PATTERN-RECOGNITION STUDIES OF TANDEM MASS-SPECTRA

Principal components analysis and pattern recognition (PCAPR) techniques were applied to MS-MS spectra of fourteen organic compounds. Each spectrum was represented as a two-dimensional matrix containing information from the MS1 spectrum as well as from one, two or three MS2 spectra. The data were reduced by calculating a one-principal component model for each spectrum which explained between 86 and 99% of the variance. Each model was used to calculate each of the spectra, and residual standard deviations (R.S.D.s) were used as a measure of spectral similarity: low R.S.D.s (< 1.0) corresponding to similar spectra and higher R.S.D.s (> 1.0) to dissimilar spectra. The system shows promise for use in monitoring situations in that MS-MS spectra can be efficiently reduced and stored as principal components models and R.S.D. calculations can be used to identify a compound based on how well its spectrum is predicted by the available reference models.