Chemometrics optimization of carbohydrate separations in six food matrices by micellar electrokinetic chromatography with anionic surfactant.

ABSTRACT

Multivariate statistical design modeling and the Derringer-Suich desirability function analysis were applied to micellar electrokinetic chromatography (MEKC) results with anionic surfactant to separate carbohydrates (CHOs) in different food matrices. This strategy has been studied with success to analyze compounds of difficult separation, but has not been explored for carbohydrates. Six procedures for the analysis of different sets of CHOs present in six food matrices were developed. The effects of pH, electrolyte and surfactant concentrations on the separation of the compounds were investigated using a central composite design requiring 17 experiments. The simultaneous optimization of the responses for separation of six sets of CHOs was performed employing empirical models for prediction of optimal resolution conditions in six matrices, condensed milk, orange juices, rice bran, red wine, roasted and ground coffee and breakfast cereal samples. The results indicate good separation for the samples, with appropriate detectability and selectivity, short analysis time, low reagent cost and little waste generation, demonstrating that the proposed technique is a viable alternative for carbohydrate analysis in foods.