As multidimensional chromatography continues to expand its reach across complex analytical challenges, the precision of data interpretation increasingly depends on what happens after separation. In comprehensive two-dimensional chromatography (2D-LC and 2D-GC), even subtle differences in how peaks appear, how wide they are, how they tail, or how they overlap, can determine whether an analyte is detected accurately or missed entirely.
A new study by Nino Milani, Nard Schellekens, Alan Garcia Cicourel, Rob Edam, Gabriel Vivo Truyols, Bob Pirok, and Tijmen Bos, published in Analytica Chimica Acta (2025), takes on this fundamental question: how do peak characteristics affect the performance of peak detection algorithms in 2D chromatography?
Simulating 700,000 Chromatograms to Understand Detection
Peak detection lies at the heart of chromatographic data processing. It converts raw signals into measurable information, retention times, areas, and ultimately, quantitative results. In 2D chromatography, where each measurement spans two retention-time axes with vastly different information densities, this process becomes far more complex.
Milani and colleagues developed a data simulator capable of generating highly realistic chromatograms derived from experimental probability distributions. By systematically varying peak properties, such as width, asymmetry, shape, relative intensity, and modulation shifts, they created an extensive virtual dataset encompassing over a terabyte of data.
Two distinct detection algorithms were then evaluated:
- The two-step algorithm, which detects peaks in the second-dimension traces and subsequently groups them across modulations.
- The watershed algorithm, which treats the chromatogram as a 2D landscape, detecting peaks through image segmentation.
Revealing the Subtle Effects of Peak Properties
Through this systematic approach, the researchers could visualize the recovery of true peak areas across thousands of configurations. The results were expressed as heatmaps, each pixel representing how accurately a simulated peak was detected under a given scenario.
Several trends emerged:
- Peak width strongly affected both algorithms. As peaks became broader, recovery decreased, especially for the two-step approach, due to modulation “exclusion,” where low-height modulations fell below detection thresholds.
- Modulation shifts, common in comprehensive GC×GC and shifting-gradient LC×LC, proved particularly disruptive. Both algorithms struggled when retention times drifted between modulations, with the watershed method especially prone to splitting peaks.
- Asymmetry and shape (tailing or fronting) affected both methods modestly, although the watershed algorithm’s topographical nature made it more sensitive to skewed peaks.
- Peak ratios (relative intensities) influenced results through thresholding effects, small peaks beside large ones were often underestimated or missed entirely.
No Perfect Algorithm—But Better Understanding
Perhaps the most insightful conclusion was that there is no universally best peak detection strategy. Instead, the choice depends on the chromatographic context:
- The watershed algorithm is advantageous for broader, more symmetrical peaks, typical of polymer or group-type analyses.
- The two-step algorithm performs better for sharper, complex chromatograms where peaks are closely spaced and moderately deformed, such as in natural product or petroleum analyses.
The study also underscores how pre-processing choices, such as interpolation or threshold settings, can profoundly shape results, sometimes more than the algorithm itself.
Towards More Reliable Data Interpretation
By disentangling the relationships between signal characteristics and detection performance, this work provides a quantitative foundation for algorithm selection and design. The methodology, openly shared through the GitHub repository CAST-Amsterdam/PeakDetectionBenchmark, offers a new benchmark for evaluating detection strategies objectively and reproducibly.
Ultimately, Milani et al. remind us that in advanced chromatography, understanding the data is as critical as generating it. As automation and machine learning increasingly drive method development, robust and transparent detection remains the cornerstone of analytical reliability.
Citation
Milani, N.B.L., Schellekens, N.C.A., Garcia Cicourel, A.R., Edam, R., Vivo Truyols, G., Pirok, B.W.J., & Bos, T.S. (2025). Evaluation of the relationship between peak characteristics and detection performance in two-dimensional chromatographic data. Analytica Chimica Acta, 1377, 344634. https://doi.org/10.1016/j.aca.2025.344634
