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.     

The statistics of ToF‐SIMS data revisited and introduction of the empirical Poisson correction

Generation of time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) data involves two overarching processes: secondary ion production and secondary ion detection. The interpretation of ToF‐SIMS data is facilitated if the intensities of the as‐measured mass spectra are proportional to the abundances of the species under investigation. While secondary ion yield is normally taken to be a linear process, ion detection is not owing to detector dead‐time effects. Consequently, methods have been devised that attempt to linearize, or correct, data that are affected by the dead time. In this article, we review the statistics of ToF‐SIMS data generation and confirm a report in the literature that abundance estimates from so‐called Poisson corrections are biased. We show that these corrections are only unbiased asymptotically and that a rigorous probabilistic analysis can quantitatively account for the observed bias. Two sources of bias are identified, one having a statistical basis and one due to the form of the correction equation at high ion detection rates. Based on insights gained from this analysis, we propose a new correction equation, the empirical Poisson correction, which largely eliminates the statistical bias. The performance of the proposed correction is illustrated by reanalyzing 14 experimentally measured datasets that suffer from varying levels of dead‐time effects. Copyright © 2016 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.     

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.     

Matrix‐enhanced secondary ion mass spectrometry: the influence of MALDI matrices on molecular ion yields of thin organic films

In this work the effect in secondary ion mass spectrometry (SIMS) of several frequently used matrix‐assisted laser desorption/ionisation (MALDI) matrices on the secondary ion intensities of low molecular weight (m/z 400–800) organic dyes and a pharmaceutical is tested. Matrix (10^?1 M) and analyte (10^?2 M) solutions were made in methanol. Mixtures with several concentration ratios were prepared from these solutions and spincoated on Si substrates prior to time‐of‐flight (TOF)‐SIMS analysis. In some cases the presence of the MALDI matrices caused a considerable increase in the positive secondary (protonated) molecular ion signals. Enhancements of a factor of 20 and more were recorded. Generally, of the matrices used, 2,5‐dihydroxybenzoic acid and 2,4,6‐trihydroxyacetophenone brought about the highest intensity increases. It was also shown that matrix‐enhanced (ME‐)SIMS is capable of lowering the detection limits for molecule ions. However, the enhancement effect is strongly influenced by the analyte/matrix combination and its concentration ratio. As a result, finding an optimal analyte/matrix mixture can be a very time‐consuming process. Mostly, the presence of the matrices causes changes in the relative ion intensities in the TOF‐S‐SIMS spectra. Compared to the spectra recorded from samples without matrices, only a few additional peaks, such as signals that originate directly from the applied matrix or adduct ions, are observed in the mass spectra. Sometimes molecule ions and some characteristic fragments at high m/z values, that cannot be recorded without matrix, do appear in the spectrum when a matrix is present. In the negative mode no enhancement effect is observed on applying the studied MALDI matrices. The results obtained from samples treated with MALDI matrices are also compared to SIMS results for the same samples after Ag and Au metallisation (MetA‐SIMS). For three of the four tested compounds Au MetA‐SIMS resulted in higher ion yields than ME‐SIMS. For both techniques possible mechanisms that can account for the enhancement effect are proposed. Copyright © 2005 John Wiley & Sons, Ltd.     

Statistically rigorous analysis of imaging SIMS data in the presence of detector saturation

We present a new strategy for analyzing imaging time‐of‐flight SIMS data sets affected by detector saturation. Rather than attempt to correct the measured data to remove saturation, we incorporate the detector behavior into the statistical basis of the analysis. This is performed within the framework of maximum a posteriori reconstruction. The proposed approach has several advantages over previous techniques. No approximations are involved other than the assumed model of the detector. The method performs well even when applied to highly saturated and/or single‐scan data sets. It is statistically rigorous, correctly treating the underlying statistical distribution of the data. It is also compatible with Bayesian methods for incorporating prior knowledge about sample properties. An efficient iterative scheme for solving the proposed equations is presented for the case of the bilinear model commonly used in analyses of SIMS data. The correctness of the approach and its efficacy are demonstrated on synthetic data sets. The method is found to perform better than a widely‐used data‐correction method used in combination with alternating‐least‐squares Multivariate Curve Resolution analysis. Copyright © 2015 John Wiley & Sons, Ltd.     

