Tile-based analysis of GC×GC–MS data reveals volatile metabolites and metabolic interactions between pathogens, enabling advanced interpretation of complex microbiological datasets.

Discover how AI-driven GC-MS workflows and predictive spectral libraries enable confident identification of unknown metabolites, integrating molecular-formula estimation and high-resolution GC×GC-TOFMS.

Discover how custom MS libraries and retention index filtering improve GC×GC–MS peak identification, alignment, and data quality in complex samples.

Integrating GC-O with GC×GC enables a direct link between chemical data and sensory perception. Sample enrichment, two-dimensional separation, and chemometrics improve aroma analysis.

ICP-OES and GC×GC-TOFMS compare new vs. used aircraft oils. Results show >20% phosphorus loss in jet oils and strong lead accumulation in piston oils—highlighting potential cabin-air and handling risks.

GC×GC-HRTOFMS enables fast and reliable screening of PFAS and emerging contaminants in complex consumer goods, combining EI/PCI/NCI and mass defect workflows for confident identification.

GC×GC-TOFMS reveals how yeast strain and honey concentration shape mead aroma. Learn the workflow from headspace sampling to PCA/PCoA/HCA, plus key VOC trends across fermentations.

Advanced chemometric methods reveal subtle, statistically significant changes in complex GC×GC-MS and LC-MS data. Learn how Alteration Analysis and 2D correlation turn massive datasets into actionable insights.

Christopher Freye (LANL) presents non-targeted PFAS analysis using LC-MS, GC×GC-TOFMS, and HRMS. Novel workflows reveal complex PFAS fingerprints in fluoropolymers and explosives.

Pilot study explores GC×GC-MS/TOF for forensic human scent analysis on cartridge cases. Promising results highlight challenges and future improvements.