Rapid Screening and Prioritization of Culture Conditions for Natural Product Discovery using the Liquid Microjunction Surface Sampling Probe

The discovery of novel bioactive compounds remains a cornerstone of natural product (NP) chemistry. However, traditional NP discovery workflows are time- and resource-intensive, hindering sustainability and efficiency of multicondition screening projects. This study evaluates the application of the liquid microjunction surface sampling probe (LMJ-SSP) with partial least-squares discriminant analysis (PLS-DA) as a preliminary step in the NP discovery pipeline. By integrating ambient mass spectrometry with machine learning, we analyzed four strains of <i>Penicillium</i> fungi grown under 13 unique conditions <i>in situ,</i> without sample preparation. Using PLS-DA, we prioritized the growth conditions that maximized chemical diversity, offering insights into metabolite composition prior to resource-intensive steps in the NP discovery workflow. This LMJ-SSP-based approach achieved a significant reduction in sampling time (96%), overall cost (98%), and solvent consumption (98%)─streamlining the NP discovery pipeline through chemically informed prioritization and improved sustainability.