Title: BARCHAN: Blob Alignment for Robust CHromatographic ANalysis,
Abstract: 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.