Cluster analysis of MS- and IR-spectra of pyrazines and related heterocyclic flavour substances

Summary The application of Principal Component Analysis (PCA) on MS- and IR-spectra of 77 substances from a test data set, belonging to 5 different classes of heterocyclic components (pyrazines, pyrroles, pyridines, thiazoles and quinoxalines, Table 1) resulted in a clear separation of the MS-spectral data in distinct clusters and led to the definition of planar classifiers, which were used for the detection of these classes of compounds in the spectral data set of a complex natural matrix. The projection of the GC-MS data of the headspace of opium in the plane of two main variances and the application of the planar classifier for pyrazines/pyrroles resulted in the reduction of the original data set by factor of 30 and allowed more efficient identification of 3 alkylpyrazines and 2 acylsubstituted pyrroles. The PCA of full dimensionality IR-spectra only resulted in less pronounced cluster separation.