Objective This study focuses on the Jinyinhua (Lonicerae Japonicae Flos, LJF) concentration process in the production of Reduning Injection (热毒宁注射液, RI), with the objective of developing and validating a data-driven technical framework. The framework is designed to enable objective evaluation, intelligent identification, and process optimization of “golden batches”, while fundamentally elucidating the key process mechanisms and quantitative control strategies influencing their formation. Ultimately, this approach aims to enhance the efficiency and stability of the production process. Methods This study collected production process data for 170 batches of LJF concentrate, and innovatively established “comprehensive concentration efficiency ratio” (E) as an evaluation index for process performance. A latent-space guided adaptive performance thresholding (LGAPT) strategy was employed to objectively classify the production batches into golden and non-golden categories. Subsequently, a classification prediction model based on extreme gradient boosting (XGBoost) was constructed. By integrating model explanation techniques such as SHAP (Shapley additive explanations) and partial dependence plots (PDP), the key process features affecting batch quality and their optimal control ranges were systematically investigated. Finally, the model findings were statistically verified through non-parametric tests, effect size analysis, and kernel density estimation. Results The study established a threshold of E = 0.136 3 kg/(m³∙min), classifying the 170 batches into 61 golden and 109 non-golden batches. The XGBoost model demonstrated excellent performance in identifying golden batches, achieving an F1-score of 0.84 on the test set. Model interpretation identified features such as LT_std-2, LT_skew-1, and T3_abd-2 as core determinants for golden batch formation and defined their optimal ranges. The statistical validation results were highly consistent with the model interpretations, confirming the reliability of the findings. Conclusion This study established a comprehensive technical framework encompassing performance quantification, intelligent classification, and model-based optimization. By successfully identifying the key process features and their optimal control ranges, this work lays a methodological foundation for the standardization and intelligent control of traditional Chinese medicine (TCM) manufacturing, driving a paradigm shift in process optimization from an empirical-based to a data-driven approach.

Reduning Injection  /  Lonicerae Japonicae Flos  /  concentration  /  data-driven  /  gold batch  /  model interpretation  /  intelligent recognition  /  comprehensive concentration efficiency ratio