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.     

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.     

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.     

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.     

ToF‐SIMS study of 1‐dodecanethiol adsorption on Au,Ag, Cu and Pt surfaces

Time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) has been used to perform a chemical analysis of long‐chain thiol (CH₃(CH₂)₁₁SH)‐treated gold, silver, copper and platinum surfaces. All the mass peaks from positive and negative ion spectra within the range m/z = 0–2000 u are studied. ToF‐SIMS data revealed that on gold, silver and copper substrates 1‐dodecanethiol form dense standing‐up phases, but on platinum being a catalytically active substrate, we were able to identify also surface‐aligned parallel lying molecules in addition to a standing thiolate layer. Our study shows that when ToF‐SIMS spectra are analyzed, not only the existence of oligomers but also metal + hydrocarbon fragments give information about the order of SAM. Copyright © 2008 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.     

ToF‐SIMS depth profiling of a complex polymeric coating employing a C60 sputter source

A complex poly(vinylidene difluoride) (PVdF)/poly(methyl methacrylate) (PMMA)‐based coil coating formulation has been investigated using time‐of‐flight SIMS (ToF‐SIMS). Employing a Bi₃⁺ analysis source and a Buckminsterfullerene (C₆₀) sputter source, depth profiles were obtained through the polymeric materials in the outer few nanometres of the PVdF topcoat. These investigations demonstrate that the PVdF coating's air/coating interface is composed principally of the flow agent included in the formulation. Elemental depth profiles obtained in the negative ion mode demonstrate variations in the carbon, oxygen and fluorine concentrations within the coating with respect to depth. All three elemental depth profiles suggest that the PVdF coating bulk possesses a constant material composition. The oxygen depth profile reveals the presence of a very thin oxygen‐rich sub‐surface layer in the PVdF coating, observed within the first second of the sputter/etch profile. Retrospectively, extracted mass spectra (from the elemental depth profile raw data set) of the PVdF coating sub‐surface and bulk layers indicates this oxygen‐rich sub‐surface layer results from segregation of the acrylic co‐polymers in the formulation towards the PVdF coating air/coating interface. Molecular depth profiles obtained in both the positive and negative secondary ion modes provide supporting evidence to that of the elemental depth profiles. The molecular depth profiles confirm the presence of a sub‐surface layer rich in the acrylic co‐polymers indicating segregation of the co‐polymers towards the PVdF topcoats air‐coating surface. The molecular depth profiles also confirm that the PVdF component of the topcoat is distributed throughout the coating but is present at a lower concentration at the air‐coating interface and in the sub‐surface regions of the coating, than in the coating bulk. Copyright © 2007 John Wiley & Sons, Ltd.     

A ToF‐SIMS investigation of a buried polymer/polymer interface exposed by ultra‐low‐angle microtomy

The interfacial region of a model multilayer coating system on an aluminium substrate has been investigated by high‐resolution time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS). Employing ultra‐low‐angle microtomy (ULAM), the interface between a poly(vinylidene difluoride) (PVdF)‐based topcoat and a poly(urethane) (PU)‐based primer ‘buried’ >20 µm below the PVdF topcoat's air/coating surface was exposed. Imaging ToF‐SIMS and subsequent post‐processing extraction of mass spectra of the ULAM‐exposed interface region and of the PVdF topcoat and PU primer bulks indicates that the material composition of the polymer/polymer interface region is substantially different to that of the bulk PVdF and PU coatings. Analysis of the negative ion mass spectra obtained from the PVdF/PU interface reveals the presence of a methacrylate‐based component or additive at the interface region. Reviewing the topcoat and primer coating formulations reveals that the PVdF topcoat formulation contains methyl methacrylate (MMA)–ethyl acrylate (EA) acrylic co‐polymer components. Negative ion ToF‐SIMS analysis of an acrylic co‐polymer confirms that it is these components that are observed at the PVdF/PU interface. Post‐processing extraction of ToF‐SIMS images based on the major ions of the MMA–EA co‐polymers reveals that these components are observed in high concentration at the extremities of the PVdF coating, i.e. at the polymer/polymer interface, but are also observed to be distributed evenly throughout the bulk of the PVdF topcoat. These findings confirm that a fraction of the MMA–EA acrylic co‐polymers in the formulation segregate to the topcoat/primer interface where they enhance the adhesive properties exhibited by the PVdF topcoat towards the underlying PU primer substrate. Copyright © 2004 John Wiley & Sons, Ltd.     

