B. Remberg et al., CLUSTER-ANALYSIS OF MS-SPECTRA AND IR-SPECTRA OF PYRAZINES AND RELATED HETEROCYCLIC FLAVOR SUBSTANCES, Fresenius' journal of analytical chemistry, 348(4), 1994, pp. 258-263
The application of Principal Component Analysis (PCA) on MS- and IR-sp
ectra of 77 substances from a test data set, belonging to 5 different
classes of heterocyclic components (pyrazines, pyrroles, pyridines, th
iazoles and quinoxalines, Table 1) resulted in a clear separation of t
he 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. Th
e 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 se
t by factor of 30 and allowed more efficient identification of 3 alkyl
pyrazines and 2 acylsubstituted pyrroles. The PCA of full dimensionali
ty IR-spectra only resulted in less pronounced cluster separation.