Tile-based Fisher ratio of GC×GC-TOFMS data in petroleum samples (Rachel Halvorsen, MDCW 2026)
A chemometric workflow combining tile-based F-ratio and P-test enables comprehensive peak detection in GC×GC–MS, generating sample-specific peak tables for complex petroleum analysis.
Tile-based Analysis of GC×GC-TOFMS Data of SPME Sampled VOCs Produced from Pseudomonas aeruginosa and Aspergillus fumigatus (Wenjing Ma, MDCW 2026)
Tile-based analysis of GC×GC–MS data reveals volatile metabolites and metabolic interactions between pathogens, enabling advanced interpretation of complex microbiological datasets.
Structural elucidation using GC×GC-TOFMS and machine learning (Masaaki Ubukata, MDCW 2026)
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.
Leveraging Local Libraries for Positively Parsimonious Peak Tables (James Harynuk, MDCW 2026)
Discover how custom MS libraries and retention index filtering improve GC×GC–MS peak identification, alignment, and data quality in complex samples.
Sniff smarter: Empowering GC–O with trap-based enrichment and GC×GC for advanced aroma profiling (Lina Mikaliunaite, MDCW 2026)
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.
Analysis of fresh and used aircraft oil: Exposure possibility to pollutants (Kevin Hayes, MDCW 2026)
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.
Exploring PFAS in consumer goods using GCxGC-high-resolution mass spectrometry (David Alonso, MDCW 2026)
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.
Application of GC×GC for the investigation of fermented beverages (Sarah C. Foster, MDCW 2026)
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.
Discovery analysis for GCxGC trends using alteration and 2D correlation (Chris Freye, MDCW 2026)
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.
