Chemometric studies of retention in capillary gas chromatographic separation of hydrocarbons in coupled columns

This paper describes how different multivariate analysis and classification methods can be used, to characterize the gas chromatographic separation of complex hydrocarbon mixtures in three columns coupled in series. Principal component analysis (PCA), correspondence factor analysis (CFA), and hierarchical ascending classification (HAC) were used as potential tools for evaluating the experiments on single columns and on column series. It has been demonstrated that: (1) multivariate analysis with PCA and CFA offers a powerful strategy to search for the main factors influencing the separation of hydrocarbons without a priori knowledge of the key factors of the separation. (2) With CFA the contribution of retention due to vapour pressure can be minimized. The use of retention indices, which use the n-alkanes as reference compounds, also helps to decrease the dominant focus on vapour pressure in favor of the more selectivity-based interaction forces. (3) CFA helps to analyze the degree of relevance of the chosen experimental design to the most important factors, controlling chromatographic selectivity.