angle measurements are not straightforward. The ability to infer particle wettability by ToF-SIMS is well suited to mineral characterization and in particular, the prediction of mineral flotation efficiencies.     

Characterization of polymeric surfaces and interfaces using time-of-flight secondary ion mass spectrometry

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is used for chemical analysis of surfaces. ToF-SIMS is a powerful tool for polymer science because it detects a broad mass range with good mass resolution, thereby distinguishing between polymers that have similar elemental compositions and/or the same types of functional groups. Chemical labeling techniques that enhance contrast, such as deuterating or staining one constituent, are generally unnecessary. ToF-SIMS can generate both two-dimensional images and three-dimensional depth profiles, where each pixel in an image is associated with a complete mass spectrum. This Review begins by introducing the principles of ToF-SIMS measurements, including instrumentation, modes of operation, strategies for data analysis, and strengths/limitations when characterizing polymer surfaces. The sections that follow describe applications in polymer science that benefit from characterization by ToF-SIMS, including thin films and coatings, polymer blends, composites, and electronic materials. The examples selected for discussion showcase the three standard modes of operation (spectral analysis, imaging, and depth profiling) and highlight practical considerations that relate to experimental design and data processing. We conclude with brief comments about broader opportunities for ToF-SIMS in polymer science.     

Metabolic differences among melanoma and two prostate cancer cell lines by electrospray ion mobility mass spectrometry

Detecting early stages of cancer has been a challenge for analytical methods. Even with the advances in mammography and prostate specific antigen (PSA) testing the number of false positives and negatives is still significant. Cancer cell lines provide realistic simulations to assess detection technologies for different stages and types of cancer. Metabolomics, the measure of end products of all biological processes, is a recent addition to the field of systems biology and can be targeted towards early disease detection. For this study, the metabolomes of three cancer cell lines, two prostate (DU145 and PC3) and one melanoma (A375), were investigated by an ion mobility mass spectrometer (IMMS) for both positive and negative ion detection mode. IMMS produced multidimensional data that includes m/z, mobility and intensity for each ion detected in the sample. In order to see differences between the cancer cell lines, principal components analysis (PCA), a multi-variant technique used to pull patterns out of the data, was used in conjunction with the IMMS data. The sample patterns from the PCA were observed in the score plot and the individual metabolites that contributed the most to the model were shown in the loadings plot. For positive mode 127 metabolite ions were statistically analyzed and in the negative mode 115 metabolite ions were analyzed. PCA score plots were able to model 98% and 87% of the original data for positive and negative mode, respectively. The metabolite ions from the loadings plot were tentatively identified based on m/z.