Capillary electrophoresis enhanced by automatic two-way background correction using cubic smoothing splines and multivariate data analysis applied to the characterisation of mixtures of surfactants.

Mixtures of the surfactant classes coconut diethanolamide, cocamido propyl betaine and alkylbenzene sulfonate were separated by capillary electrophoresis in several media containing organic solvents and anionic solvophobic agents. Good resolution between both the surfactant classes and the homologues within the classes was achieved in a BGE containing 80 mM borate buffer of pH 8.5, 20% n-propanol and 40 mM sodium deoxycholate. Full resolution, assistance in peak assignment to the classes (including the recognition of solutes not belonging to the classes), and improvement of the signal-to-noise ratio was achieved by multivariate data analysis of the time-wavelength electropherograms. Cubic smoothing splines were used to develop an algorithm capable of automatically modelling the two-way background, which increased the sensitivity and reliability of the multivariate analysis of the corrected signal. The exclusion of significant signals from the background model was guaranteed by the conservativeness of the criteria used and the safeguards adopted all along the point selection process, where the CSS algorithm supported the addition of new points to the initially reduced background sample. Efficient background modelling made the application of multivariate deconvolution within extensive time windows possible. This increased the probability of finding quality spectra for each solute class by orthogonal projection approach. The concentration profiles of the classes were improved by subsequent application of alternating least squares. The two-way electropherograms were automatically processed, with minimal supervision by the user, in less than 2 min. The procedure was successfully applied to the identification and quantification of the surfactants in household cleaners.