Exploiting the synergistic effect of concurrent data signals: Low-level fusion of liquid chromatographic with dual detection data

Abstract

A low-level data fusion strategy was developed and implemented for data processing of second-order liquid chromatographic data with dual detection, i.e. absorbance and fluorescence monitoring. The synergistic effect of coupling individual information provided by two different detectors was evaluated by analyzing the results gathered after the application of a series of data preprocessing steps and chemometric resolution. The chemometric modeling involved data analysis by MCR-ALS, PARAFAC and N-PLS. Their ability to handle the new data block was assessed through the estimation of the analytical figures of merits achieved in the prediction of a validation set containing fifteen fluorescent and non-fluorescent veterinary active ingredients that can be found in poultry litter. Eventually, the feasibility of the application of the fusion strategy to real poultry litter samples containing the studied compounds was verified.