Automated non-target screening workflow for source tracking of organic contaminants in industrialized coastal...

This study describes the development of an automated workflow for non-target screening data processing and applies multivariate statistical analysis to characterize chemical fingerprints in sediment from industrialized coastal regions. R-based software facilitates streamlined processing of gas chromatography coupled with time-of-flight mass spectrometer datasets through retention index-based filtering, feature consolidation, and blank subtraction, enhancing data reliability and minimizing manual workloads. The workflow is applied to sediment from industrialized bays, and partial least squares discriminant analysis is used to distinguish site-specific chemical fingerprints. Tentatively identified compounds are linked to specific industrial activities, including petrochemical manufacturing, fossil-fuel combustion, waste incineration, and shipbuilding. Model performance and compound relevance are validated through cross-validation and permutation testing, confirming the robustness of the workflow. This integrated approach provides a scalable and reproducible tool for the tracking of chemical sources and environmental monitoring, highlighting the utility of combining non-target screening workflow with statistical analysis in sediment studies.