Peak detection is crucial in GC×GC & LC×LC workflows. We study how signal features impact detection performance, helping select tools for both manual and automated method development.

The integration of chromatographic and statistical approaches in GC×GC-MS enhances compound separation, data interpretation, and reliability—crucial for complex sample analysis across many fields.

We developed an AI-based EI mass spectral database with 120 million predicted spectra to improve unknown compound identification in GC×GC-MS beyond commercial library limits.

LC-GC×GC-FID/TOFMS enhances MOSH/MOAH analysis in food ingredients and additives, helping to resolve coelutions and reduce interpretation errors beyond current ISO 20122:2024 limitations.

Using the FDA's Century Mix, we developed a robust QA/QC system for untargeted GC×GC-TOFMS metabolomics, tracking 100 compounds to improve standardization, reproducibility, and long-term performance.

Ultrafine particles (UFP) <100 nm pose health risks due to deep lung penetration. This study uses DTD-GC×GC-TOFMS to reveal PAH profiles in ambient air UFP sampled in Augsburg, Germany, in 2023.

TD/PY-GC×GC-MS enables detailed analysis of complex pyrolysis products. Soft ionization reveals high-mass compounds undetectable by GC-MS or GC×GC-MS.

This talk presents parallel analyses of multidimensional chromatographic data in time and frequency domains using open source tools.

Using GC×GC-TOFMS, we assess cannabis exposure in mothers and infants via urine, milk, and passive samplers, identifying metabolites and informing prenatal care.

Using HT-GC×GC-HRMS and Pearson correlation, 18 VGOs were ranked by properties, and key chemical differences were visualized across complex chromatograms.