Trilinear decomposition method applied to removal of three-dimensional background drift in comprehensive two-dimensional separation data

A novel technique for removal of three-dimensional background drift in comprehensive two-dimensional (2D) liquid chromatography coupled with diode array detection (LCxLC-DAD) data is proposed. The basic idea is to perform trilinear decomposition on the instrumental response data, which is based on the alternating trilinear decomposition (ATLD) algorithm. In model construction, the background drift is modeled as one component or factor as well as the analytes of interest, hence, the drift is explicitly included into the calibration. The method involves performing trilinear decomposition on the raw data, then extracting the background component and subtracting this background data from the raw data, leaving the analytes' signal on a flat baseline. Simultaneous evaluation of three-dimensional background drift and true signals may improve the quality of the data. This method is applied to the determination and removal of three-dimensional background drifts in simulated multidimensional data as well as experimental comprehensive two-dimensional liquid chromatographic data. It is shown that this technique yield a good removal of background drift, without the need to perform a blank chromatographic run, and required no prior knowledge about the sample composition.