FOOD SCIENCE ›› 2007, Vol. 28 ›› Issue (5): 77-80.

Previous Articles     Next Articles

Modeling of HPLC Analysis of Food Additives with Neural Network

 TANG  Ming-Xiang, CHEN  Hai-Yuan, YANG  Gong-Ming, LI  Kai-Xiong   

  1. 1.College of Food Science,Shihezi University,Shihezi 832003,China; 2.College of Food Science and Engineering,Northwest Agriculture and Forest University,Yangling 712100,China; 3.Station of Quality and Measurement Inspection of Akesu Region,Akesu 843000,China; 4.College of Food Science,South China Agricultural University,Guangzhou 510642,China
  • Online:2007-05-15 Published:2011-12-31

Abstract: An artificial neural network(ANN)model is constructed to predict the retention time,based on the high performance liquid chromatographic(HPLC)analysis data of seven food additives.The model is created in the structure of feed-forward network with the algorithra of back propagation.It is a double-layer topological network,including one hidden layer.The optimum node number of hidden layer is determined.With these parameter values,the model can be trained accurately,and the training speed are kept in an appropriate range.The network redundancy and the drop of constringency speed are avoided.The simulation results shows that the network can be trained stably by basic BP algorithm,and the predicting results are well in accordance with experimental data.

Key words: artificial neural network (ANN), food additives, high performance liquid chromatographic (HPLC), modeling