UNSUPERVISED CLASSIFICATION OF CHROMATOGRAPHIC COLUMNS IN MICELLAR AND CONVENTIONAL REVERSED-PHASE HIGH-PERFORMANCE LIQUID CHROMATOGRAPHY

Abstract

The classification of chromatographic columns with bonded octadecyl groups in conventional reversed-phase high-performance liquid chromatography (RP-HPLC) and in micellar liquid chromatography (MLC) was completed using unsupervised pattern recognition methods: principal component analysis, cluster analysis, and self-organized Kohonen neural networks (KNN). KNN provided an unbiased classification of the chromatographic columns and can be used as a powerful tool for columns selection in routine analysis. Despite the covering of C18 bonded silica surface of stationary phase with surfactant monomers in MLC, which strongly affect the compounds retention, the selectivity of the test compounds separation in MLC as well as in conventional RP-HPLC is affected by the sorbent properties. Thus, elucidation of columns similarity in both HPLC modes is of primary importance for developing robust analytical methods and selection of the analogous sorbents.

Keywords:

• cluster analysis (CA)
• column
• Kohonen neural networks (KNN)
• micellar liquid chromatography
• peak resolution
• principal component analysis (PCA)

Notes

^a Tested in MLC mode.

^b Tested in RP-HPLC mode.

Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/ljlc.