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.     

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.     

XPS and ToF‐SIMS study of a chalcopyrite–pyrite–sphalerite mixture treated with xanthate and sodium bisulphite

We present data from the surface analysis of a mineral mixture of chalcopyrite, pyrite, and sphalerite, elucidating surface reactions occurring during grinding and flotation. Flotation tests are also performed on the mixture, carried out in the presence of collector (SIBX) and also in the absence and presence of sodium bisulphite (NaHSO₃), a gangue sulphide mineral depressant. X‐ray photoelectron spectroscopy (XPS) studies on the ground mineral sample prior to flotation indicate that the mineral feed is heavily oxidised, especially the sphalerite in the mixture. Flotation recovery data clearly shows the effect of this oxidation, with the mineral recoveries of all three phases being lower than those observed in single mineral studies. In addition, the flotation recoveries show the effect of the inadvertent copper activation of pyrite and sphalerite, and the effect of bisulphite in reducing the flotation of sphalerite and pyrite in the mixture. Time of flight secondary ion mass spectrometry(ToF‐SIMS) data indicates that the depressing effect of bisulphite is due to the removal of copper and sulphur‐like species from the surface of pyrite and sphalerite and a consequent increase in the oxidation of these minerals. ToF‐SIMS data also indicates that the low recovery of pyrite and chalcopyrite in the absence of collector is most likely due to precipitation of zinc hydroxide on the surfaces of these minerals, formed in solution due to copper activation of sphalerite. Copyright © 2005 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.     

Differentiating calcium carbonate polymorphs by surface analysis techniques—an XPS and TOF‐SIMS study

Calcium carbonate has evoked interest owing to its use as a biomaterial, and for its potential in biomineralization. Three polymorphs of calcium carbonate, i.e. calcite, aragonite, and vaterite were synthesized. Three conventional bulk analysis techniques, Fourier transform infrared (FTIR), X‐ray diffraction (XRD), and SEM, were used to confirm the crystal phase of each polymorphic calcium carbonate. Two surface analysis techniques, X‐ray photoelectron spectroscopy (XPS) and time‐of‐flight secondary ion mass spectroscopy (TOF‐SIMS), were used to differentiate the surfaces of these three polymorphs of calcium carbonate. XPS results clearly demonstrate that the surfaces of these three polymorphs are different as seen in the Ca(2p) and O(1s) core‐level spectra. The different atomic arrangement in the crystal lattice, which provides for a different chemical environment, can explain this surface difference. Principal component analysis (PCA) was used to analyze the TOF‐SIMS data. Three polymorphs of calcium carbonate cluster into three different groups by PCA scores. This suggests that surface analysis techniques are as powerful as conventional bulk analysis to discriminate calcium carbonate polymorphs. Copyright © 2008 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.     

Evaluation of mass spectrometric data using principal component analysis for determination of the effects of organic lakes on protein binder identification

Matrix‐assisted laser desorption/ionisation–time of flight (MALDI‐TOF) mass spectrometry is commonly used for the identification of proteinaceous binders and their mixtures in artworks. The determination of protein binders is based on a comparison between the m/z values of tryptic peptides in the unknown sample and a reference one (egg, casein, animal glues etc.), but this method has greater potential to study changes due to ageing and the influence of organic/inorganic components on protein identification. However, it is necessary to then carry out statistical evaluation on the obtained data. Before now, it has been complicated to routinely convert the mass spectrometric data into a statistical programme, to extract and match the appropriate peaks. Only several ‘homemade’ computer programmes without user‐friendly interfaces are available for these purposes. In this paper, we would like to present our completely new, publically available, non‐commercial software, ms‐alone and multiMS‐toolbox, for principal component analyses of MALDI‐TOF MS data for R software, and their application to the study of the influence of heterogeneous matrices (organic lakes) for protein identification. Using this new software, we determined the main factors that influence the protein analyses of artificially aged model mixtures of organic lakes and fish glue, prepared according to historical recipes that were used for book illumination, using MALDI‐TOF peptide mass mapping. Copyright © 2015 John Wiley & Sons, Ltd.     

