Best of the Week: Sustainability, PFAS, and ASMS


This week, LCGC International published a variety of articles on the hottest topics in chromatography and beyond. Below, we’ve highlighted some of the most popular articles, according to our readers. Happy reading!

Sustainability and Separation Science: An Update

Alasdair Matheson

Elefteria (Elia) Psillakis is a Professor in Water Chemistry at the School of Chemical and Environmental Engineering, Technical University of Crete, Greece. She is Head of the Sample Preparation Network of the EuChemS-Division of Analytical Chemistry, Editor-in-Chief of “Advances in Sample Preparation” (Elsevier), and the Founder and Director of ExtraTECH Analytical Solutions, a spin-off company of the Technical University of Crete. Recently, she published two highly cited papers that focus on the 10 principles of green sample preparation and an innovative green metric tool called AGREEprep. Additionally, she elaborated on the newly published goal-framework of circular analytical chemistry and its relation to sustainability. Recently, she spoke to The Column about these papers and specifically discussed sustainability and separation science.

Investigating PFAS in Plastic Food Storage Bags Using LC–MS/MS

Alasdair Matheson

Yelena Sapozhnikova is a Research Chemist at the United States Department of Agriculture (USDA) in Wyndmoor, PA, USA. Her research focuses on the development of novel methods for the analysis of chemical contaminants in foods and food packaging to improve their analysis and enhance food safety. Recently, she conducted research investigating PFAS in plastic food storage bags using targeted and non targeted liquid chromatography–tandem mass spectrometry (LC–MS/MS). As part of this, rigorous QA–QC procedures were implemented to overcome challenges associated with background contamination and interferences. In this interview, The Column spoke to her about her research.

ASMS Comes to Anaheim for 2024 Edition

Patrick Lavery

The 72nd edition of the American Society for Mass Spectrometry (ASMS)’s Conference on Mass Spectrometry and Allied Topics, otherwise known as ASMS 2024, is fast approaching. The meeting will be held June 2–6, 2024 at the Anaheim Convention Center in Anaheim, California, with short courses to be presented on June 1–2. ASMS was first founded in 1969, and according to the group’s website, the conference draws in crowds of more than 6500 annually and presenting more than 3000 papers in either lecture or poster format. To learn more about important dates during the conference, networking opportunities, award presenations, and more, be sure to read up on the conference and look for more information on the ASMS website.

A Look Inside the 2024 ASMS Awards Program

Caroline Hroncich

Every year, the American Society for Mass Spectrometry (ASMS) presents multiple awards to experts in the field. The society, which is made up of more than 8500 scientists, annually honors nine scientists working in different applications of mass spectrometry (MS). These awards include the John B. Fenn Distinguished Contribution in Mass Spectrometry Award, the Research Awards, and the Biemann Medal, all of which honor different aspects of mass spectrometry-related work. The following article describes these awards and their criteria more in-depth.

Removing Batch Effects in Large Liquid Chromatography-Mass Spectrometry Experiments

John Chasse

Liquid chromatography-mass spectrometry (LC–MS) has become a powerful method for profiling complex biological samples. However, batch effects typically arise due to the omnipresence of confounding factors, which can be divided into those biological in nature (such as age or gender) and non-biological (such as batch effects). To rectify this, a recent study published in Nature Communications suggested using various batch effect removal neural networks (BERNN) to remove these batch effects. The authors behind this paper, which represent laboratories in the United States, Canada, the Netherlands, and more, present this approach as being different from other solutions, as it does not rely on a single solution but rather proposes multiple potential solutions to address batch effects.

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