This past week, LCGC International published a variety of articles on hot topics in separation science. Michael Dong’s video series provides viewers with an educational overview of high-performance liquid chromatography (HPLC), a new interview compilation article provides an inside look at how gas chromatography (GC) is propelling forensics forward, and a recent review article highlights the statistical and visualization tools for processing lipidomics and metabolomics data using R and Python.
This is the Best of the Week.
HPLC for Practicing Scientists: A Brief History of the Technique
Michael Dong’s video series offers a comprehensive educational overview of HPLC, accessible for free on YouTube. The series is designed for scientists at any career stage, providing valuable insights and skill enhancement. Dong’s columns cover HPLC’s role in pharmaceutical analysis, new product reviews, sample preparation, and regulated testing procedures (1). The content is aimed at improving understanding and execution of HPLC techniques in various scientific and regulatory contexts (1).
Forensic Perspectives on Gas Chromatography
This article highlights advances in forensic gas chromatography (GC) through the work of three researchers. Petr Vozka (California State University, Los Angeles) uses comprehensive two-dimensional GC–time-of-flight MS (GC×GC–TOF-MS) to study chemical aging in fingerprints, offering potential for time-of-deposition estimates (2). Ira Lurie (George Washington University) employs flip-flop chromatography and GC–vacuum ultraviolet spectroscopy (VUV) to distinguish drug isomers with high precision, reducing the risk of misidentification (2). Darshil Patel (University of Windsor) analyzes volatile organic compounds (VOCs) from life to death using GC×GC–TOF-MS to enhance decomposition profiling (2). Collectively, their innovations demonstrate GC’s expanding role in forensic chemistry and data-driven investigations.
Liquid chromatography (LC), which is often coupled with mass spectrometry (MS), is transforming forensic science by enhancing analytical precision and reliability. At Aarhus University, Ida Marie Marquart Løber uses ultrahigh-pressure LC–quadrupole time-of-flight MS (UHPLC–QTOF-MS) with machine learning (ML) to estimate postmortem intervals, identifying biomarkers with error margins as low as 3–6 hours (3). At Sam Houston State University, J. Tyler Davidson employs LC–ESI-MS/MS to characterize emerging nitazene analogs, improving drug identification through fragmentation profiling (3). Meanwhile, NIST’s Walter B. Wilson advances LC–PDA methods to distinguish hemp from marijuana more accurately (3). Together, these innovations demonstrate LC–MS’s growing impact on forensic accuracy and data-driven investigation.
Profiling Pathogen-Induced Stress in Ginger Using Chromatographic Techniques
Researchers from Mohanlal Sukhadia University and the Central University of Punjab investigated fungal pathogens causing ginger rhizome rot, focusing on Pythium aphanidermatum, a major contributor to yield loss. Published in Frontiers in Microbiology, the study used molecular characterization, chromatography, and GC–MS to identify fungal metabolites and assess oxidative stress responses in infected plants (4). Results showed increased reactive oxygen species, lipid peroxidation, and chlorophyll degradation, alongside elevated antioxidant enzyme activity (4). Notably, pre-treatment with fungal crude extracts reduced oxidative damage, suggesting a potential defense role. The findings enhance understanding of P. aphanidermatum pathogenicity and support future metabolite profiling and field validation studies.
Standardizing Statistical Tools for ‘Omics’: Best Practices Using R and Python
A new Nature Communications article led by Michal Holčapek (University of Pardubice, Czech Republic) and Jakub Idkowiak (KU Leuven, Belgium) presents a comprehensive review of statistical and visualization tools for processing lipidomics and metabolomics data using R and Python (5). The study, involving 20 co-authors from 19 institutions, introduces the open-access GitBook Omics Data Visualization in R and Python, which provides example scripts, workflows, and best practices to enhance reproducibility (5). The resource supports chromatographic and mass spectrometry-based studies by enabling clearer data interpretation, improved quality control, and more reliable biomarker discovery through modular, transparent, and user-friendly analytical approaches.
ReferencesDong, M. W. HPLC for Practicing Scientists: A Brief History of the Technique. LCGC International. Available at: https://www.chromatographyonline.com/view/hplc-for-practicing-scientists-a-brief-history-of-the-technique (accessed 2025-10-22).Jones, K. Forensic Perspectives on Gas Chromatography. LCGC International. Available at: https://www.chromatographyonline.com/view/forensic-perspectives-on-gas-chromatography (accessed 2025-10-22).Jones, K. Liquid Chromatography in Forensic Science: Advances in Postmortem Analysis, Drug Identification, and Cannabis Differentiation. LCGC International. Available at: https://www.chromatographyonline.com/view/liquid-chromatography-in-forensic-science-advances-in-postmortem-analysis-drug-identification-and-cannabis-differentiation (accessed 2025-10-22).Chasse, J. Profiling Pathogen-Induced Stress in Ginger Using Chromatographic Techniques. LCGC International. Available at: https://www.chromatographyonline.com/view/profiling-pathogen-induced-stress-in-ginger-using-chromatographic-techniques (accessed 2025-10-22).Matheson, A. Standardizing Statistical Tools for “Omics’: Best Practices Using R and Python. LCGC International. Available at: https://www.chromatographyonline.com/view/standardizing-statistical-tools-for-omics-best-practices-using-r-and-python (accessed 2025-10-22).