On-line supercritical fluid extraction – supercritical fluid chromatography – mass spectrometry (SFE-SFC-MS) is a powerful tool for automated preparation and analysis.  While its application base continues to grow, method development for new applications can be challenging due to the range of variables that need to be optimized, the need to trap the extract at the head of the analytical column, and the wide breadth of applications that can be considered.  We have previously utilized response surface methodology to study and optimize the extraction process.  Current efforts incorporate the use of advanced “surrogate” optimization and machine learning strategies, to create a framework for more efficient future application development.  Additionally, a sample-plug retention test was developed to help evaluate the choice of an appropriate analytical column, which can accommodate both trapping and separation of the target compounds.  This talk will survey the on-going research in our lab that combines data science and analytical measurements to achieve more streamlined method development processes for SFE-SFC-MS.

Method development strategies for on-line supercritical fluid extraction: Supercritical fluid chromatography-mass spectrometry

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