[Application scenario design and prospect of generative artificial intelligence (AI) in intelligent manufacturing and supply chain of traditional Chinese medicine].

Intelligent manufacturing technologies, including databases, mathematical modeling, and information systems have played a significant role in process control, production management, and supply chain management in traditional Chinese medicine(TCM) industry. However, their ability to process and utilize unstructured data, such as research and development reports, batch production records, quality inspection records, and supplier documents, is relatively weak. For text, images, language, and other unstructured data, generative artificial intelligence(AI) technology has shown strong potential for development in extracting information, extracting knowledge, semantic retrieval, and content generation. Generative AI is expected to provide a feasible set of tools for the utilization of unstructured data resources in the TCM industry. Based on years of research and industrial application experience in TCM intelligent manufacturing technology, this study reviewed the current situation of intelligent manufacturing in TCM and the utilization of unstructured data, analyzed the application value of generative AI in the TCM manufacturing process and supply chain, summarized four typical application scenarios, including intelligent pharmaceutical knowledge base/knowledge graph, intelligent on-the-job trai-ning, intelligent production quality control, and intelligent supply chain. Furthermore, this study also explained the data collection and processing, business process design, application potential, and value of each scenario based on industry demands. Finally, based on the integration of generative AI and TCM industrial models, the study proposed a preliminary concept of a smart industrial brain for TCM, aiming to provide a reference for the application of AI technology in the field of TCM manufacturing.

[Application progress on data-driven technologies in intelligent manufacturing of traditional Chinese medicine extraction].

The application of new-generation information technologies such as big data, the internet of things(IoT), and cloud computing in the traditional Chinese medicine(TCM)manufacturing industry is gradually deepening, driving the intelligent transformation and upgrading of the TCM industry. At the current stage, there are challenges in understanding the extraction process and its mechanisms in TCM. Online detection technology faces difficulties in making breakthroughs, and data throughout the entire production process is scattered, lacking valuable mining and utilization, which significantly hinders the intelligent upgrading of the TCM industry. Applying data-driven technologies in the process of TCM extraction can enhance the understanding of the extraction process, achieve precise control, and effectively improve the quality of TCM products. This article analyzed the technological bottlenecks in the production process of TCM extraction, summarized commonly used data-driven algorithms in the research and production control of extraction processes, and reviewed the progress in the application of data-driven technologies in the following five aspects: mechanism analysis of the extraction process, process development and optimization, online detection, process control, and production management. This article is expected to provide references for optimizing the extraction process and intelligent production of TCM.

[Methodology and application of process analytical technology (PAT) for traditional Chinese medicine manufacturing:a review].

Owing to the advancement in pharmaceutical technology, traditional Chinese medicine industry has seen rapid development. Preferring conventional manufacturing mode, pharmaceutical enterprises of traditional Chinese medicine have no effective process detection tools and process control methods. As a result, the quality of the final products mainly depends on testing and the quality is inconsistent in the same batch. Process analytical technology(PAT) for traditional Chinese medicine manufacturing, as one of the key advanced manufacturing techniques, can break through the bottleneck in quality control of medicine manufacturing, thus improving the production efficiency and product quality and reducing the material and energy consumption. It is applicable to the process control and real-time release of advanced manufacturing modes such as intelligent manufacturing and continuous manufacturing. This paper summarized the general idea of PAT for traditional Chinese medicine manufacturing. Through the analysis of the characteristics and status quo of the technology, we summed up the methodology for the continuous application and improvement of PAT during the whole life-cycle of traditional Chinese medicine. The five key procedures(process understanding, process detection, process modeling, process control, and continuous improvement) were summarized, and the application was reviewed. Finally, we proposed suggestions for the technical and regulatory challenges in implementing PAT in traditional Chinese medicine industry. This paper aims to provide a reference for development and application of PAT in advanced manufacturing, intelligent manufacturing, and continuous manufacturing of traditional Chinese medicine industry.

[Granulation of Yangxue Qingnao Granules in fluidized bed based on near-infrared spectroscopy].

To realize the real-time monitoring of the production process of Yangxue Qingnao Granules and improve the inter-batch consistency of granule quality in the granulation process, this study established a near-infrared quantitative prediction model of moisture, particle size, bulk density, and angle of repose in the fluidized bed granulation process of Yangxue Qingnao Granules based on near-infrared spectroscopy(NIRS). The near-infrared spectra were collected from 355 samples in 12 batches in the granulation process by integrating the sphere detection module of the near-infrared spectrometer. In combination with the pretreatment methods such as the first derivative, multiplicative scatter correction(MSC), and standard normal variate(SNV), the model was established by partial least squares(PLS) regression. The root mean square error of prediction(RMSEP) of moisture was 0.347 and R_P~2 was 0.935. The RMSEP of the D_(50) particle size model was 38.4 and R_P~2 was 0.980. The RMSEPs of bulk density and angle of repose were 0.018 8 and 0.879, with R_P~2 of 0.085 9 and 0.958. The results showed that the prediction of the PLS quantitative model combined with NIRS was accurate, and this model can be applied to the monitoring of key quality attributes in the fluidized bed granulation of Chinese medicinal granules in the production scale.

