Analysis of atmospheric plasma-treated polypropylene by large area ToF-SIMS imaging and NMF

Applications of multivariate analysis and unsupervised machine learning to ToF-SIMS images of organic, bioorganic, and biological systems

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging offers a powerful, label-free method for exploring organic, bioorganic, and biological systems. The technique is capable of very high spatial resolution, while also producing an enormous amount of information about the chemical and molecular composition of a surface. However, this information is inherently complex, making interpretation and analysis of the vast amount of data produced by a single ToF-SIMS experiment a considerable challenge. Much research over the past few decades has focused on the application and development of multivariate analysis (MVA) and machine learning (ML) techniques that find meaningful patterns and relationships in these datasets. Here, we review the unsupervised algorithms—that is, algorithms that do not require ground truth labels—that have been applied to ToF-SIMS images, as well as other algorithms and approaches that have been used in the broader family of mass spectrometry imaging (MSI) techniques. We first give a nontechnical overview of several commonly used classes of unsupervised algorithms, such as matrix factorization, clustering, and nonlinear dimensionality reduction. We then review the application of unsupervised algorithms to various organic, bioorganic, and biological systems including cells and tissues, organic films, residues and coatings, and spatially structured systems such as polymer microarrays. We then cover several novel algorithms employed for other MSI techniques that have received little attention from ToF-SIMS imaging researchers. We conclude with a brief outline of potential future directions for the application of MVA and ML algorithms to ToF-SIMS images.

Mass spectrometry as a tool to advance polymer science

In contrast to natural polymers, which have existed for billions of years, the first well-understood synthetic polymers date back to just over one century ago. Nevertheless, this relatively short period has seen vast progress in synthetic polymer chemistry, which can now afford diverse macromolecules with varying structural complexities. To keep pace with this synthetic progress, there have been commensurate developments in analytical chemistry, where mass spectrometry has emerged as the pre-eminent technique for polymer analysis. This Perspective describes present challenges associated with the mass-spectrometric analysis of synthetic polymers, in particular the desorption, ionization and structural interrogation of high-molar-mass macromolecules, as well as strategies to lower spectral complexity. We critically evaluate recent advances in technology in the context of these challenges and suggest how to push the field beyond its current limitations. In this context, the increasingly important role of high-resolution mass spectrometry is emphasized because of its unrivalled ability to describe unique species within polymer ensembles, rather than to report the average properties of the ensemble.