Machine learning is considered increasingly important in analytical separation science because of its potential to enable faster and more accurate interpretation of complex, high-dimensional data from techniques like chromatography and mass spectrometry. Additionally, machine learning accelerates method development and improves reproducibility, leading to more efficient and reliable analytical workflows.
Introduction to Artificial Intelligence
To help both newcomers and experienced practitioners in the field, Bob Pirok and Tijmen Bos were invited to give a short course entitled Introduction to Artificial Intelligence in Chromatography at the 54th International Symposium on High Performance Liquid Phase Separations and Related Techniques (HPLC2025) in Bruges, Belgium.
The course, designed for both academic and industry scientists, was structured in four parts. It began with a clear introduction to the foundations of artificial intelligence, including the historical context and core concepts such as regression, optimization, and pattern recognition. In the second part, the presenters expanded into modern machine learning techniques — from support vector machines to neural networks and reinforcement learning approaches such as Q-learning and Proximal Policy Optimization (PPO).
The third part showcased real-world applications in chromatography, including predictive modeling for retention time, peak detection using neural networks, and data-driven optimization of method parameters. The session concluded with hands-on exercises, challenging participants to apply what they had learned to realistic chromatographic problems.
“AI is not a replacement for analytical expertise, it’s an extension of it,” said Dr. Pirok. “With the right understanding, these tools can help us interpret complex data faster and develop better methods with fewer experiments.”
Our goal is to demystify AI for chromatographers. These technologies are no longer futuristic — they’re ready to be applied, provided we know how to ask the right questions.
Dr. Bos added, “Our goal is to demystify AI for chromatographers. These technologies are no longer futuristic — they’re ready to be applied, provided we know how to ask the right questions.”
The short course also served as a platform to emphasize responsible and informed application of machine learning in laboratory settings. The presenters stressed the importance of data quality, domain knowledge, and understanding the assumptions behind different models.