OBJECTIVE To efficiently process data, uncover implicit production patterns, and guide production in the evolution of botanical drug from digital to intelligent pharmaceuticals remains a pivotal research area in botanical drug manufacturing.

METHODS This study address challenges such as the high heterogeneity of raw materials and the limited flexibility in adjusting process parameters, focusing on the pharmaceutical process of compound Dendrobium officinale granules. By employing advanced analytical tools, including Shewhart control charts, Bayesian networks, graph attention networks, graph convolutional neural networks, and genetic algorithms, an innovative raw material-process collaborative control strategy was developed and implemented.

RESULTS This strategy markedly enhanced the process performance of four key indicators: moisture, ash content, crude polysaccharides, and total saponins. By establishing feasible intervals for raw material quality attributes, the process performance indices( P_pk ) of the four key indicators all reached the six-sigma level. Among them, the P_pk of moisture, ash content, and crude polysaccharides was significantly enhanced(moisture: 1.847 to 2.667; ash content: 5.214 to 7.111; crude polysaccharides: 1.889 to 2.192). Subsequent investigations revealed that the implementation of a feedforward control strategy, based on raw material attributes and applied to specific test set batches, the P_pk of the four key indicators can also reach the six-sigma level, and the P_pk of moisture, ash content, and crude polysaccharides was also significantly improved(moisture: 2.052 to 5.687; ash content: 7.296 to 32.934; crude polysaccharides(expressed as glucose): 2.172 to 4.237).

CONCLUSION The proposed method, by presetting feasible intervals of raw material quality attributes and combining them with a feedforward control strategy for process parameters, can stabilize the critical quality attributes of the final product at the Six Sigma level without requiring any adjustments to the existing process route, demonstrating good engineering applicability.