In situ XPS and SIMS analysis of O2+ beam‐induced silicon oxidation

The chemical composition variation of silicon under 4 keV O₂⁺ ion beam bombardment at different incident angles was studied by in situ small‐area XPS. The changes in secondary ion profile (³⁰Si⁺, ⁴⁴SiO⁺, ⁵⁶Si₂⁺, ⁶⁰SiO₂⁺) during oxygen ion beam bombardment also have been monitored. We present a direct correlation of the changes in secondary ion depth profile with surface composition during sputtering. Evolution of the secondary ion profile obtained from SIMS shows similar trends with variation of oxygen concentration in the crater surface measured by XPS. It is shown that when the oxygen ion beam incidence angle is < 40° silicon dioxide is the dominant species on the crater surface and the matrix ion species ratio (MISR) value for ⁴⁴SiO⁺/⁵⁶Si₂⁺ is higher than for ³⁰Si⁺/⁵⁶Si₂⁺. For incidence angles of >40°, the formation of sub‐oxide is favoured and thus the MISR value for ⁴⁴SiO⁺/⁵⁶Si₂⁺ is lower than for ³⁰Si⁺/⁵⁶Si₂. At 40° bombardment there are similar amounts of SiO₂ and sub‐oxides present on the crater surface and the MISR values for ⁴⁴SiO⁺/⁵⁶Si₂⁺ and ³⁰Si⁺/⁵⁶Si₂⁺ are also similar. Copyright © 2004 John Wiley & Sons, Ltd.     

ToF‐SIMS and principal component analysis: Effect of film thickness on crystal surfaces of polymers

The effect of film thickness on the structural conformation of the surfaces of the amorphous state, edge‐on lamellae and flat‐on lamellae of a semiflexible polymer, poly(bisphenol‐A‐etheroctane), was investigated by time‐of‐flight secondary ion mass spectrometry (ToF‐SIMS) with the aid of principal component analysis (PCA). PCA results empirically indicate that a structurally regular polymer surface was obtained with the formation of the flat‐on lamellae from the amorphous state at a low degree of supercooling. A higher concentration of end group and cilium ion fragments, which are indicative of free chain ends, was observed on the edge‐on lamellar surfaces than on the amorphous and the flat‐on lamellar surfaces. This finding was attributed to the fact that the lateral surface of the edge‐on lamellae contains many growth fronts, leaving behind a large number of uncrystallized chain remnants on the surfaces. Structural disorder was facilitated on both edge‐on and flat‐on lamellar surfaces as the film thickness decreased. Hence, this PCA study offers new insights into the nonequilibrium nature of polymer crystals and the mechanism of polymer crystallization in thin and ultrathin films. Copyright © 2010 John Wiley & Sons, Ltd.     

Development of an analytical strategy to identify and classify the global chemical constituents of Ziziphi Spinosae Semen by using UHPLC with quadrupole time‐of‐flight mass spectrometry combined with multiple data‐processing approaches

According to traditional Chinese medical theory, Ziziphi Spinosae Semen needs to be stir‐fried before clinical application for its sedative‐hypnotic effect enhancement. A rapid and comprehensive analysis strategy of ultra high performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry and multiple data analysis platforms was developed for the efficient and sensitive identification of components in crude and parched Ziziphi Spinosae Semen to explore the composition changes that happen during the stir‐frying process. Both positive and negative ion modes were applied for mass spectrometry detection, and 40 components were identified from crude and parched Ziziphi Spinosae Semen, respectively. Principal component analysis and t‐test were applied to find differences between crude and parched Ziziphi Spinosae Semen. As a result, Ziziphi Spinosae Semen samples could be clearly divided into two groups according to their processing methods, and 19 key markers that contributed to the classification significantly (P < 0.05) were found. This kind of change in contents of components might be responsible for the recommended clinical application of parched Ziziphi Spinosae Semen.     

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.     

Identification of four cornua by ultra‐performance liquid chromatography with time‐of‐flight mass spectrometry coupled with principal component analysis

An ultra‐high performance liquid chromatography with quadrupole time‐of‐flight mass spectrometry method coupled with principal component analysis was developed and applied to the identification of Cornu Antelopis, Cornu Bubali, Cornu Naemorhedi, and Cornu Bovis. The data obtained from the trypsin‐digested samples were subjected to principal component analysis to classify these four cornua. Additionally, marker peptides of the cornua were determined by orthogonal partial least‐squares discriminant analysis, and fragmentation tandem mass spectra of these marker peptides were evaluated. The results from this study indicate that the proposed method is reliable, and it has been successfully applied to the identification of variants of cornua commonly used in traditional Chinese medicine.