Data Analysis for Multi-Dimensional Liquid Chromatography

Abstract

Multi-dimensional liquid chromatography methods, particularly those involving comprehensive multi-dimensional separation, often produce datasets that are much richer and more complex than those normally obtained by conventional one-dimensional separations. Of course, it is great for the analyst to be able to draw on these richer datasets, but the volume and complexity of the data pose new data analysis challenges that cannot be addressed using tools developed for one-dimensional chromatography alone. In this chapter we first discuss recent advances in the development of methods for treating one-dimensional chromatography data, as these do indeed form the basis of methods for treating two-dimensional data. We then go on to discuss challenges unique to the analysis of data from multi-dimensional chromatography methods, and the methods that have been developed to address these challenges. Finally, we discuss multi-way analysis methods that not only are capable of dealing with multi-dimensional data, but are also well suited to capitalize on the power of these methods for resolving chromatographic signals from background signals, and from each other (i.e., mathematical resolution of overlapping peaks). Future advances in this area will benefit from the use of benchmark datasets that allow comparison of new data analysis methods to established ones, and evaluation of method performance using real data from multiple sources