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Optimization of a high‐performance liquid chromatography system by artificial neural networks for separation and determination of antioxidants
Author(s) -
Wang Huaiwen,
Liu Weimin
Publication year - 2004
Publication title -
journal of separation science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.72
H-Index - 102
eISSN - 1615-9314
pISSN - 1615-9306
DOI - 10.1002/jssc.200401719
Subject(s) - separation (statistics) , chromatography , artificial neural network , high performance liquid chromatography , chemistry , artificial intelligence , computer science , machine learning
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).