MULTICOMPONENT PRINCIPAL COMPONENT REGRESSION AND PARTIAL LEAST-SQUARES ANALYSES OF OVERLAPPED CHROMATOGRAPHIC PEAKS

Principal component and partial least-squares in latent variable regression methods were applied to the multivariate calibration of overlapping chromatographic peaks for toluene, isooctane and ethanol mixtures. The degree of peak overlap was varied using column temperatures of 105, 120 and 130-degrees-C. Even using the most severely overlapped peaks (130-degrees-C), the analysis errors obtained for validation set samples using both regression techniques were of the same size as those encountered using simple linear regression for individual determination of the three constituents. Truncation of the overlapped peak chromatograms appeared to lower the noise level without a significant loss of statistical information about the constituent concentrations.