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.

Probabilistic neural network-based classifier of ToF-SIMS single-pixel spectra

Tuccitto, Nunzio;Bombace, Alessandra;Torrisi, Alberto;Licciardello, Antonino;Lo Sciuto, Grazia;Capizzi, Giacomo;
2019

Abstract

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.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11769/367160
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