Liquid Chromatography (LC/HPLC)

The first part of an end-of-the-year review of LCGC International’s editorial coverage of HPLC 2025, which took place in Bruges from 19–23 June 2025 .

The article discusses advancements in two-dimensional liquid and gas chromatography (LC×LC and GC×GC) to address the challenges of analyzing complex samples in non-target analysis.

Jessica Lin and Zhenqi (Pete) Shi from Genentech describe a novel machine learning approach to predicting retention times for small molecule pharmaceutical compounds across reversed-phase high performance liquid chromatography (HPLC) columns.

This article explores the diverse career paths where analytical sciences PhDs create impact in fundamentally different ways than traditional research roles, and provides practical guidance on how to pursue them.

What steps should be taken when reactivating your gel permeation chromatography/size-exclusion chromatography (GPC/SEC) instrument after an extended shutdown period?

A fast, selective, and quantitative liquid chromatography (LC) method was developed for lipid nanoparticles (LNPs) analysis, using evaporative light scattering for sensitive detection of the analytes of interest.

Discover the 2026 HTC Innovation Award, recognizing groundbreaking contributions in separation science and hyphenated techniques by emerging scientists.

This LCGC blog highlights the ACS SCSC’s initiatives, events, and awards that connect and support the global separation science community across academia, industry, and government.

The 54th International Symposium on High Performance Liquid Phase Separations and Related Techniques (HPLC 2025), was held from June 15–19, 2025, in Bruges, Belgium. This “Column Watch” installment presents many of the highlighted topics and trends observed at this symposium.

Explore the complexities of GLP-1 impurity detection using HPLC/UHPLC and innovative strategies to enhance analysis and regulatory compliance.

Discover innovative LC–HRMS methods for analyzing RNA-based therapeutics, enhancing biopharma development and regulatory compliance in drug characterization.

Master LC troubleshooting techniques to optimize your analysis and resolve issues with confidence.

A reversed-phase liquid chromatography–charged aerosol detector (RPLC–CAD) method was developed and validated per ICH Q2(R1) guidelines for simethicone quantification.

How does liquid chromatography (LC) assist in forensic analysis?

The development of a high-throughput LC–MS/MS method for quantifying 135 pesticides with improved accuracy and robustness is discussed.

A recent study details the development of an LC–MS/MS method for detecting pesticide residues in a challenging matrix, chili powder.

An Excel-based tool has been developed to help users understand the fundamentals of comprehensive 2D-LC.

How to take ownership of your next career steps

Kerstin Thurow, who achieved first professorship in “Laboratory Automation” at the Faculty of Engineering at the University of Rostock, in Germany, offers insights into the constantly-evolving field of laboratory automation.

HPLC is indispensable in pharmaceutical analysis and quality control despite some notable shortcomings in laboratory testing and processes to ensure regulatory compliance.

Liquid chromatography (LC) is an incredibly powerful analytical tool that can be used to characterize materials across an astonishingly wide array of applications, ranging from 1 megaDalton polystyrenes to small genotoxic impurities of concern in pharmaceutical manufacturing. However, with this power also comes with a lot of detail related to successful operation of an LC instrument and analysis of the data it produces. This can be challenging when onboarding new LC users—how do we train someone new, where do we start, and which resources are best? In this installment, I will highlight ten high priority topics that every new user should be familiar with, no matter the training mechanism.

High performance liquid chromatography (HPLC) is a widely used and well-established technique, routinely employed by thousands of analytical scientists worldwide. Nonetheless, certain challenges—arising from the complex interplay of multiple factors affecting peak retention and separation—persist. In this context, multidimensional modeling approaches can provide valuable support.

Discover how to optimize LC×LC orthogonality for organic micropollutants analysis, enhancing method development with a new open-source Python tool.

Discover how a multi-metric orthogonality score enhances LC×LC separation quality, reducing bias and improving method development in chromatography.

Are you ready to put your skills to the test?