Research on the Quantitative Analysis of Near Infrared Spectroscopy of Oleanolic Acid of Clematis Root Based on Artificial Neural Network and Wavelet Transform

The traditional Chinese medicine must be carried on the separation and withdrawing by use of the conventional analysis methods. With rapidly analysis, no pollution, no damage, simple operation, low analysis cost, environmental protection, and many other advantages, the Near Infrared (NIR) Spectroscopy analysis has made breakthrough progress in the Chinese medicine field. In this paper, using the Fourier transform near infrared diffuse reflectance spectrometer for transmittance detection for two kinds of Clematis Root samples, and the wavelet transform (WT) method is adopted, the compression of the spectral variables, the compression ratio can reach 99.14%. The quantitative analysis of NIR of Oleanolic Acid of Clematis Root is carried on, based on artificial neural network (ANN) and wavelet transform. The simulation results show that, the prediction decision coefficient (R2 is 0.9876 the average relative error (ARE) is 0.0278 the root mean square error of Cross-Validation (RMSECV) is 0.0191 in the Clematis Root extract samples the ratio of material to liquid 1:2), and the predictive decision coefficient is 0.9904, the average relative error is 0.0191, and the root mean square error of Cross-Validation is 0.0125 in the Clematis Root extract samples (the ratio of material to liquid 1:5). The two models can meet the need of practical application and provide technical support for quantitative analysis to extract of Clematis Root and analysis of NIR in traditional Chinese medicinal materials.