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Researchers explored the potential of Prunus laurocerasus (cherry laurel) and Prunus cerasifera (cherry plum) fruit pits as natural food additives. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) revealed that the extracts were found safe for healthy cells, selectively targeted certain cancer cells, and showed strong antioxidant and enzyme-inhibiting activities, including anti-browning effects. These findings highlight the pits’ promise as sustainable, functional food ingredients.

This week on ChromatographyOnline.com, we highlight sustainable sample preparation alongside the transformative impact of AI and machine learning in chemical analysis, and explore advanced methodologies like ion mobility-HRMS and HPLC for tackling complex real-world challenges.

A study identified invasive Burmese pythons as effective biological sentinels for monitoring chemical pollution in the Florida Everglades. Researchers analyzed liver samples from 67 pythons using high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) to track 30 per- and polyfluoroalkyl substances (PFAS).

Gauthier Eppe discusses how applicable the SWIM approach is beyond halogenated POPs.

André Striegel recently published a book called "Macromolecular Separations". In this segment, he highlights the most significant technological advances and changes in macromolecule analysis in the last ten years.

German researchers used gas chromatography-mass spectrometry (GC–MS) and liquid chromatography-mass spectrometry (LC–MS) to analyze urinary and plasma steroid hormones in a female athlete, revealing cycle-dependent fluctuations in androgen levels during a natural menstrual cycle, with peaks around ovulation and declines in the luteal phase. In contrast, hormonal contraceptive use suppressed androgen variability, demonstrating the value of chromatographic techniques for sensitive monitoring of endocrine changes relevant to athletic performance and physiology.

LCGC International spoke to Mary Ellen McNally about green and sustainable chromatographic approaches and sample preparation.

To investigate how permanent chemical hair straightening influences the accuracy of hair drug testing using liquid chromatography–tandem mass spectrometry (LC-MS/MS), researchers analyzed hair samples before and after treatment with an alkaline straightener (ammonium thioglycolate), measuring levels of the endogenous compound gamma-hydroxybutyrate and the drug alprazolam. The study emphasizes the need for careful interpretation of LC-MS/MS hair analysis results and systematic documentation of cosmetic treatments in forensic and toxicological investigations, particularly when detecting low-level substances.

At Pittcon 2026, LCGC International Emerging Leader Award Winner, Bob Pirok, discussed the barriers to fully automated method development using machine learning in chromatography and if "intuition" can be incorporated.

André Striegel examines the major hurdles facing macromolecular separations today and discusses how chromatographers can work to overcome them.

At Pittcon 2026, LCGC International Emerging Leader Award Winner, Bob Pirok, emphasized the importance of incorporating chromatographic expertise into machine learning approaches for successful automated method development.

A mass balance and pharmacokinetic study of fezolinetant in healthy postmenopausal women employed high-performance liquid chromatography (HPLC) with radioactivity detection alongside liquid chromatography–tandem mass spectrometry (LC–MS/MS) to enable detailed metabolite profiling and structural elucidation. Overall, chromatographic techniques were central to quantifying drug disposition, identifying metabolic routes, and supporting the clinical development of this non-hormonal therapy for vasomotor symptoms.

To address the need for objective and non-invasive methods to measure workplace stress, a recent study evaluated metabolite profiling of mouth-rinsed water. Researchers using capillary electrophoresis-mass spectrometry (CE-MS) and liquid chromatography-mass spectrometry (LC-MS) analyzed 559 analytes from 32 participants categorized into high-stress and control groups.

At analytica 2026, Gauthier Eppe described adding ion mobility–HRMS to GC–HRMS workflows to support analysis of environmental pollutant mixtures.

Saer Samanipour from the University of Amsterdam, The Netherlands used machine-learning (ML)-predicted retention time indices to estimate true-match probabilities to boost identification confidence when retention time calibrants are unavailable.

Because sample preparation uses the most solvents, it is typically the least environmentally friendly step of the process. While completely eliminating sample preparation is the ideal "green" scenario, it is rarely practical. Therefore, over the next few columns, "Sample Prep Perspectives" will evaluate various tools developed over the past decade to gauge the sustainability of these methods, with the ultimate goal of helping the scientific community adopt a universal, practical standard for measuring analytical "greenness."

Controlling the "heat" or pungency levels in chili peppers (Capsicum annuum) is a major challenge for breeders due to complex genetic inheritance. To address this, researchers are using high-performance liquid chromatography (HPLC) to precisely quantify capsaicin and dihydrocapsaicin levels. By combining these accurate chemical measurements with genomic prediction (GP) models, scientists can now better predict the spice levels of offspring based on parental data.

Gas chromatography-mass spectrometry (GC–MS), liquid chromatography-mass spectrometry (LC–MS), and inductively coupled plasma (ICP)-based techniques were applied to characterize extractables and potential leachables from polyurethane-insulated deep brain stimulation probes, comparing polyether urethane (PEU) and polycarbonate urethane (PCU) after prolonged exposure to a brain phantom.

André Striegel discusses how work in industry, academia, and government has shaped his view of innovation in separation science and practical impact.

Researchers used high-performance liquid chromatography–mass spectrometry (HPLC–MS) to perform targeted metabolomic analysis of urine samples from children with autism spectrum disorder (ASD) and healthy controls. By focusing on metabolites associated with the microbiota–gut–brain axis, researchers identified distinct biochemical patterns that differentiate ASD from typical development. The chromatography-based approach enabled precise and reliable measurement of seven key metabolites, highlighting its value over broader, non-targeted methods.

Click here to access the LCGC International April 2026 European PDF in an interactive format.

Click here to access the LCGC International April 2026 North American PDF in an interactive format.

A recent paper introduced what the authors described as “an uncertainty-aware graph-based neural network that predicts retention times across multiple column chemistries and buffer pH conditions. LCGC International spoke to some of the authors of this paper, including corresponding author Pankaj Aggarwal, about their work.

At Pittcon 2026, Dal Nogare Award winner André Striegel discusses unresolved chromatography fundamentals and outlines research paths for progress.

Rasmus Bro shows how generative AI extends chemometrics by automating curation, linking analytical data with text knowledge, and improving interpretation.

Top articles published this week include a two part discussion on slalom chromatography, a look at the current analytical procedure lifecycle practices, and the utility of liquid chromatography coupled with mass spectrometric detection (LC–MS) for analysis of oligonucleotides (ONs).

Headspace solid-phase microextraction coupled with GC–MS (HS-SPME/GC-MS), integrated with machine learning, enabled comprehensive profiling of 127 volatile compounds across multiple vinegar types. Random Forest modeling achieved high classification accuracy, identifying key aroma markers and demonstrating the power of chromatographic–data science workflows for differentiating fermented food products.

At Pittcon 2026, Mary Ellen McNally discussed sustainable chromatography, highlighting SFC, low-solvent LC, and greener sample prep to cut waste in industrial labs.