Optimization of a liquid chromatography ion mobility-mass spectrometry method for untargeted metabolomics using experimental design and multivariate data analysis

摘要: High-resolution mass spectrometry coupled with pattern recognition techniques is an established tool to perform comprehensive metabolite profiling of biological datasets. This paves the way for new, powerful and innovative diagnostic approaches in post-genomic era molecular medicine. However, interpreting untargeted metabolomic data requires robust, reproducible reliable analytical methods translate results into biologically relevant actionable knowledge. The analyses samples were developed based on ultra-high performance liquid chromatography (UHPLC) ion mobility - (IM-MS). A strategy optimizing conditions UHPLC-IM-MS proposed using experimental design approach. Optimization experiments conducted through a screening process designed identify factors that have significant effects selected responses (total number peaks peaks). For this purpose, full fractional factorial designs used while partial least squares regression was modeling optimization parameter values. total yielded best predictive model parameters setting.