Looking Back, Moving Forward: With Deirdre Cabooter

What would you say has been the biggest accomplishment in the field of analytical science as a whole over the past decade or so?
 

I couldn't narrow it down to just one! One major advancement that has had a huge impact on society as a whole, as well as analytical sciences more generally, is the introduction and integration of machine learning and artificial intelligence. Now a part of our daily lives, it's been exciting to see how these technologies have been used in analytical science for quite some time now. They are instrumental in data analysis, for instance, as mass spectrometers become more advanced and data more abundant. Machine learning algorithms assist with data interpretation and can even make predictions, such as forecasting compound properties based on their structure. Retention time predictions also rely on these approaches. Additionally, AI helps automate tedious tasks like peak identification, integration, and tracking, to assist method development. I believe the integration of AI in decision-making will continue to grow and shape the future of the field.

Another significant accomplishment is the development and increased accessibility of affordable, user-friendly, high-resolution mass spectrometers. These instruments have become more common in laboratories, which is fantastic because it means more people have access to high-resolution techniques and their improved sensitivities. One application I’ve found to be particularly fascinating is their use in real-time measurements, such as in operating rooms, where they’re used to make on-the-spot decisions on whether tissue is cancerous or healthy. 

Lastly, something I personally appreciate as a daily user is the commercialization of two-dimensional liquid chromatography (2D-LC). In the past, this was primarily a technique used in academic labs with systems built in-house. Commercial availability has made 2D-LC more accessible, and it's now being adopted more and more in industrial settings. This represents a significant step forward in tackling the complexity of modern samples and meeting future analytical challenges. 

What about trends over the past year? Are they a continuation of the ones you mentioned earlier, or did anything new emerge?
 

Something that stood out to me at conferences in 2024 were the numerous talks on oligonucleotides. These therapeutics are drawing a lot of attention due to the challenges they present in analysis, so it makes sense that many groups are focusing on them. 

Another trend I’ve noticed, although not exactly new, has been a renewed interest in green chemistry. It’s been around for quite a while, but possibly due to the global emphasis on Sustainable Development Goals, it seems to be regaining momentum. What’s particularly interesting is that it’s not just about using greener, renewable, or less toxic solvents anymore. There’s also a push to make analysis more efficient; by streamlining workflows, reducing solvent consumption, and embracing automation. I think automation using machine learning and artificial intelligence is playing a big role here, allowing us to move away from the endless “trial-and-error” approaches in favour of more efficient method development. Miniaturization and maximizing the number of analyses performed in a single run are also contributing to reducing environmental impact.

Finally, as I mentioned earlier the increased adoption of machine learning in research has been noticeable – not just in publications but also in conference presentations. It’s exciting to see how this technology is being embraced by so many groups and applied in innovative ways.

Looking to the future, what would you say are the biggest challenges facing the field? Are there any in particular you’d highlight?
 

One area I often discuss is complex samples, which I think represent significant challenges across various fields. In health and clinical settings, we deal with biological samples and drug development, which are inherently complex. Similarly in environmental sciences, there’s a lot of focus on detecting, identifying, and treating contaminants in the environment, with these efforts also involving highly complex samples.

To truly understand what’s happening in these areas and drive progress, we need to be able to analyze these samples efficiently and quickly. This requires the development of better methods – high-resolution techniques that can deliver results in shorter time frames. At present, method development is still taking too much time, so we need to find ways to streamline and optimize this process. Improving the speed and efficiency of analysis will be a major focus moving forward, as faster, more robust methods will enable us to perform better and more frequent analyses, which is crucial in order to effectively tackle these challenges.

Are there any societal trends you see impacting analytical science?
 

Absolutely. Although analytical sciences are applied in so many fields, I think two major trends stand out. First, the environmental focus is critical – addressing contamination, monitoring pollutants, and understanding their impact is a significant driver. Second, health and the treatment of diseases are equally crucial. There’s a growing demand for techniques that enable earlier, quicker, and more accurate disease detection, as well as more personalized approaches to treatment. This push toward precision medicine is shaping the field.

Both of these areas are driving the need for better, more sensitive, and higher-resolution techniques. What excites me is how these challenges force us to think creatively, to innovate, and to develop new methods that not only meet these demands but also provide broad societal benefits. 

How do you feel analytical science is currently perceived by the wider scientific community?
 

