This year has been rich in separation science conferences, and multidimensional chromatography has been one of the big hits. From the Multidimensional Chromatography Workshop to the International Symposium on Chromatography, including the highly successful return of Riva, multidimensional chromatography, and especially GC×GC, has shown its maturity and attractiveness in many fields.
Over the past three months, The Analytical Scientist’s GC×GC Detective Stories have portrayed the status of multidimensional gas chromatography. They show that, building on a rich history of impactful research, the global quest for separation resolution has turned the technique into a “must have” in many analytical laboratories.
Every good detective story ends with the investigator gathering everyone in one room and laying out the evidence. It is time to do the same for GC×GC: to review what the series has established, to name the bottlenecks that remain, and to ask the questions that remain open. In short, it is time to close the case by presenting the final arguments.
A technique going across fields and across generations
The GC×GC Detective Stories brought together the vision of different generations of leaders in the field.
The first generation built the instrument itself. Their stories are about hardware development: modulators that could reliably trap and reinject narrow fractions of the first-dimension effluent, column combinations offering complementary selectivity, and detectors fast enough to capture peaks only a few hundred milliseconds wide. Just as important were the essential proof-of-concept studies that demonstrated the exceptional capabilities of the technique. Ordered patterns of chemical families in the separation space, the resolution of thousands of compounds in a single run, and the detection of trace constituents previously buried under matrix peaks convinced a cautious community that comprehensive two-dimensional separation was more than an academic curiosity.
The next generation took these tools out of the specialist laboratory. Petroleum and fuels, where sample complexity made GC×GC almost indispensable, were followed by environmental monitoring, food, flavor and fragrance, forensics and, more recently, metabolomics, exposomics and breath analysis. With each new field came new questions, and the technique adapted: flow modulation broadened access by removing the need for cryogens, fast-acquisition mass spectrometry became routine, and software began to turn raw data into chemically meaningful information.
The youngest generation, finally, is growing up with GC×GC as a standard tool rather than an exotic one. For them, the question is no longer whether the technique works, but how best to use it: how to design studies, how to process large data sets, and how to translate chemical fingerprints into answers that matter outside the analytical community. Read as a whole, what stands out in the series is the continuity between these generations. The same curiosity that drove the first modulator designs now drives the search for robust data-processing workflows, and each wave of practitioners has inherited, and extended, the tools of the one before.
A technique that all detectives want on their side
If one theme runs through every case in the series, it is GC×GC’s ability to reveal what one-dimensional separations miss. Whether the investigation concerned a contaminated food product, a disease signature in exhaled breath or the origin of a petroleum sample, the critical evidence was often a minor compound co-eluting with something far more abundant – invisible until a second dimension pulled the two apart.
From pictures to decisions
GC×GC’s 2D plot is iconic for good reason: structured patterns of homologous series and chemical classes make complex samples readable at a glance. But the plot matters less for its beauty than for the interpretation it enables. For example, in process monitoring, it shows how a feedstock is transformed or how a product degrades over time; in clinical research, it supports the search for diagnostic patterns of volatile compounds; and in regulatory settings, it provides the evidence needed to demonstrate compliance or to flag a non-conforming sample. In each case, the value lies in the decision that follows the picture.
The bottleneck has moved
In the early days of GC×GC, the limiting factor was the instrument. Today, commercial systems are robust, and modulators and detectors are no longer the main obstacle. The bottleneck has moved to operator training, data handling, and standardization. Operating a GC×GC system well, choosing the right column set and modulation conditions, and then making sense of files containing thousands of features requires skills that are not yet part of most analytical curricula. Data processing in particular remains time-consuming and operator-dependent, and the lack of common formats and procedures makes it difficult to compare results across laboratories or software packages. This is where investment is likely to have the largest impact in the coming years.
The good news is that these are problems the community knows how to address. Hands-on courses, shared tutorials and open data sets lower the entry barrier for new users, while vendor-independent processing tools and agreed file formats would make results easier to share and reproduce. Detectives rarely work alone, and neither should GC×GC users: the most convincing cases in the series were built by teams combining instrumental expertise, chemometrics and deep knowledge of the application.
A technique that is still aiming for its full potential
The series also helps define what GC×GC is, and what it is not.
The core value is unmasking co-elution
Every case in the series could be summarized the same way: the answer was hidden under a bigger peak. This is the fundamental strength of comprehensive two-dimensional separation, and the reason it keeps finding new applications. Whenever a sample is complex enough that co-elution compromises identification or quantitation, the second dimension offers a way out.
Fit-for-purpose thinking
A second dimension of separation isn’t always required. Many routine analyses are perfectly served by a well-optimized one-dimensional method, and choosing the more powerful technique by default adds cost, complexity and data burden without adding information. The most successful users start from the analytical question, not from the instrument. That discipline will be essential if the technique is to be adopted beyond its current strongholds.
In practice, this means asking early whether the problem really requires a second dimension, which compounds or classes must be resolved, and how the data will be interpreted once acquired. Answering these questions before the first injection saves time, keeps data sets manageable and makes the added value of GC×GC easier to demonstrate to colleagues, managers and reviewers.
Read critically, the stories also reveal several gaps, and these point to the questions that remain open.
Quantitation and harmonization
The series said little about quantitation, validation or harmonization between laboratories. Yet these are precisely what regulators, clinicians and industrial quality managers need before they can rely on a method. Interlaboratory studies, reference materials and agreed reporting practices for GC×GC are still rare, and closing this gap would do more for the credibility of the technique than any new modulator.
Artificial intelligence
AI is mentioned as an aspiration, with no demonstrated results. Machine learning is clearly well suited to the rich, structured data that GC×GC produces, and expectations are high. But the series offered no case in which AI had already changed the outcome of an investigation. Demonstrating one, with transparent methods and validation on independent samples, remains an open challenge for the community.
Looking forward
Most success stories look back at past cases. This is natural for a series built around stories, but it leaves the future less clearly drawn. Which questions will GC×GC answer in five or ten years? Which fields are still waiting for it? A more forward-looking agenda would help newcomers see where their contribution could make a difference.
A broader industrial voice
The industrial perspective came only from food and fragrance companies. These sectors are important early adopters, but they are not the only ones. The experience of petrochemical, pharmaceutical, chemical and environmental testing laboratories, where GC×GC is also used, would give a fuller picture of how the technique performs under routine constraints such as throughput, cost and regulatory scrutiny.
Closing the case
So, can the case be closed? On the evidence presented, the verdict is clear: GC×GC has proven its value. It has grown from a laboratory curiosity into a mature technique used across fields and generations, and it has repeatedly uncovered evidence that would otherwise have remained hidden. The remaining challenges are no longer about whether the technique works, but about how it is taught, how its data are processed and shared, and how its results are validated and trusted.
In the best detective tradition, closing one case often opens the next. For GC×GC, the next investigation will be less about separation power and more about people, data and standards. That may well prove to be the most interesting story yet.
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