Application of chemical pattern recognition to quality control of Radix Paeoniae Alba / 中草药

ABSTRACT

Objective With generalization and steadiness,a new evaluation model by Integrating Non Linear Features extraction algorithm with artificial neural networks(ANN) used for pattern recognition of quality control of Radix Paeoniae Alba was proposed in this paper.Methods The HPLC data from 29 samples with different quality were proceeded with nonlinear kernel principal component analysis(KPCA) and an improved Back propagation algorithm of ANN.The extract characteristics was fed into BP neural networks as input elements for pattern recognition.In the meantime,the processing data,the optimal numbers of hidden layers,the numbers of hidden nodes,excitation functions,and over-fitting,etc. were discussed wholly so that standardization networks was designed without jamming.Results As recognition ratio was 100%,the pattern recognition of quality control of Radix Paeoniae Alba was established successfully by trained networks and predicted results.Conclusion Integrating KPCA algorithm with ANN for pattern recognition of quality control of Radix Paeoniae Alba has been proved to be available.