Simulation Study on Color Matching of Traditional Chinese Medicine Compound Placebo Based on Neural Network Optimization Model

  OBJECTIVE  To predict the amount of colorants used in the preparation of placebo of Chinese medicine by constructing particle swarm optimization-back propagation neural network PSO-BPNN compound granules, and to provide a new idea for the simulation of placebo color of Chinese medicine compound granules.

  METHODS  The BP neural network was used to establish the model of sample color parameters L, a^*, b^* and pigment mass fraction. The global search ability of particle swarm optimization algorithm was used to optimize the weight and bias of BP neural network to prevent the local minimum value of the model. The linear reduction weight coefficient method and the introduction of mutation operator were used to improve the global optimization ability of particle swarm optimization algorithm. The color comprehensive evaluation index (ΔE) was used as the objective evaluation standard to verify the test results.

  RESULTS  The training results show that the fitting accuracy of the improved PSO-BP neural network was up to 98.31%. The prediction results show that the prediction error of the improved PSO-BP neural network was the smallest, and the mean absolute percentage error (MAPE), root mean square error (RMSE) and mean color difference (ΔE) were 0.411 5, 2.164 6 and 2.56, respectively. The verification samples of three kinds of particles were prepared for verification. The ΔE of the verification sample and the model drug were 1.73, 2.63 and 4.11, respectively. The color difference between the two groups and the model drug was small by visual evaluation.

  CONCLUSION  The BP neural network based on the improved particle swarm optimization algorithm can simulate the prediction of the amount of colorants used in the preparation of Chinese medicine compound granules, and can be used as a recommended optimization model for placebo color matching research.