Recognition of spikes of Schizonepeta tenuifolia from different area based on backpropagation neural network coupled with dimension reduction of principal component analysis.
- Author:
Weifeng YAO
1
;
Linlin CAO
;
Mingqiu SHAN
;
Li ZHANG
;
Anwei DING
Author Information
- Publication Type:Journal Article
- MeSH: China; Consumer Product Safety; Lamiaceae; chemistry; Neural Networks (Computer); Principal Component Analysis; Quality Control; Spectrophotometry, Ultraviolet; methods
- From: China Journal of Chinese Materia Medica 2010;35(14):1815-1817
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVEThe spikes of Schizonepeta tenuifolia from different habits were predicted by UV-Vis spectrum.
METHODThe 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.
RESULTThe recognition rate of backpropagation neural network coupled with principal component analysis (PCA-BPNN) was 100%, and its mean square error was 0.001 0.
CONCLUSIONPCA-BPNN can be used for the classifying of spikes of S. tenuifolia from different producing area, and this method is simple and fast.
