[Recognition of spikes of Schizonepeta tenuifolia from different area based on backpropagation neural network coupled with dimension reduction of principal component analysis]

Zhongguo Zhong Yao Za Zhi. 2010 Jul;35(14):1815-7. doi: 10.4268/cjcmm20101409.
[Article in Chinese]

Abstract

Objective: The spikes of Schizonepeta tenuifolia from different habits were predicted by UV-Vis spectrum.

Method: The dimensions of spectrum data obtained from ten habits were reduced by principal component analysis, and the first six new variables with 99.82% of cumulative reliability were put into the backpropagation neural network for model building.

Result: The recognition rate of backpropagation neural network coupled with principal component analysis (PCA-BPNN) was 100%, and its mean square error was 0.001 0.

Conclusion: PCA-BPNN can be used for the classifying of spikes of S. tenuifolia from different producing area, and this method is simple and fast.

Publication types

  • Evaluation Study

MeSH terms

  • China
  • Consumer Product Safety
  • Lamiaceae / chemistry*
  • Neural Networks, Computer*
  • Principal Component Analysis
  • Quality Control
  • Spectrophotometry, Ultraviolet / methods*