Chromatography Data Systems, Part II: Specifying, Evaluating, and Selecting a System

Interest in applying artificial intelligence (AI) and machine learning (ML) to chromatography is greater than ever. In this article, we discuss data-related barriers to accomplishing this goal and how rethinking chromatography data systems can overcome them.

Here is some of the most popular content posted on LCGC International this week.

Scientists from Tabriz University and the University of Tabriz explored cellulose acetate-UiO-66-COOH as an affordable coating sorbent for thin film extraction of biogenic amines from cheese and alcohol-free beverages using HPLC-MS/MS.

The researchers explored ways to simplify retention time calculation across LC–MS and various other chromatographic methods.

Here's a taster of what to look forward to at the 54th International Symposium on High Performance Liquid Phase Separations and Related Techniques (HPLC 2025), which will be held from Sunday through Thursday, 15–19 June, 2025, in Bruges, Belgium.

This article introduces a multi-step preparative purification workflow for synthetic peptides using liquid chromatography–mass spectrometry (LC–MS). The process involves optimizing separation conditions, scaling-up, fractionating, and confirming purity and recovery, using a single LC–MS system. High purity and recovery rates for synthetic peptides such as parathormone (PTH) are achieved. The method allows efficient purification and accurate confirmation of peptide synthesis and is suitable for handling complex preparative purification tasks.