Multidimensional approaches for the analysis of complex samples using HPLC

Abstract

Complex samples of natural origin, such as, tobacco leaf extracts and coffee bean extracts, comprise a vast number and variety of compounds. The characterisation of such complex samples can be arduous and time consuming. Multidimensional analytical techniques have the potential to provide detailed and informative characterisation data, especially if these techniques exploit separation selectivity and detection selectivity. This thesis explores multidimensional approaches for the analysis of complex samples using High Performance Liquid Chromatography (HPLC) and selective detection. Uni-dimensional HPLC (1DHPLC) multi-detection and multiplexed detection through the use of Active Flow Technology (AFT) columns were used to characterise tobacco leaf extracts to determine key chemical entities by systematically reducing the data complexity of the sample whilst obtaining a greater degree of molecule-specific information. Even though substantial chemical data was obtained, there were limitations; in particular the separation performance of 1DHPLC. This lead to studies directed towards two-dimensional HPLC (2DHPLC), where separation selectivity could be exploited by optimising stationary phase and mobile phase combinations; tailored to the characteristics of the sample. In this work, various combinations of stationary and mobile phases were explored to determine the optimal 2DHPLC selectivity conditions for the separation and analysis of tobacco leaf extracts. However, the performance of 2DHPLC is compromised by the interface between each dimension, namely the compatibility of the solvents and the volume of fluid required in the transfer from the first to the second dimension. This incompatibility often results in loss of separation performance. A preliminary investigation was therefore undertaken to assess the performance of AFT columns in 2DHPLC. The outcomes from this work showed that efficiency and sensitivity could both be improved with the use of AFT columns, however, the results were highly dependent on the construction of the HPLC system.

Date of Award	2014
Original language	English