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High-resolution mass spectra (HRMS) were obtained at the University of Pennsylvania's Mass Spectrometry Service Center on Waters LC-TOF mass spectrometer (model LCT-XE Primer) using electrospray ionization in positive or negative mode depending on the analytes. Low-resolution mass spectra were obtained using liquid chromatography-mass spectrometry (LCMS) on a Waters instrument with electrospray ionization in either positive or negative mode. Proton nuclear magnetic resonance ( 1H NMR) and carbon nuclear magnetic resonance ( 13C NMR) spectra were recorded on a Bruker UNI 400В MHz, AVII 500В MHz and Biodrx 600В MHz NMR and processed by MestReNova or TopSpin software. The appropriate generation and selection of spectral features is considered to be the most essential part in the development of spectral classifiers. The stability of molecular ions and some functional groups can be characterized by features that describe the distribution of peaks across the mass range. Other spectral features are based on intensity ratios and reflect competing fragmentation pathways autocorrelation features contain information about mass differences between abundant peaks. The often used modulo-14 summation features (also called mass periodicity spectra) consider this fact by an intensity summation in mass intervals of 14.

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A characteristic fragment in a homologous series of compounds may not appear at the same mass but may be shifted by a multiple of 14 mass units, corresponding to (CH 2) n. It has been shown that such features are more closely related to chemical structures than the original peak data an example will illustrate this. Table 4 contains a list of frequently used spectral features for mass spectra. Problem-relevant numerical spectral features x j are defined as linear or non-linear functions of the peak intensities the definitions are based on spectroscopic and mathematical concepts. Besides the simple use of the peak intensities for these variables more sophisticated transformations have been applied. Application of most multivariate classification methods first requires a transformation of the data to be classified (the mass spectrum) into a fixed number of variables (so-called features).

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