Automated data mining of secondary ion mass spectrometry spectra

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.