Best of the Week: LCGC Awards, Chiral GC, and AI in Chromatography

This week on ChromatographyOnline.com, we celebrate two outstanding separation scientists and take a closer look at how artificial intelligence is changing the way chromatographers work. We open with the announcement of the 2027 LCGC Awards, honoring Robert T. Kennedy with the Lifetime Achievement in Chromatography Award and María José Trujillo-Rodríguez with the Emerging Leader in Chromatography Award. Mariosimone Zoccali of the University of Messina then shows how fast, low-pressure chiral gas chromatography–tandem mass spectrometry (GC–MS/MS) can separate more than 100 pesticides in wine. Our AI/ML Now series continues with two in-depth conversations: Sebastian Böcker of Friedrich Schiller University Jena explains why predicting the order in which compounds are eluted, rather than their exact retention times, can make liquid chromatography (LC) predictions work across laboratories, and Maximilian Besenhard of University College London describes how hybrid mechanistic and machine learning models can cut the number of experiments needed to develop high-performance liquid chromatography (HPLC) methods and pave the way for autonomous “digital twin” workflows.

This is the Best of the Week.

LCGC Announces the 2027 Winners of the Lifetime Achievement and Emerging Leader in Chromatography Awards

Robert T. Kennedy and María José Trujillo-Rodríguez win the 2027 LCGC Lifetime Achievement and Emerging Leader in Chromatography Awards.¹

Fast Low-Pressure Chiral GC for Pesticides in Wine

Mariosimone Zoccali of Italy’s University of Messina discusses the use of low-pressure gas chromatography-tandem mass spectrometry (LPGC-MS/MS) with a wide-bore chiral column to separate 107 pesticides in wine.²

AI/ML Now: Rethinking Retention Time Prediction Using Machine Learning

Sebastian Böcker of Friedrich Schiller University (Jena, Germany) discusses how predicting retention order, not time, makes liquid chromatography retention time prediction transferable across laboratories.³

In addition, in “AI/ML Now: Keeping Elution Order Intact During LC Method Transfer,”⁴ Böcker discusses taking retention prediction beyond reversed-phase to hydrophilic interaction liquid chromatography (HILIC), what changes elution order in method transfer, and sharing data via RepoRT.

AI/ML Now: Bringing Autonomous Digital Twin Workflows Into the Laboratory

Max Besenhard of University College London explains how hybrid mechanistic and machine learning models can make high-performance liquid chromatography (HPLC) method development quicker, more robust and ready for autonomous digital twins.⁵

In “AI/ML Now: Hybrid Machine Learning for Experiment-Efficient HPLC,”⁶ Besenhard explains how his group’s hybrid modeling framework, which pairs a mechanistic model with a data-driven one, can sharply reduce the number of experiments needed to develop an optimized HPLC method, and what labs need to put it into practice.

References

1. Jones, K. LCGC Announces the 2027 Winners of the Lifetime Achievement and Emerging Leader in Chromatography Awards. Chromatography Online website. https://www.chromatographyonline.com/view/lcgc-announces-the-2027-winners-of-the-lifetime-achievement-and-emerging-leader-in-chromatography-awards (accessed 2026-11-12)
2. Zoccali, M.; Chasse, J. Fast Low-Pressure Chiral GC for Pesticides in Wine. Chromatography Online website. https://www.chromatographyonline.com/view/fast-low-pressure-chiral-gc-pesticides-wine (accessed 2026-11-12)
3. Böcker, S.; Matheson, A. AI/ML Now: Rethinking Retention Time Prediction Using Machine Learning. Chromatography Online website. https://www.chromatographyonline.com/view/ai-ml-now-rethinking-retention-time-prediction-using-machine-learning(accessed 2026-11-12)
4. Böcker, S.; Matheson, A. AI/ML Now: Keeping Elution Order Intact During LC Method Transfer.Chromatography Online website. https://www.chromatographyonline.com/view/ai-ml-now-keeping-elution-order-intact-during-lc-method-transfer (accessed 2026-11-12)
5. Besenhard, M.; Matheson, A. AI/ML Now: Bringing Autonomous Digital Twin Workflows Into the Laboratory. Chromatography Online website. https://www.chromatographyonline.com/view/ai-ml-now-bringing-autonomous-digital-twin-workflows-into-the-laboratory (accessed 2026-11-12)
6. Besenhard, M.; Matheson, A. Hybrid Machine Learning for Experiment-Efficient HPLC. Chromatography Online website. https://www.chromatographyonline.com/view/ai-ml-now-hybrid-machine-learning-for-experiment-efficient-hplc(accessed 2026-11-12)