[A GA-BP neural network model based on spectrum-effect relationship for assessing spectrum-effect score and quality
Haiyan Yan1, Heng Wang1,2, Chuncai Zou1
1School of Pharmacy, Wannan Medical College, Wuhu 241002 , China.
Objectives:
To construct a GA-BP neural network model based on the spectrum-effect relationship of Cassia seeds extract and test its performance for quality control of Cassia seeds using spectrum-effect score.
Methods:
The HPLC fingerprints of Cassia seeds extract (0.1, 0.2, and 0.4 g/mL) were established. In a mouse model of 5-Fu-induced liver injury treated with 0.4, 0.8, and 1.6 g/kg of Cassia seeds extract, the pharmacodynamics parameters were measured to calculate the comprehensive efficacy using AHP-EWM. A GA-BP neural network model between the fingerprints and comprehensive efficacy was constructed, and the corresponding predicted comprehensive efficacy was obtained. The spectrum-effect relationship between the fingerprints and the measured and predicted comprehensive efficacy was established using grey correlation method followed by Gaussian fitting analysis. The spectral efficiency score was calculated using the relative peak area of the fingerprints and the correlation degree of the spectral efficiency. The reliability of the data was tested using the Z-ratio score method. The limit range of the spectral efficiency score was determined and the quality of the verification samples was evaluated.
Results:
The error between the predicted value using the GA-BP neural network model and the measured value of the comprehensive efficacy was less than 0.2. Gaussian fitting analysis showed good fitting between the spectrum-effect relationship data of the measured and predicted comprehensive efficacy. The limit of the spectral efficiency score was 6.16-7.30. The prediction results for each verification group were consistent with the experimental results and within the limit of spectral efficiency score, and the results of Z-ratio score analysis demonstrated good data reliability.
Conclusions:
The GA-BP neural network model can effectively predict the comprehensive efficacy of Cassia seeds extract, and the established spectrum-effect scoring method can be used for quality evaluation of samples.
