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Automated data mining of secondary ion mass spectrometry spectra
Author(s) -
Tuccitto Nunzio
Publication year - 2018
Publication title -
journal of chemometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.47
H-Index - 92
eISSN - 1099-128X
pISSN - 0886-9383
DOI - 10.1002/cem.2968
Subject(s) - principal component analysis , raw data , mass spectrometry , wavelet , data processing , component (thermodynamics) , analytical chemistry (journal) , computer science , chemistry , data reduction , mass spectrum , wavelet transform , secondary ion mass spectrometry , data mining , pattern recognition (psychology) , biological system , artificial intelligence , chromatography , database , physics , thermodynamics , programming language , biology
Time of flight secondary ion mass spectrometry (ToF‐SIMS) allows the reliable analytical determination of organic and polymeric materials. Since a typical raw data may contain thousands of peaks, 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. Here, the use of the 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 have been gathered.