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.
Unsupervised Analysis of Big ToF-SIMS Data Sets: a Statistical Pattern Recognition Approach
Nunzio Tuccitto, Giacomo Capizzi, Alberto Torrisi, and Antonino Licciardello
Analytical Chemistry 2018 90 (4), 2860-2866
Automated data mining of secondary ion mass spectrometry spectra
Journal of Chemometrics 2017;e2968.
A wavelet-PCA method saves high mass resolution information in data treatment of SIMS molecular depth profiles
Nunzio Tuccitto, Gabriella Zappalà, Stefania Vitale, Alberto Torrisi and Antonino Licciardello
Surf. Interface Anal. 2016, 48, 317–327