Comparison of a new dead‐time correction method and conventional models for SIMS analysis

Recently, secondary ion mass spectrometry (SIMS) has been used in the analysis of not only impurities but also matrix elements, thus requiring a wide dynamic range for SIMS analysis. However, SIMS detectors, which are mostly used in pulse counting systems, have difficulties with detector saturation. In this paper, we investigate whether a dead‐time model that was developed for X‐ray measurement is applicable for SIMS analysis. We then compare a new correction method with conventional correction methods for detector saturation in SIMS analysis. We report that the new method can better correct the intensity in regions of higher intensity than that achieved by conventional methods. Copyright © 2012 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.     

Quantitative analysis of styrene‐pentafluorostyrene random copolymers by ToF‐SIMS and XPS

Poly(styrene) (PS), poly(2,3,4,5,6‐pentafluorostyrene) (5FPS) and their random copolymers were prepared by bulk radical polymerization. The spin‐cast polymer films of these polymers were analyzed using X‐ray photoelectron spectroscopy (XPS) and time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). The surface and bulk compositions of these copolymers were found to be same, implying that surface segregation did not occur. The detailed analysis of ToF‐SIMS spectra indicated that the ion fragmentation mechanism is similar for both PS and 5FPS. ToF‐SIMS quantitative analysis using absolute peak intensity showed that the SIMS intensities of positive styrene fragments, particularly C₇H₇⁺, in the copolymers are higher than the intensities expected from a linear combination of PS and 5FPS, while the SIMS intensities of positive pentafluorostyrene fragments are smaller than expected. These results indicated the presence of matrix effects in ion formation process. However, the quantitative approach using relative peak intensity showed that ion intensity ratios are linearly proportional to the copolymer mole ratio when the characteristic ions of PS and 5FPS are selected. This suggests that quantitative analysis is still possible in this copolymer system. Copyright © 2005 John Wiley & Sons, Ltd.     

Ga‐focused ion beam time‐of‐flight secondary ion mass spectrometer analysis of the grain boundary segregation/precipitation of boron in steel

Ga‐focused ion beam time‐of‐flight secondary ion mass spectrometry (FIB‐TOF‐SIMS) analysis was performed to investigate the grain boundary segregation/precipitation of boron in steel. To overcome the low secondary ion yield from the primary Ga⁺ source and the sensitivity using a high‐resolution Ga‐FIB source, a low energy oxygen ion beam was used prior to the Ga‐FIB‐TOF‐SIMS analysis. As a result, it was found that Ga‐FIB‐TOF‐SIMS is a very powerful tool for mapping boron segregation and/or precipitation in steel with a spatial resolution of ~200 nm. In addition, the results were strongly dependent on the surface composition. Copyright © 2014 John Wiley & Sons, Ltd.     

Adsorption of genetically engineered proteins studied by time‐of‐flight secondary ion mass spectrometry (TOF‐SIMS). Part B: hierarchical cluster analysis (HCA)

In Part A, we adopted principal component analysis (PCA) for the analysis of TOF‐SIMS data to assess the binding specificity of GBP‐1 to metallic Au, Ag and Pd. Within a given set of data, PCA aids in the interpretation of the TOF‐SIMS spectra by capitalizing on the differences from one spectrum to another. In Part B, we introduce another multivariate statistical method called ‘hierarchical cluster analysis (HCA)’, where visualization of the similarity and difference in data is readily observed, from which a variety of adsorption conditions of GBP‐1 were characterized. Copyright © 2007 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.     

