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This week, Chromatography Online featured conversations from ASMS 2026 on untargeted MS/MS quantitation, RPLC-HRMS analysis of PLGA copolymers, and AI/ML predictions for non-targeted LC–ESI–HRMS workflows, alongside expert debate on harmonizing PFAS analytical methods and a sensor GC study examining whether water chasers affect alcohol metabolism.

Atmospheric pressure matrix-assisted laser desorption/ionization mass spectrometry (AP-MALDI-MS screens black pepper for fraud with minimal preparation.

Sheher Mohsin and Jeremy Koemel explore where machine learning and predictive CCS could transform PFAS identification, and what's holding it back.

Siuzdak argues the field must correct AI-fueled “phantom metabolites” before trusting predictive models, favoring experimentally grounded measurement.

The panel weighs non-targeted screening against total PFAS measurement to catch the compounds routine targeted methods may miss entirely.

Various chromatography-spectrometry techniques map lipids to isolate omega-3s from algae.

Shimadzu's new TOC-1000e S continuously monitors ultrapure water for chip and precision manufacturing.

Gas chromatography-mass spectrometry (GC-MS) identifies compounds in essential oils that fight cucumber mildew.

Sheher Mohsin and Jeremy Koemel identify the biggest bottlenecks in non-targeted PFAS workflows, from CCS libraries to human review.

Lapo Renai from the Van 't Hoff Institute for Molecular Sciences, The Netherlands discusses a novel prediction model for estimating chemical space coverage for non-targeted LC–ESI–HRMS workflows.

With 960,000 empirically acquired standards, Siuzdak shows how METLIN delivers reliable, cross-instrument IDs and reveals fragmentation artifacts.

Sensor gas chromatography (GC) determines whether water chasers change alcohol metabolism.

Headspace-gas chromatography-ion mobility spectrometry (HS-GC-IMS) tracks how baking temperature shapes low-protein cookie flavor.