In recent years, food analysis has moved beyond largely targeted measurements toward more comprehensive, data-intensive approaches. Advances in chromatographic performance, high-resolution mass spectrometry, and data-driven interpretation are enabling deeper insight into complex food matrices, while growing emphasis on sustainability, standardization, and reproducibility is reshaping how analytical power is applied.
In this interview, Francesco Cacciola – Full Professor of Food Chemistry, University of Messina, Italy, whose work focuses on advanced methods for characterizing bioactive compounds in foods and natural products – reflects on these shifts, the challenges they present, and where the field is heading next.
What have been the key developments, tools, or turning points that have shaped food analysis in recent years?
Over the past decade, the biggest turning point has been the integration of high-resolution mass spectrometry with advanced chromatographic separations. Combining targeted and untargeted approaches has allowed us to explore the food metabolome in unprecedented detail, with the ability to discover and quantify new bioactive molecules.
Another important shift has been the move toward green analytical chemistry. This includes miniaturized sample preparation, solvent-free extraction techniques, and the use of versatile, non-toxic solvents – such as propylene carbonate or dimethyl carbonate – in high-performance liquid chromatography (HPLC). Together, these advances are making food analysis faster, cleaner, and more sustainable.
The third – and perhaps most striking – development is the rise of data-driven analysis. Chemometrics, machine learning, and AI-assisted workflows are increasingly essential for interpreting the complex datasets generated by modern food analysis.
Tell us about the focus of your current work in this area?
My group’s current focus is on developing advanced liquid, gas, and supercritical chromatography techniques to characterize bioactive compounds in foods and natural products. In parallel, some colleagues have successfully exploited MS-based technologies – such as the iKnife – for detecting food fraud.
At the moment, I’m particularly interested in miniaturized comprehensive two-dimensional LC coupled with mass spectrometry. This approach has the potential to significantly increase sensitivity while also supporting a greener analytical strategy through reduced solvent and sample consumption.
We’ve also been exploring the use of quantitative structure-retention relationship (QSRR) modeling – essentially merging analytical chemistry with computational modeling – to better understand and predict chromatographic behavior.
What techniques or innovations are you most excited about right now?
As previously mentioned, I’m very excited about the potential of comprehensive two-dimensional liquid chromatography – which, over the past decade, has migrated from being niche and complex to much more user-friendly and robust. When combined with high-resolution mass spectrometry, one can generate a “molecular map” of food matrices that was considered unimaginable just a few years ago.
On the data side, I am fascinated by chemometric modeling and AI-assisted feature recognition. These tools help us extract meaningful patterns from enormous datasets, and can even predict compound behavior before running an experiment. This represents a powerful bridge between experimental chemistry and data science.
What are the main challenges facing food analysis today?
One persistent challenge is sample complexity: foods and natural extracts can contain thousands of chemically diverse compounds spanning wide concentration ranges. As a result, achieving both comprehensive coverage and quantitative accuracy remains difficult.
Another major challenge is standardization. While we currently possess great analytical capabilities, comparing results across laboratories or studies is still quite tricky. With this in mind, we have recently cooperated with Davy Guillarme and his team from the University of Geneva to develop an excel-based tool, to help users understand the fundamentals of comprehensive two-dimensional LC.
As we move toward big-data-driven analysis, our aim should not be to simply generate beautiful chromatograms, but to also ensure data quality, reproducibility, and interpretability – to aid future scientific collaboration.
How are recent advances in analytical technologies changing the way we study and ensure food quality and safety?
Advances in data science are already having a profound impact on the field. We’ve moved from interpreting chromatograms largely “by eye” to using multivariate pattern recognition and machine learning models that can detect subtle quality signatures invisible to human interpretation. At the same time, increased automation is allowing researchers to handle much larger sample volumes, from robotic sample preparation through to real-time quality monitoring.
Moreover, omics technologies – metabolomics in particular – are helping us link the chemical fingerprint of a food to its biological and nutritional function. This is moving the field toward a more holistic, and increasingly predictive, understanding of food quality and safety.
In your view, what impact could these developments have on society?
The societal impact is huge. With more powerful and sustainable analytical tools, we can reduce food waste, improve the detection of adulteration, and enable more reliable authentication of origin and quality.
From a nutritional perspective, these technologies move us closer toward fully personalized food profiling, where the composition of what we eat can be more directly linked to real health outcomes. And in terms of sustainability, greener sample preparation and miniaturized methods offer a clear route to reducing the environmental footprint of analytical laboratories.
Looking to the next 5 to 10 years, where do you see the field heading?
I think the next decade will bring a convergence between chromatography, high-resolution mass spectrometry, and artificial intelligence – resulting in truly “smart” analytical platforms that can self-optimize and learn from data. I would also like to see breakthroughs in real-time food analysis – for example, compact, intelligent sensors or portable LC-MS systems capable of delivering laboratory-quality data directly on the production line or in the field.
And perhaps most importantly, by integrating chemical data with biological and sensory outcomes, I think we will move from measuring food composition to truly understanding it. That, to me, is where the real future of food chemistry lies: making analysis not just more powerful, but more meaningful.
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