Principal Component Analysis of Nonlinear Chromatography

Principal component analysis (PCA) has been used for the modeling of nonlinear chromatography under overload conditions. A 10-fold range of crude erythromycin samples were loaded onto columns with different stationary-phase chemistries (2 polystyrene, 1 methacrylate) in direct proportion to the bed volumes. The elution profiles indicated slightly concave isotherms for the polystyrene supports and a convex Langmuirian isotherm for the methacrylic support used. The principal component models accounted for over 98% of the original variance in the data for all three columns and were able to give excellent models of complete chromatograms in the absence of first-principle models or physicochemical data. Correlations between sample mass and the principal component scores were made for each that were consistent for the column types despite the different geometries and stationary phases. Linear relationships with high correlation coefficients were observed when the scores of the same principal component were compared between columns. Such correlations offer considerable potential for modeling of nonlinear chromatography.