BARCHAN: Blob Alignment for Robust CHromatographic ANalysis
This work addresses the need for fast and repeatable analysis of complex chromatograms in analytical chemistry, representing an incremental improvement in automation for GCxGC.
The authors tackled the problem of automating peak area identification in comprehensive two-dimensional gas chromatography (GCxGC) by developing BARCHAN, a chromatogram and template alignment method based on robust peak registration, which significantly reduces analysis time and proves to be fast and reliable on two datasets.
Comprehensive Two dimensional gas chromatography (GCxGC) plays a central role into the elucidation of complex samples. The automation of the identification of peak areas is of prime interest to obtain a fast and repeatable analysis of chromatograms. To determine the concentration of compounds or pseudo-compounds, templates of blobs are defined and superimposed on a reference chromatogram. The templates then need to be modified when different chromatograms are recorded. In this study, we present a chromatogram and template alignment method based on peak registration called BARCHAN. Peaks are identified using a robust mathematical morphology tool. The alignment is performed by a probabilistic estimation of a rigid transformation along the first dimension, and a non-rigid transformation in the second dimension, taking into account noise, outliers and missing peaks in a fully automated way. Resulting aligned chromatograms and masks are presented on two datasets. The proposed algorithm proves to be fast and reliable. It significantly reduces the time to results for GCxGC analysis.