Koen Sandra Joins LCGC’s Editorial Advisory Board
LCGC is pleased to announce the addition of Koen Sandra to its editorial advisory board.
Sandra received his PhD in Biochemistry from Ghent University in Belgium in 2005. He then joined Pronata, a molecular diagnostic company, where he was active in developing analytical platforms for disease biomarker discovery and in setting up external collaborations.
In 2008, Sandra joined the Research Institute for Chromatography, where he focuses on life sciences. Currently, he leads a team of scientists working on all aspects of life sciences, including (bio)pharmaceutical analysis, proteomics, metabolomics, and lipidomics. Additionally, he serves as a consultant for several (bio)pharmaceutical and biotech companies and instrument manufacturers.
As a non-academic scientist, Sandra is the author of more than 30 highly-cited scientific papers, and has presented his work at numerous conferences as an invited speaker. He holds several patents related to analytical developments in the life sciences.
Investigating the Protective Effects of Frankincense Oil on Wound Healing with GC–MS
April 2nd 2025Frankincense essential oil is known for its anti-inflammatory, antioxidant, and therapeutic properties. A recent study investigated the protective effects of the oil in an excision wound model in rats, focusing on oxidative stress reduction, inflammatory cytokine modulation, and caspase-3 regulation; chemical composition of the oil was analyzed using gas chromatography–mass spectrometry (GC–MS).
Evaluating Natural Preservatives for Meat Products with Gas and Liquid Chromatography
April 1st 2025A study in Food Science & Nutrition evaluated the antioxidant and preservative effects of Epilobium angustifolium extract on beef burgers, finding that the extract influenced physicochemical properties, color stability, and lipid oxidation, with higher concentrations showing a prooxidant effect.
Rethinking Chromatography Workflows with AI and Machine Learning
April 1st 2025Interest 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.
