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Meet the expert panel exploring the analytical challenges of environmental PFAS detection in this LCGC International Peer Exchange®.

Gas chromatography-mass spectrometry (GC-MS) reveals harmful cooking emissions in New York City restaurant kitchens, raising worker health concerns.

Liquid chromatography-mass spectrometry (LC-MS) reveals how flunitrazepam degrades post-mortem, aiding forensic overdose confirmation.

In the first of this four-part series, Yifan Liu of Northeastern University (Boston, MA) discusses her ASMS 2026 presentation, Low-Temperature HILIC Provides Enhanced Separations and Stability for LC–MS-Based Metabolomics.

Wallman explains that PeptDeepKontext is designed to generalize without lab-specific calibration, though highly unusual setups may still benefit from additional fine-tuning in the future.

Gas chromatography-mass spectrometry (GC-MS) and spectroscopy confirm algae biodiesel as a potential clean, efficient fuel.

AI and robotics are converging to make chromatographic method development faster and smarter.AI and robotics are converging to make chromatographic method development faster and smarter.

LCGC International spoke to Sandy Al Bardawil and Ludivine Ferey, authors of a review article summarizing recent advances in chromatographic methods coupled with MS for the characterization of therapeutic oligonucleotides.

Wallman unpacks how graph-level fragmentation in fragDETR captures internal fragments and neutral losses, with peak and intensity improvements concentrated in challenging non-standard peptides.

This week's Chromatography Online highlights span LC solvent mismatch solutions, advanced PFAS monitoring by GC–MS and TD–GC–MS, deep learning in DIA proteomics, multidimensional LC–MS for plant extracts, PEG column guidance, and LC–MS/MS and GC–MS applications in consumer product safety and disease research.

Liquid chromatography-mass spectrometry (LC–MS) plasma nicotine data trains machine learning (ML) models for vaping exposure.

Nikoline Juul Nielsen from the University of Copenhagen, Denmark explores deprotomer formation for accurate CCS prediction and metabolite identification and the potential of implementing these workflows for routine use.

LCGC International spoke with Nikoline Juul Nielsen from the University of Copenhagen, Denmark on the benefits of RPLC×HILIC–cIMS for data-independent profiling of phenolic constituents in plant extracts. In this episode she explores practical aspects of cIMS and the potential for routine analysis.