Kate Jones

Caroline West on supercritical fluid chromatography's advances, pharmaceutical applications, and sustainability.

Princeton Chromatography's Jeff Caldwell says SFC can now complete separations in 10–15 seconds.

Perception, not technology, remains the biggest barrier to supercritical fluid chromatography (SFC) adoption, says JASCO's Tom DePhillipo.

Erin Chambers reflects on two leadership philosophies and a personal mantra that have shaped her approach to leading teams.

Erin Chambers shares her near-term and long-term view of how artificial intelligence will shape the chromatography community.

UHPLC–MS/MS and GC×GC methods are cutting food-safety testing times while quantifying hundreds of mycotoxins and mineral oil hydrocarbons at once.

Erin Chambers connects trustworthy data to scientific confidence and describes how Waters builds regulatory readiness into development.

A two-dimensional gas chromatography-time-of-flight mass spectrometry method detects legacy pesticides and PCBs below guideline levels.

Erin Chambers outlines the signals Waters tracks when deciding where to invest R&D across GLP-1s, lipid nanoparticles, mRNA, and ADCs.

Erin Chambers explains why she views column chemistry, not hardware, as chromatography's real differentiator, citing two recent Waters launches.

Marinella Vitulli calls formal cross-laboratory proficiency testing, not better detection technology, the field's biggest gap in NIAS analysis.

Ruling out false-positive NIAS annotations using triplicate testing and matched oven blanks rather than spectral criteria alone.

LCGC International launches "Beyond the Critical Point: Expert Insights in SFC," a topic takeover exploring SFC's science, evolution, and real-world applications.

Ranking non-intentionally added substance (NIAS) risk by consumer exposure and packaging use-case, not by hazard data alone..

Marinella Vitulli finds concentrated extracts reveal a broader contaminant profile than simulant migration tests, but filtration risks introducing cross-contamination.

A 2026 snapshot of chromatography, mass spectrometry, software, and laboratory tools advancing automation, compliance, sensitivity, and workflow efficiency

The identification-confidence framework used to rank suspect and non-intentionally added substance (NIAS) detections is discussed.

Marinella Vitulli explains how her lab prioritizes and updates its PFAS and bisphenol screening libraries as new analogues emerge.

What five LCGC roundtables reveal about barriers, mentorship, and networking for women building careers in chromatography.

Summary: A preview of 2026 separation science trends, from PFAS and SFC to metabolomics, next-generation HPLC columns, oligonucleotides, and ADC analysis.

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.

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

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

Sheher Mohsin and Jeremy Koemel walk through a real CCS Atlas example showing how homologous series confirm unknown PFAS identifications.

Jeremy Koemel discusses detecting low-intensity PFAS features when MS/MS signal is too weak, and how they work around it.

As metabolomics datasets balloon, Siuzdak explains using retention time and in-source fragmentation checks to separate real molecules from artifacts.

Sheher Mohsin explains how CCS measurements add a physical confirmation layer beyond mass and retention time in PFAS analysis.

Siuzdak separates true standard-based empirical spectra from artifactual signals, warning AI trained on flawed data risks perpetuating errors.

Gary Siuzdak explains how uMRM converts inconsistent untargeted LC–MS data into standardized MRM transitions.