Analysis of antioxidants in insulation cladding of copper wire: a comparison of different mass spectrometric techniques (ESI–IT,MALDI–RTOF and RTOF–SIMS)

Commercial copper wire and its polymer insulation cladding was investigated for the presence of three synthetic antioxidants (ADK STAB AO412S, Irganox 1010 and Irganox MD 1024) by three different mass spectrometric techniques including electrospray ionization–ion trap–mass spectrometry (ESI–IT–MS), matrix‐assisted laser desorption/ionization reflectron time‐of‐flight (TOF) mass spectrometry (MALDI–RTOF–MS) and reflectron TOF secondary ion mass spectrometry (RTOF–SIMS). The samples were analyzed either directly without any treatment (RTOF–SIMS) or after a simple liquid/liquid extraction step (ESI–IT–MS, MALDI–RTOF–MS and RTOF–SIMS). Direct analysis of the copper wire itself or of the insulation cladding by RTOF–SIMS allowed the detection of at least two of the three antioxidants but at rather low sensitivity as molecular radical cations and with fairly strong fragmentation (due to the highly energetic ion beam of the primary ion gun). ESI–IT‐ and MALDI–RTOF–MS‐generated abundant protonated and/or cationized molecules (ammoniated or sodiated) from the liquid/liquid extract. Only ESI–IT–MS allowed simultaneous detection of all three analytes in the extract of insulation claddings. The latter two so‐called ‘soft’ desorption/ionization techniques exhibited intense fragmentation only by applying low‐energy collision‐induced dissociation (CID) tandem MS on a multistage ion trap‐instrument and high‐energy CID on a tandem TOF‐instrument (TOF/RTOF), respectively. Strong differences in the fragmentation behavior of the three analytes could be observed between the different CID spectra obtained from either the IT‐instrument (collision energy in the very low eV range) or the TOF/RTOF‐instrument (collision energy 20 keV), but both delivered important structural information. Copyright © 2009 John Wiley & Sons, Ltd.     

Linearity of the instrumental intensity scale in TOF‐SIMS—a VAMAS interlaboratory study

A VAMAS interlaboratory study involving 21 time‐of‐flight SIMS instruments from nine countries has been conducted to evaluate the linearity of the instrumental intensity scale and procedures for intensity correction. Analysts were supplied, by National Physical Laboratory (NPL), with a protocol for analysis (closely aligned with ISO 23830) together with a reference material of polytetrafluoroethylene (PTFE) tape. The primary ion beam current is varied to provide secondary ion intensities that span the linear to nonlinear regime. The natural carbon isotope ratios ¹²C_xF_y⁺/¹³C¹²C_x?1F_y⁺ for five peaks are used to evaluate the linearity, without a need to measure the ion beam current. A method is given for determining the linearity as a function of secondary ion intensity, with and without dead time correction. It is found that single ion counting statistics is closely obeyed, and the linearity achievable is generally excellent with careful application of dead time correction. Three quarters of instruments in the study achieved better than 95% linearity at a count rate of 0.8 measured counts per pulse, equivalent to 1.6 secondary ions impinging the detector per primary ion pulse. We discuss factors affecting linearity and the precise application of dead time correction and give guidance for practical analysis. This includes suboptimal detector efficiency, inhomogeneous intensities across the rastered area, inadequate charge compensation, and the choice of peak integration limits. The interlaboratory study shows that the method to determine linearity is generally applicable, robust and provides an excellent basis for the development of an ISO standard. © Crown copyright 2011. Reproduced with the permission of Her Majesty's Stationery Office. Published by John Wiley & Sons, Ltd.     

Cluster TOF‐SIMS imaging of human skin remains: analysis of a South‐Andean mummy sample

A skin sample from a South‐Andean mummy dating back from the XI^th century was analyzed using time‐of‐flight secondary ion mass spectrometry imaging using cluster primary ion beams (cluster‐TOF‐SIMS). For the first time on a mummy, skin dermis and epidermis could be chemically differentiated using mass spectrometry imaging. Differences in amino‐acid composition between keratin and collagen, the two major proteins of skin tissue, could indeed be exploited. A surprising lipid composition of hypodermis was also revealed and seems to result from fatty acids damage by bacteria. Using cluster‐TOF‐SIMS imaging skills, traces of bio‐mineralization could be identified at the micrometer scale, especially formation of calcium phosphate at the skin surface. Mineral deposits at the surface were characterized using both scanning electron microscopy (SEM) in combination with energy‐dispersive X‐ray spectroscopy and mass spectrometry imaging. The stratigraphy of such a sample was revealed for the first time using this technique. More precise molecular maps were also recorded at higher spatial resolution, below 1 µm. This was achieved using a non‐bunched mode of the primary ion source, while keeping intact the mass resolution thanks to a delayed extraction of the secondary ions. Details from biological structure as can be seen on SEM images are observable on chemical maps at this sub‐micrometer scale. Thus, this work illustrates the interesting possibilities of chemical imaging by cluster‐TOF‐SIMS concerning ancient biological tissues. Copyright © 2012 John Wiley & Sons, Ltd.     

