Application of Genetic Algorithm and Annealing Neural Network to Optimize Separation Conditions for Eight Kinds of Water-soluble Vitamins by HPLC

Abstract

Abstract: Using the uniform test designs method, with the initial concentration of CH3 OH in the linear gradient and the slope of linear gradient as optimization parameters, the separation conditions for eight kinds of water-soluble vitamins were optimized, which are VC, VB1, VB2, VB6, folic acid, VB12, nicotinic acid and nicotinamide. Based on genetic algorithm and annealing artificial neural network, the predication model of efficient separation was established. Verification experiments were carried out with optimized separation conditions predicted by the model and the results are satisfying. Combination of the genetic algorithm and annealing neural network is available for the optimization of separation conditions of HPLC.

Key words: genetic algorithm, annealing neural network, water-soluble vitamin, optimization of gradient separation condition

CHEN Chang-Yun, LI Xiao-Hua, MA Mei-Hua, SHAO Yang, YU Rong. Application of Genetic Algorithm and Annealing Neural Network to Optimize Separation Conditions for Eight Kinds of Water-soluble Vitamins by HPLC[J]. FOOD SCIENCE, 2008, 29(7): 318-321 https://doi.org/

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Application of Genetic Algorithm and Annealing Neural Network to Optimize Separation Conditions for Eight Kinds of Water-soluble Vitamins by HPLC

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