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

被引：13

作者：

Tuccitto, Nunzio ^{[1
]}

Bombace, Alessandra ^{[1
]}

Torrisi, Alberto ^{[1
]}

Licciardello, Antonino ^{[1
]}

Lo Sciuto, Grazia ^{[2
]}

Capizzi, Giacomo ^{[2
]}

Wozniak, Marcin ^{[3
]}

机构：

[1] Univ Catania, Dept Chem Sci, Viale Andrea Doria 6, I-95125 Catania, Italy

[2] Univ Catania, Dept Elect Elect & Informat Engn, Viale Andrea Doria 6, I-95125 Catania, Italy

[3] Silesian Tech Univ, Inst Math, Kaszubska 23, PL-44100 Gliwice, Poland

来源：

CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS | 2019年 / 191卷

关键词：

Secondary ion mass spectrometry; ToF-SIMS spectra; Neural networks; Feature extraction; ToF-SIMS single-pixel spectra classification; ION MASS-SPECTROMETRY; PRINCIPAL COMPONENT ANALYSIS; MULTIVARIATE-ANALYSIS; FILMS;

D O I：

10.1016/j.chemolab.2019.07.002

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：138 / 142

页数：5

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