Unlocking Objective Numerical Evaluation of data analysis strategies: A Novel Platform to Generate Highly Realistic LC×LC and GC×GC data

Significance of the Topic

Objective and reproducible evaluation of data analysis workflows is critical in comprehensive two-dimensional chromatography. Traditional approaches rely on either purely experimental datasets, which lack a standardized ground truth for comparison, or generic simulated data, which may fail to capture realistic sample complexity. Bridging this gap enables method developers and end users to benchmark algorithms under conditions that reflect true chromatographic behavior while maintaining quantitative control over performance metrics.

Study Objectives and Overview

This work presents a novel simulation platform designed to generate highly realistic LC×LC and GC×GC chromatograms. The primary goals are to establish a numerical framework for objective comparison of peak detection strategies and to preserve the nuanced features of experimental chromatograms. By parameterizing key peak characteristics and modulation effects, the platform produces synthetic datasets with known ground truth, facilitating accurate recovery assessments.

Methodology and Instrumentation

The simulation engine models individual chromatographic peaks using a skewed Lorentz–Normal function. Key variables include peak height, width (in both first and second dimensions), skewness, and modulation shifts between injections. A fixed interference peak and a movable target peak are placed on a 100×100 grid to emulate two-dimensional retention behavior. By systematically varying parameters at multiple stages of the chromatogram, the platform generates 70 distinct representations per dataset.

Tested parameters include:

• One- and two-dimensional peak widths
• Peak area ratio relative to an interference peak
• Retention time shifts due to modulation timing
• Peak shape and asymmetry

Data analysis algorithms evaluated comprise:

• Two-step approach: one-dimensional peak finding followed by clustering to reconstruct two-dimensional peaks
• Watershed segmentation for direct two-dimensional peak delineation

Main Results and Discussion

Simulations reveal how peak asymmetry and modulation timing critically affect area recovery. In two-step detection, strong skewness leads to systematic under- or overestimation of peak areas, as indicated by recovery maps where deviations exceed ±1%. Impact of modulation shifts is visualized through heat-map representations showing regions of optimal and degraded performance. Watershed segmentation shows improved robustness against minor asymmetries but may fail when peaks overlap heavily.

Practical Implications

By aligning simulated conditions with experimental parameter distributions, users can pre-evaluate their data processing pipelines and select optimal parameter settings. This platform highlights common pitfalls—such as bias introduced by peak skew—and guides practitioners in choosing between detection strategies based on specific sample characteristics. It also supports quality control by defining acceptable recovery thresholds under realistic chromatographic scenarios.

Future Trends and Opportunities

The simulation framework will be extended to incorporate mass spectrometric dimension, enabling objective comparison of peak deconvolution and compound identification algorithms in hyphenated techniques. Integration of machine learning-based peak classifiers trained on these realistic datasets promises further improvements in robustness and throughput. Community engagement through an open-access web portal is planned to crowd-source algorithm benchmarking and continuous dataset expansion.

Conclusion

This study introduces a versatile, parameter-driven platform for generating realistic two-dimensional chromatography data with known ground truth. It enables objective numerical evaluation of peak detection methods, uncovers algorithm-specific sensitivities, and assists users in making informed choices tailored to their analytical context.

References

• Niezen L et al Analytica Chimica Acta 1201 2022 339605
• Milani N et al Analytica Chimica Acta 1312 2024 342724