chromatoPy: An Open‐Source Tool for Chromatographic Peak Deconvolution and Integration Applied to Glycerol Dialkyl Glycerol Tetraethers

Abstract Glycerol dialkyl glycerol tetraethers (GDGTs) are valuable proxies for reconstructing past environmental conditions but sources of analytical uncertainty remain unresolved, as highlighted by a recent inter‐laboratory comparison project. Analytical uncertainties owe in part to subjective solutions to manual peak integration, including baseline corrections, peak deconvolution, and signal smoothing. Here, we present chromatoPy, an open‐source Python package implementing a multi‐Gaussian fitting algorithm that automatically models peak co‐elution while requiring user approval of the fitted results, thereby improving peak area integration accuracy, enhancing reproducibility, and reducing the time required for peak processing. We evaluate chromatoPy through inter‐user comparisons and against manual integration using a global data set of GDGTs from marine, lacustrine, and loess sediments, demonstrating high concordance in peak areas and fractional abundances for both branched and isoprenoid GDGTs. Subtle but systematic differences are observed primarily for branched GDGTs prone to co‐elution, which is consistent with the deconvolution approach employed. By reducing analyst subjectivity and providing uncertainty estimates, chromatoPy facilitates more comparable GDGT measurements across laboratories and data sets, thereby strengthening the foundations of GDGT‐based paleoclimate and biogeochemical reconstructions. The package significantly decreases processing time while providing quantitative uncertainty estimates using Monte Carlo error propagation, enabling rapid replicate analyses. chromatoPy thus offers a robust, user‐friendly tool that enhances reproducibility and will ultimately yield more reliable paleoclimate reconstructions.