Metabolic characterization of the early stage of hepatic fibrosis in rat using GC‐TOF/MS and multivariate data analyses

The aim of this study was to explore the changes in the urine metabolic spectrum in rats with the early stage of liver fibrosis using gas chromatography–time of flight/mass spectrometry (GC‐TOF/MS), try to search for potential biomarkers and elucidate the probably metabonomic pathogenesis. The early stage of liver fibrosis was established with a single subcutaneous injection of carbon tetrachloride twice each week for 4 weeks continuously. At the end of the experiment, GC‐TOF/MS technology with multivariate statistical approaches such as principal component analysis, partial least squares‐discriminant analysis and orthogonal partial least squares‐discriminant analysis was used to analyze the changes in the metabolic spectrum trajectory and identify potential biomarkers. Twelve potential biomarkers in the model group, such as succinic acid, threonine and lactose, were selected, which indicate that the metabonomic pathogenesis of the early stage of liver fibrosis may be related to disorders of energy metabolism, amino acid metabolism and fatty acid metabolism.     

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.     

Enhanced surface sensitivity in secondary ion mass spectrometric analysis of organic thin films using size‐selected Ar gas‐cluster ion projectiles

A size‐selected argon (Ar) gas‐cluster ion beam (GCIB) was applied to the secondary ion mass spectrometry (SIMS) of a 1,4‐didodecylbenzene (DDB) thin film. The samples were also analyzed by SIMS using an atomic Ar⁺ ion projectile and X‐ray photoelectron spectroscopy (XPS). Compared with those in the atomic‐Ar⁺ SIMS spectrum, the fragment species, including siloxane contaminants present on the sample surface, were enhanced several hundred times in the Ar gas‐cluster SIMS spectrum. XPS spectra during beam irradiation indicate that the Ar GCIB sputters contaminants on the surface more effectively than the atomic Ar⁺ ion beam. These results indicate that a large gas‐cluster projectile can sputter a much shallower volume of organic material than small projectiles, resulting in an extremely surface‐sensitive analysis of organic thin films. Copyright © 2010 John Wiley & Sons, Ltd.     

An effect of residual gas component on detected secondary ions during TOF‐SIMS depth profiling and a method to estimate contained component

With regard to Secondary Ion Mass Spectroscopy (SIMS) measurement of atmospheric gas elements, a problem occurs that the detected signal includes background components caused by residual gas along with contained components. Relating to this issue, an available method to quantify the contained components by separating the background ones had been established for Dynamic SIMS. Time‐of‐Flight SIMS with sputtering ion gun has also applied for depth profiling as well as Dynamic SIMS. However, few studies have attempted to investigate the secondary ion behavior of the atmospheric gas elements for depth profiling by Time‐of‐flight SIMS, especially for low concentration levels. In this study, experimental examinations of the secondary ions of the atmospheric gas elements, such as oxygen, hydrogen, and carbon in the silicon substrate, has been conducted in various analytical conditions of TOF‐SIMS depth profiling mode. Under the analytical conditions of our study, it has been proved that the background intensity of these elements was correlated to the sputtering rate. For the analysis of Floating Zone Silicon substrate, the oxygen intensity of the background component was proportional to the inverse number of the sputtering rate. Based on these facts, the total detected intensity of the atmospheric gas elements was able to be separated into the contained components and background ones by changing the sputtering rate during TOF‐SIMS measurement. An experimental result has shown that the contained oxygen concentration in the Czochralsk Silicon substrate estimated by the “TOF‐SIMS Raster Change Method” has successfully agreed with the result by the Dynamic SIMS.     

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.