Chemometric Tools for Enhanced Performance in Liquid Chromatography-Mass Spectrometry

University dissertation from Uppsala : Acta Universitatis Upsaliensis

Abstract: Liquid chromatography-mass spectrometry (LC-MS) has become an important analytical on-line technique, capable of producing large amounts of data with high selectivity and sensitivity. Optimal use of the sophisticated instrumentation can be attained if the analytical chemists are guided to perform the proper experiments and to extract the useful information from the acquired data. In this thesis, strategies and methods concerning these two issues are presented.LC-MS method development will benefit from fundamental understanding of the processes involved. An experimental procedure was designed to determine the coefficients in a model for the electrospray process. By relating these coefficients to the experimental conditions, the influence on signal level and sensitivity for presence of matrix compounds was studied.For the optimization of LC-MS methods, strategies based on empirical modelling were worked out. Comparisons were made between artificial neural network (ANN) modelling and linear modelling tools, and a genetic algorithm was implemented to explore the ANN models.Visual interpretation and multivariate analysis of LC-MS data is hampered by background signals and noise, and a digital filter for background suppression and signal-to-noise improvement was developed. It is also important to indicate the presence of overlapping peaks, and a strategy for the assessment of peak purity was therefore worked out. These methods and several established methods were implemented in an add-on program (LC-MS Toolbox 1.0) for information extraction of LC-MS data.Ultimately, the data produced with LC-MS can be separated into the mass spectra, the elution profiles and the concentrations of the analytes, e.g. with PARAFAC modelling. The trilinear data structure assumed may, however, be distorted by variations in the LC conditions causing retention time shifts. An improved algorithm for time warping that can compensate for some of these deviations was worked out, and its performance as a pre-processing tool for PARAFAC was examined.

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