At this year’s HPLC 2025 conference in Bruges, Belgium, attendees gathered for cutting-edge presentations and networking. One of the highlights of the event was the launch of a new book that is already proving to be a valuable resource for the separation science community. Analytical Separation Science, authored by Bob W. J. Pirok and Peter J. Schoenmakers and published by the Royal Society of Chemistry, was officially introduced during the event.
The book aims to bridge the gap between foundational principles and advanced applications, offering a complete learning pathway for both newcomers and experienced practitioners. Drawing on decades of teaching and research experience, the authors have structured the material to be as adaptable as it is comprehensive. Their modular approach allows educators to integrate selected content into their own courses, while self-learners can follow a progression that suits their level and goals.
In a field where technological advances—from micro-engineered columns to AI-assisted method development—are continually reshaping best practices, Pirok and Schoenmakers emphasize the importance of a strong scientific foundation. They designed Analytical Separation Science not just to teach techniques, but to help readers think critically, troubleshoot effectively, and approach separations with a deeper understanding of the underlying principles.
Following the launch, we spoke with the authors about their motivations, the book’s structure, and their views on current and future trends in the field.
This book spans fundamentals to advanced topics. How do you strike a balance between depth and accessibility for different audiences?
The contents and organization of the book are based on how and what we ourselves (and our colleagues in Amsterdam) teach at different levels. Each chapter consists of several modules. We distinguish between B modules, intended for BSc courses, M modules, intended for MSc courses, and advanced (A) modules, for PhD students and more-experienced separation scientists. Teachers in other universities may make their own choice from all modules.
What misconceptions do students or early-career scientists often bring to separation science?
It is not necessarily a misconception, but more a different perspective. We sometimes see chromatographers who face a separation problem getting lost in the different options, parameters, and potential causes. This is a typical case where, in our book, we try to ground the reader by bringing them back to the fundamental equation for resolution, which states clearly that we first must identify which of the three potential roads we can take. This is a case-in-point for what our book tries to address.
What are the most common mistakes you see in method development, and how should labs avoid them?
Method development is the 10th and last chapter in our book for good reasons. It is the culmination of all the knowledge and explanations in previous chapters. We realize that not every chromatographer has the opportunity to acquire all the knowledge and skills required to develop optimal methods. We are trying to provide knowledge through the book and software tools through our websites (ass-ets.org and cast-amsterdam.org) to help chromatographers as well as we can.
If methods are needed only sporadically (for example, for troubleshooting), sub-optimal methods may suffice. In contrast, if methods are to be applied numerous times (such as for controlling processes or products), systematic optimization may lead to important savings in time and consumables needed.
Moving forward, in which areas of separation science do you see the biggest potential for innovation?
We may see micro-engineered or micro-machined columns or separation channels play a greater role. Some exciting examples were presented at HPLC2025 Bruges. Two-dimensional separation systems are still improving significantly. In liquid chromatography we still need better universal detectors, but a breakthrough is not in sight.
The hottest topic at the moment is artificial intelligence (AI), but it remains to be seen how much separation science is to benefit in the near future. Notably, machine learning (ML) has been around for a few decades already and proven its use in our field. Recent developments in generative AI are not likely to revolutionize method development in chromatography, as many ML techniques require too many chromatograms to be helpful. Hybrid approaches, which combine ML tools with a great deal of knowledge on separation science are most promising. Examples include the work on QSRR modelling and peak integration. In this light, there remain some challenges ahead that chromatographers must address. For example, we still have not found a way to mathematically express what exactly constitutes a good chromatogram. ML tools employ objective functions to guide themselves to an optimal solution and need this expression to be effective. With AI unlikely to take over the field any time soon, studying our textbook remains a good idea!
If you could see one change in how industry or academia approaches separations, what would it be?
We hope that industrial and academic chromatographers will see the field as a science. There is a very solid foundation, constructed by the “Heroes of Separation Science” that we identify in our book – and many others. What we try to achieve is that our students and other readers learn to be effective and successful in developing and performing separations, because they make this foundation their own.