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The research field of untargeted metabolomics aims to determine the relative abundance of all small metabolites in a biological system in order to find biomarkers or make biological inference with regards to the internal or external stimuli. This is no trivial aim, as the small metabolites are both vast in numbers and extremely diverse in their chemical properties. As such, no single analytical method exist that is able to capture the entire metabolome on its own. In addition, the data generated from such experiments is both immense in volume and very complex. This forces researchers to use algorithmic data processing methods to extract the informative part of this data. Such algorithms are, however, both difficult to parametrize and designed to be highly inclusive, the combination of which often leads to errors. One such algorithm is the peak picking procedures used to find chromatographic peaks in liquid chromatography-mass spectrometry (LC-MS) data.

In this thesis, four papers are included that focus both on the development of new methods for sample analysis and data processing as well as the application of such, and other, methods in two interdisciplinary research projects. The first paper describes the development and application of a protocol for LC-MS based untargeted analysis of guinea pig perilymph. The focus of the study was to investigate the biochemical processes underlying the protective effect of hydrogen gas on noise-induced hearing loss (NIHL) in guinea pigs exposed to impulse noise. This study sparked two research projects based on limitations observed during the analytical work. The first limitation was that of limited chemical coverage in the analysis when sample volumes are highly limited. The second paper describes the design and validation of a novel separation method for the sequential analysis of both hydrophilic and lipophilic compounds in biological samples. The second limitation observed was the abundance of false peaks reported by peak picking software. These have a negative effect on both downstream data processing as well as data analysis and metabolite identification. The third paper describes the development of a new algorithm for comprehensive peak characterization in untargeted analytical data with the purpose of filtering such false peaks. Both methods presented in the second and third paper were applied to the analysis of guinea pigs perilymph samples in a follow-up study on the attenuating effect of hydrogen gas on NIHL in guinea pigs exposed to broad band continuous noise.