Identification and quantification of bio-actives and metabolites in physiological matrices by automated HPLC-MS

Identification and quantification of bio-actives and metabolites in physiological matrices by automated HPLC-MS > Food plays an important role in human health. Nowadays there is an increasing interest in the health effects of so-calles functional foods, e.g. effects on blood pressure, cholesterol level and body weigth. In order to develop new functional foods and to understand the underlying physiological mechanisms, improved highly sensitive > analytical methods are required for identification and quantification of the bio-active food ingredients and their metabolites in body fluids and tissues. In this thesis the feasibility is investigated of widely applicable, automated analytical procedures for this purpose. The work is focused on small peptides from fermented milk, plant polyphenols and steroidal glycosides which are claimed to deliver abovementioned health effects when applied in particular foodstuffs. The procedures are based on liquid chromatography coupled to either quadrupole or high resolution mass spectrometry (HPLC-MS). Various sample pretreatment methods are compared, e.g. solid phase extraction (SPE), liquid/liquid extraction (LLE) and Monotrap extraction applied in particular in blood and urine. A very fast LC-MS method for the identification of blood pressure lowering peptides is described. Also a sensitive LC-MS method, combined with metabolite prediction software, was developed for the identification of bio-active steroidal glycosides and metabolites, applicable in in-vivo and in-vitro experiments. Furthermore robust quantification methods for peptides and steroidal glycosides in plasma were developed, applicable down to femtomole concentrations. Options are discussed for full automation of robotic sample preparation in line with LC-MS. The feasibility of fully automated sample analysis applicable to a broad range of compound hydrophobicities and in various sample matrices is evaluated.