Time-of-Flight Secondary Ion Mass Spectrometry Coupled with Unsupervised Methods for Advanced Saffron Authenticity Screening
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ABSTRACT:
SUBMITTER: De Angelis E
PROVIDER: S-EPMC11241374 | biostudies-literature | 2024 Jun
REPOSITORIES: biostudies-literature
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Project description:Low secondary ion yields from organic and biological molecules are the principal limitation on the future exploitation of time of flight-secondary ion mass spectrometry (TOF-SIMS) as a surface and materials analysis technique. On the basis of the hypothesis that increasing the density of water related fragments in the ion impact zone would enhance proton mediated reactions, a prototype water cluster ion beam has been developed using supersonic jet expansion methodologies that enable ion yields using a 10 keV (H2O)1000(+) beam to be compared with those obtained using a 10 keV Ar1000(+) beam. The ion yields from four standard compounds, arginine, haloperidol, DPPC, and angiotensin II, have been measured under static+ and high ion dose conditions. Ion yield enhancements relative to the argon beam on the order of 10 or more have been observed for all the compounds such that the molecular ion yield per a 1 ?m pixel can be as high as 20, relative to 0.05 under an argon beam. The water beam has also been shown to partially lift the matrix effect in a 1:10 mixture of haloperidol and dipalmitoylphosphatidylcholine (DPPC) that suppresses the haloperidol signal. These results provide encouragement that further developments of the water cluster beam to higher energies and larger cluster sizes will provide the ion yield enhancements necessary for the future development of TOF-SIMS.
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Project description:BackgroundTime-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is a surface sensitive mass spectrometry technique with potential strengths as a method for detecting enzymatic activity on solid materials. In particular, ToF-SIMS has been applied to detect the enzymatic degradation of woody lignocellulose. Proof-of-principle experiments previously demonstrated the detection of both lignin-degrading and cellulose-degrading enzymes on solvent-extracted hardwood and softwood. However, these preliminary experiments suffered from low sample throughput and were restricted to samples which had been solvent-extracted in order to minimize the potential for mass interferences between low molecular weight extractive compounds and polymeric lignocellulose components.ResultsThe present work introduces a new, higher-throughput method for processing powdered wood samples for ToF-SIMS, meanwhile exploring likely sources of sample contamination. Multivariate analysis (MVA) including Principal Component Analysis (PCA) and Multivariate Curve Resolution (MCR) was regularly used to check for sample contamination as well as to detect extractives and enzyme activity. New data also demonstrates successful ToF-SIMS analysis of unextracted samples, placing an emphasis on identifying the low-mass secondary ion peaks related to extractives, revealing how extractives change previously established peak ratios used to describe enzyme activity, and elucidating peak intensity patterns for better detection of cellulase activity in the presence of extractives. The sensitivity of ToF-SIMS to a range of cellulase doses is also shown, along with preliminary experiments augmenting the cellulase cocktail with other proteins.ConclusionsThese new procedures increase the throughput of sample preparation for ToF-SIMS analysis of lignocellulose and expand the applications of the method to include unextracted lignocellulose. These are important steps towards the practical use of ToF-SIMS as a tool to screen for changes in plant composition, whether the transformation of the lignocellulose is achieved through enzyme application, plant mutagenesis, or other treatments.
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Project description:Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is an important technique for studying chemical composition of micrometer scale objects due to its high spatial resolution imaging capabilities and chemical specificity. In this work we focus on the application of ToF-SIMS to gain insight into the chemistry of micrometer size liposomes as a potential model for neurotransmitter vesicles. Two models of giant liposomes were analyzed: histamine and aqueous two phase system (ATPS)-containing liposomes. Characterization of the internal structure of single fixed liposomes was done both with the Bi3+ and C60+ ion sources. The depth profiling capability of ToF-SIMS was used to investigate the liposome interior.
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Project description:Time of flight secondary ion mass spectrometry (ToF-SIMS) is a powerful surface-sensitive characterization tool allowing the imaging of chemical properties over a wide range of organic and inorganic material systems. This technique allows precise studies of chemical composition with sub-100-nm lateral and nanometer depth spatial resolution. However, comprehensive interpretation of ToF-SIMS results is challenging because of the very large data volume and high dimensionality. Furthermore, investigation of samples with pronounced topographical features is complicated by systematic and measureable shifts in the mass spectrum. In this work we developed an approach for the interpretation of the ToF-SIMS data, based on the advanced data analytics. Along with characterization of the chemical composition, our approach allows extraction of the sample surface morphology from a time of flight registration technique. This approach allows one to perform correlated investigations of surface morphology, biological function, and chemical composition of Arabidopsis roots.
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Project description:Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging is used across many fields for the atomic and molecular characterization of surfaces, with both high sensitivity and high spatial resolution. When large analysis areas are required, standard ToF-SIMS instruments allow for the acquisition of adjoining tiles, which are acquired by rastering the primary ion beam. For such large area scans, tiling artifacts are a ubiquitous challenge, manifesting as intensity gradients across each tile and/or sudden changes in intensity between tiles. Such artifacts are thought to be related to a combination of sample charging, local detector sensitivity issues, and misalignment of the primary ion gun, among other instrumental factors. In this work, we investigated six different computational tiling artifact removal methods: tensor decomposition, multiplicative linear correction, linear discriminant analysis, seamless stitching, simple averaging, and simple interpolating. To ensure robustness in the study, we applied these methods to three hyperspectral ToF-SIMS data sets and one OrbiTrapSIMS data set. Our study includes a carefully designed statistical analysis and a quantitative survey that subjectively assessed the quality of the various methods employed. Our results demonstrate that while certain methods are useful and preferred more often, no one particular approach can be considered universally acceptable and that the effectiveness of the artifact removal method is strongly dependent on the particulars of the data set analyzed. As examples, the multiplicative linear correction and seamless stitching methods tended to score more highly on the subjective survey; however, for some data sets, this led to the introduction of new artifacts. In contrast, simple averaging and interpolation methods scored subjectively poorly on the biological data set, but more highly on the microarray data sets. We discuss and explore these findings in depth and present general recommendations given our findings to conclude the work.
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Project description:Mass spectrometric imaging is a powerful tool to interrogate biological complexity. One such technique, time-of-flight secondary ion mass spectrometry (TOF-SIMS) imaging, has been successfully utilized for subcellular imaging of cell membrane components. In order for this technique to provide insight into biological processes, it is critical to characterize the figures of merit. Because a SIMS instrument counts individual events, the precision of the measurement is controlled by counting statistics. As the analysis area decreases, the number of molecules available for analysis diminishes. This becomes critical when imaging subcellular features; it limits the information obtainable, resulting in images with only a few counts of interest per pixel. Many features observed in low intensity images are artifacts of counting statistics, making validation of these features crucial to arriving at accurate conclusions. With TOF-SIMS imaging, the experimentally attainable spatial resolution is a function of the molecule of interest, sample matrix, concentration, primary ion, instrument transmission, and spot size of the primary ion beam. A model, based on Poisson statistics, has been developed to validate SIMS imaging data when signal is limited. This model can be used to estimate the effective spatial resolution and limits of detection prior to analysis, making it a powerful tool for tailoring future investigations. In addition, the model allows comparison of pixel-to-pixel intensity and can be used to validate the significance of observed image features. The implications and capabilities of the model are demonstrated by imaging the cell membrane of resting RBL-2H3 mast cells.
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| S-EPMC10587417 | biostudies-literature