Abstract
<jats:p>The comprehensive characterization of the chemical exposome in large epidemiological cohorts requires high-throughput, reliable, and sensitive analytical pipelines. We present a scalable, fully automated workflow for liquid-liquid extraction coupled with gas chromatography-high resolution mass spectrometry (GC-HRMS), designed for simultaneous targeted and non-targeted exposomics. Suitable for large-scale studies, with a runtime of 25 min per sample that includes robotic sample preparation and data acquisition, this work describes an analytical framework to ensure high data quality in human biomonitoring and exposomics studies. It requires a low volume of biospecimen, 0.1 mL of plasma, and maintains compatibility with parallel LC-MS workflows for enhancing chemical space coverage. A rigorous quality control strategy was implemented, highlighting the apparent recovery as a key parameter for evaluating performance in GC-HRMS. The presented workflow quantifies >130 anthropogenic compounds, mainly persistent organic pollutants (PCBs, PBDEs, PCDDs, and OCPs), other pesticide classes (such as organophosphates and pyrethroids), and PAHs. Moreover, the use of full-scan acquisition enables potential retrospective data analysis for emerging contaminants, suspect screening, and non-targeted analysis. Following the evaluation of different extraction solvents for the broad target panel, isooctane was selected for yielding the highest overall apparent recoveries. Three approaches for sensitivity assessment were evaluated, resulting in limits of quantification ranging from 0.2 to 2.0 ng/mL. Procedural calibration, with fortified biological samples and cohort-specific pooled QC samples, is proposed to effectively correct for matrix interferences and ensure accurate (semi-)quantification across analytical batches. The final analytical design confirmed the fitness-for-purpose of the automated-LLE-GC-HRMS pipeline. Furthermore, the integration of NIST SRM 1958 served as a benchmark for in-house long-term stability and inter-lab comparability. The scalable, standardized framework addresses limitations in sample availability while providing the reliability needed for exposome-wide association studies (ExWAS).</jats:p>