A new automated procedure able to extract latent chemical information from ToF-SIMS big data sets is presented. A classifier based on a probabilistic neural network able to provide a correct classification of spectra acquired from four different polymers is designed and trained. The procedure is fast and low-demanding in terms of CPU performances, and it is also able to evaluate the similarity/dissimilarity in the Fourier transform domain of very low intensity single pixel spectra without any effort of the analyst.
Titolo: | Probabilistic neural network-based classifier of ToF-SIMS single-pixel spectra | |
Autori interni: | ||
Data di pubblicazione: | 2019 | |
Rivista: | ||
Handle: | http://hdl.handle.net/20.500.11769/367160 | |
Appare nelle tipologie: | 1.1 Articolo in rivista |
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