Sometimes, I feel like we’re treated as just a "tool," with people coming to us saying, "Can you quickly analyze this sample for me?" But I believe we, as analytical scientists, need to take more pride in what we do. Our techniques and methods form the foundation of understanding across disciplines.

If you want to solve a problem or make informed decisions, you first need to know what’s there, what’s causing an effect. This makes separation, identification, and quantification so important in making decisions and reaching conclusions. Analytical science isn’t just an add-on; it’s central to the entire process. I believe it will continue to play a crucial role in solving these major challenges and advancing our understanding in the years to come.

In order to shift this perception of analytical scientists as being “mere tools,” what are the main areas to focus on? Could interdisciplinary collaboration help here?
 

Interdisciplinary projects and collaborations are happening more and more. I see it in my own work, and in the types of projects we apply for. On one hand, funding agencies are increasingly encouraging interdisciplinary approaches, often creating special funding schemes for such projects. This makes sense, as addressing big challenges like health, sustainability, and environmental issues require interdisciplinary approaches.

That being said, I think it’s equally important to maintain a strong focus on the basics and ensure there’s still funding for fundamental research. Funding priorities vary by country, but I always try to emphasize the importance of fundamental research in my interdisciplinary projects; I often include a dedicated work package focused on foundational developments, for example. It’s essential to demonstrate that while we’re capable of addressing applied problems, we can achieve even more with time and resources for investigating fundamental aspects, such as hardware improvements or new methodologies.

I think another area we need to focus on is education. It’s not exactly about “adding new tools to the toolbox,” but rather ensuring we train people properly in analytical sciences. Sometimes I feel that students are content with simply pressing the “start” button on an instrument and generating data, without fully understanding how the system works. In our group, we emphasize the importance of understanding the instruments – how they work, how to troubleshoot when they break, and the fundamentals behind their operation. I’m fortunate that my students are enthusiastic about diving into these details, but this focus needs to start early, even at the bachelor’s level. We need to ensure that students grasp the instrumentation, the core concepts and the working mechanisms, as this foundational knowledge equips them to excel in their future careers and adapt to the evolving challenges in the field.

Is there anything else you think needs to happen to make that progress over the next five to ten years?
 

From my perspective, it’s important that we focus on high-resolution techniques. I work primarily with chromatography, so my expertise isn’t in mass spectrometry, but on the chromatographic side there’s still room for innovation. For example, we already use 2D-LC, but we need to integrate and optimize it further. Smarter, more efficient combinations of chromatographic techniques could help us extract even more information from complex samples.

Beyond separation, we need to focus on hyphenated techniques – integrating separation with detection and identification. The goal is to not only separate as much as possible but also to detect and identify everything we can within a sample. However, as these techniques improve and generate more data, we’ll face the challenge of managing and interpreting all of it. Generating efficient algorithms will be essential to make sense of the abundance of data these methods will inevitably produce.

Could you talk about the role of chromatography in achieving these big goals and ambitions?
 

Questions about whether you still need chromatography if you have high-performing mass spectrometers come up often. But I absolutely think chromatography remains crucial, as it addresses limitations that mass spectrometers alone cannot solve. For example, mass spectrometry struggles to differentiate between compounds with identical masses. In these instances, chromatographic separation is essential for resolving these issues.

Chromatography also mitigates problems like matrix effects in mass spectrometry. A better separation leads to cleaner sample introduction, which is especially important when dealing with unknown compounds. This, in turn, strengthens and simplifies identification. I see chromatography and mass spectrometry as complementary tools, with neither replacing the other. Together, they provide the best possible outcomes for analyzing complex samples.

What’s your overall perspective on the future of the field?
 

I’m very positive about the future. I still wake up inspired by the challenges and opportunities ahead – whether it’s improving hardware, advancing software, or tackling exciting new applications. Every day brings new possibilities, whether in clinical analysis, food safety, or environmental monitoring.

I don’t see the field as fully mature or nearing its limits. There’s so much more to explore and innovate. I’m particularly excited about interdisciplinary projects, where we collaborate with people from other fields to brainstorm, solve problems, and develop new techniques and solutions for societal challenges. These opportunities keep the work fresh and meaningful.

Ask me again in five or ten years, but for now, I remain extremely excited and optimistic about what’s to come!