Identifying Process\nDifferences with ToF-SIMS: An\nMVA Decomposition Strategy

In time-of-flight secondary ion mass spectrometry (ToF-SIMS),\nmultivariate\nanalysis (MVA) methods such as principal component analysis (PCA)\nare routinely employed to differentiate spectra. However, additional\ninsights can often be gained by comparing processes, where each process\nis characterized by its own start and end spectra, such as when identical\nsamples undergo slightly different treatments or when slightly different\nsamples receive the same treatment. This study proposes a strategy\nto compare such processes by decomposing the loading vectors associated\nwith them, which highlights differences in the relative behavior of\nthe peaks. This strategy identifies key information beyond what is\ncaptured by the loading vectors or the end spectra alone. While PCA\nis widely used, partial least-squares discriminant analysis (PLS-DA)\nserves as a supervised alternative and is the preferred method for\nderiving process-related loading vectors when classes are narrowly\nseparated. The effectiveness of the decomposition strategy is demonstrated\nusing artificial spectra and applied to a ToF-SIMS materials science\ncase study on the photodegradation of N719 dye, a common dye in photovoltaics,\non a mesoporous TiO<sub>2</sub> anode. The study revealed that the\nphotodegradation process varies over time, and the resulting fragments\nhave been identified accordingly. The proposed methodology, applicable\nto both labeled (supervised) and unlabeled (unsupervised) spectral\ndata, can be seamlessly integrated into most modern mass spectrometry\ndata analysis workflows to automatically generate a list of peaks\nwhose relative behavior varies between two processes, and is particularly\neffective in identifying subtle differences between highly similar\nphysicochemical processes.