A dual-column tandem mode was used to establish the fingerprints of Sarcandrae herb from different origins, and their chemical compositions were characterized by UPLC-Q-TOF-MS/MS, which provided an experimental basis for the establishment of a rapid and efficient method for the overall quality control of Sarcandrae herba. For the first time, nine common components were identified from the Sarcandrae herba herbs of 24 origins, which were neochlorogenic acid, chlorogenic acid, 4-caffeoylquinic acid, eleutheroside B1, quercetin-3-O-β-D-glucuronide, neoastilbin, astilbin, isofraxidin, and rosmarinic acid, respectively. A total of 92 compounds were identified by liquid mass spectrometry. The quality of the Sarcandrae herb from 24 origins was analyzed by similarity evaluation, principal component analysis, and cluster analysis, and the chemical components of Sarcandrae herba were identified by UPLC-Q-TOF-MS/MS. The results showed that the overall analysis based on fingerprinting and mass spectrometry could differentiate the origins of the herbs.

Electronic nose (E-nose) combined with gas chromatography–mass spectrometry (GC-MS) was used to analyze the volatile components of silver wormwood from different habitats, and the antibacterial activity of essential oils was also studied, to provide a scientific basis for quality control of silver wormwood and rational utilization of their essential oils. In this study, the total content of essential oils in silver wormwood was determined by steam distillation; the volatile components were conducted in an overall analysis by E-nose combined with chemometrics; the volatile components were analyzed and identified by GC-MS; and two G-negative bacteria and one Gram-positive bacteria were used as test bacteria to determine the antibacterial activity of the essential oils from silver wormwood. The results showed that principal component analysis (PCA) and linear discriminant analysis (LDA) of E-nose could distinguish the essential oils of silver wormwood from different habitats, and the odor difference of essential oils was obvious. A total of 87 volatile components were identified by GC-MS, and there were significant differences in components and contents in silver wormwood from different habitats; PCA and hierarchical cluster analysis (HCA) could effectively distinguish silver wormwood from different habitats. The essential oils from silver wormwood from different habitats all had a certain inhibitory effect on Bacillus subtilis, Staphylococcus aureus, and Escherichia coli. Therefore, the combination of E-nose and GC-MS could quickly distinguish silver wormwood from different habitats and provide a reference for quality control, drug selection, and comprehensive utilization of silver wormwood.

Background: Tablet manufacturing is challenged by strong dynamic coupling of process parameters, significant material property fluctuations, and delayed quality control, with tablet weight stability being particularly critical in high-speed production. Traditional static optimization methods relying on empirical judgment struggle to manage these dynamics, leading to substantial variations in tablet weight and hardness that severely compromise production efficiency. Methods: This study proposes a data-driven closed-loop control system centered on a novel Iterative Learning Model Predictive Control (IL-MPC) architecture. The core innovation lies in directly integrating iterative learning constraints within the MPC optimization framework. This constraint-embedding mechanism enables systematic utilization of historical batch data while preserving real-time optimization capabilities. The IL-MPC approach achieves enhanced batch-to-batch performance consistency with reduced computational burden, effectively combining the dual advantages of learning and optimization. Results: Simulation experiments and industrial production data validate the practical feasibility of the IL-MPC algorithm. Implementation results demonstrate that the proposed system effectively manages dynamic process variations, significantly improving control precision for both tablet weight and hardness, outperforming conventional control methods. Conclusions: This research breaks through the technical bottleneck of dynamic regulation in tablet manufacturing. The developed IL-MPC framework provides a reproducible closed-loop control paradigm for intelligent pharmaceutical manufacturing, promoting the industry’s transformation toward data-driven models and advancing intelligent drug production.