Optimization of a high-performance liquid chromatography system by artificial neural networks for separation and determination of antioxidants

A high-performance liquid chromatography (HPLC) system was used to determine the antioxidants tert-butyl-hydroquinone (TBHQ), tert-butylhydroxyanisole (BHA), and 3,5-di-tert-butylhydroxytoluene (BHT) simultaneously in oils. The paper presents a new methodology for the optimized separation of antioxidants in oils based on the coupling of experimental design and artificial neural networks. The orthogonal design and the artificial neural networks with extended delta-bar-delta (EDBD) learning algorithm were employed to design the experiments and optimize the variables. The response function (Rf) used was a weighted linear combination of two variables related to separation efficiency and retention time, according to which the optimized conditions were obtained. The above-mentioned antioxidants in rapeseed oils were separated and determined simultaneously under optimized conditions by HPLC with UV detection at 280 nm. Linearity was obtained over the range of 10-200 μg/mL with recoveries of 98.3% (TBHQ), 98.1% (BHT), and 96.2% (BHA).