There are many types of GC-MS and GCxGC-MS data besides just mass spectral data. By incorporating all available data when identifying unknowns, confidence in the correct assignment can be increased. In this seminar, newly developed software tools that consider all available GC-MS and GCxGC-MS information to identify unknown compounds will be shown. Additionally, a predicted-EI+ mass spectral library has been developed using machine learning. Examples of using built-in AI tools to identify unknown compounds not found in standard MS databases will be shown.
Kirk R. Jensen, PhD
Kirk graduated from the University of Northern Colorado in 2005 with a bachelor’s degree in chemistry, after which spent three years doing industrial quality assurance before returning to school to pursue a PhD. He graduated from Colorado School of Mines in 2014 with a doctorate in applied chemistry and disciplines in mass spectrometry, microbiology, and statistics.
