| Summary: | Background: BMFishfeed is a cooperative project since 2021 between UiT the Arctic University of Norway, NORCE Stavanger and the University of South-Eastern Norway where the goal is to develop a lipid-rich bacterial meal that can be utilized as fish feed in the aquaculture industry. Carbon-rich waste from an omega-3 production site is used as nutrition for marine bacterial cultures, which are fermented and fed to promote lipid production in the bacteria. Alternatively, propionic acid (PA) is utilized as nutrition. The biomass is then dried to form a bacterial meal. Lipidomics was used to analyze the bacterial meal using liquid chromatography and mass spectrometry (LC-MS) in this thesis. The goal was to identify the lipid profile/lipidome in the bacterial meal. The original method for lipid extraction, Soxhlet extraction, was time and solvent consuming, and there was a need for a more efficient extraction method. Different extraction techniques were performed and compared to find the most efficient method concerning lipid yield. Method: Sonication followed by direct extraction in DCM:MeOH was compared to Soxhlet extraction and direct extraction in DCM, providing a higher lipid yield. Biomass from a mixed microbial culture (MMC) was accumulated and harvested before it was sonicated to break down the cell walls. The sonicated biomass was then lyophilized to remove the water before extraction of lipids with dichloromethane and methanol (DCM:MeOH) as extraction solvent was performed. The lipids were dried to calculate dry weight before it was solved in isopropanol as preparation before Ultra High-Performance Liquid Chromatography – Mass Spectrometry (UHPLC-MS) analysis. The analysis was carried out in positive ionization mode to identify as many lipids as possible. The analysis data was managed through Thermo Fisher Scientifics data acquisition software called AcquireX. Lipids were identified using the data from AcquireX in the LipidSearch software. PHA precipitation was examined in different solvents as PHA is ...
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