Unsupervised Analysis of Big ToF-SIMS Data Sets


Thanks to the capabilities of the modern analytical technologies, the typical dataset may contain thousands of information, the amount of information to deal with is accordingly large so that data reduction techniques become indispensable for extracting the most significant information from the given dataset.
I'm working on fast and low demanding methods which is able to extract latent chemical information from ToF-SIMS big data sets, such as those arising from chemical imaging, by working on the unbinned raw data files. In particular, I'm using wavelet‐principal component analysis–based signal processing of giant raw data acquired during ToF‐SIMS experiments is presented. The proposed procedure provides a straightforwardly “manageable” dataset without any binning procedure neither detailed integration. By studying the principal component analysis results, detailed and reliable information about the chemical composition of polymeric samples can be gathered.

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