[Research on feedforward control method of mixing process for compound Danshen Dripping Pills based on quality by design(QbD)].

The mixing process is one of the key operation units for solid preparation of traditional Chinese medicine. The physical properties such as particle size, density and viscosity of the mixture are key factors that need to be controlled, which will directly affect the performance of the preparation molding process and product quality. Subsequent dripping process performance and appearance qua-lity of dripping pills will be affected by dynamic viscosity of materials in the mixing process. Based on this, with mixing process of compound Danshen dripping pills as the object, a feedforward control method for the dripping pill mixing process was established based on the concept of quality by design(QbD). Firstly, critical quality attribute(CQA)-dynamic viscosity, critical material attributes(CMAs)-the moisture content of compound Danshen extract, average molecular weight of polyethylene glycol 6000 and critical process parameter(CPP)-mixing temperature were identified through the analysis of properties for multiple batches of the raw materials and excipients as well as technological mechanism. Then the Box-Behnken experimental design was used to establish the regression model among CMA, CPP and CMA(R■=0.972 0, RMSE =16.24) to obtain the design space. Finally, through the verification of three batches within the design space, the mixing process temperature was adjusted according to the properties of the raw materials and exci-pients to achieve accurate control of the dynamic viscosity attribute. The relative deviation between the actual dynamic viscosity value and the target value was less than 3.0 %. The feedforward control of the mixing process of compound Danshen dripping pills was rea-lized in this study, which can contribute to improving quality consistency of the mixing process intermediates, simultaneously provide a reference for the research on the process quality control of other Chinese medicine dripping pills.

[Study of intelligent manufacturing quality digitalization of traditional Chinese medicine and practice on Compound Danshen Dripping Pills].

China healthcare industry has gradually developed the consumer-centric integrated service model. To satisfy consumers' increasing demands on pluralistic, personalized and transparent healthcare services, pharmaceutical manufacturing enterprises must provide high-quality, precise and flexible medicines. This can be achieved by accelerating implementation of intelligent manufacturing, which is the core competitiveness of pharmaceutical manufacturing enterprises. According to the authors' intelligent manufacturing projects in a traditional Chinese medicine(TCM) factory, study and industrial practice on intelligent manufacturing were presented in this paper. First, the quality digitalization-based intelligent manufacturing methodology of TCM was proposed in this paper. The methodology mainly included three digitalized technologies in process and quality design, manufacturing process control and product batch evaluation. Next, the architectural design of intelligent manufacturing systems in one TCM factory was introduced, and the functional modules and data transmission relationships covering seedling, cultivation, herbal slices, preparation, storage and quality management systems were described. Finally, these technologies were fully used, and an integrated quality digitalization system was successfully established in the production workshop of a TCM product Compound Danshen Dripping Pills. The actual operation and application of process analyzers, supervisory control and data acquisition(SCADA), manufacturing execution system(MES), data analysis system, and enterprise resource planning system(ERP) were introduced. This paper provides reference for technical path planning and systematic architecture of TCM intelligent manufacturing.

[Perilla resources of China and essential oil chemotypes of Perilla leaves].

This study, based on the findings for Perilla resources, aimed to describe the species, distribution, importance, features, utilization and status of quantitative Perilla resources in China. This not only helps people to know well about the existing resources and researching development, but also indicates the overall distribution, selection and rational use of Perilla resource in the future. According to the output types, Perilla resources are divided into two categories: wild resources and cultivated resources; and based on its common uses, the cultivated resources are further divided into medicine resources, seed-used resources and export resources. The distribution areas of wild resources include Henan, Sichuan, Anhui, Jiangxi, Guangxi, Hunan, Jiangsu and Zhejiang. The distribution areas of medicine resources are concentrated in Hebei, Anhui, Chongqing, Guangxi and Guangdong. Seed-used resources are mainly distributed in Gansu, Heilongjiang, Jilin, Chongqing and Yunnan. Export resource areas are mainly concentrated in coastal cities, such as Zhejiang, Jiangsu, Shandong and Zhejiang. For the further study, the essential oil of leaf samples from different areas were extracted by the steam distillation method and analyzed by GC-MS. The differences in essential oil chemotypes among different Perilla leaves were compared by analyzing their chemical constituents. The main 31 constituents of all samples included: perillaketone (0.93%-96.55%), perillaldehyde (0.10%-61.24%), perillene (52.15%), caryophyllene (3.22%-26.67%), and α-farnesene (2.10%-21.54%). These samples can be classified into following five chemotypes based on the synthesis pathways: PK-type, PA-type, PL-type, PP-type and EK-type. The chemotypes of wild resources included PK-type and PA-type, with PK-type as the majority. All of the five chemotypes are included in cultivated resources, with PA-type as the majority. Seed-used resources are all PK-type, and export resources are all PA-type. The P. frutescens var. frutescens include five chemotypes, with PK-type as the majority. The PK-type leaves of P. frutescens var. acuta are green, while the PA-type leaves are reddish purple. The P. fruteseens var. crispa was mainly PA type with reddish purple leaves. The differences of the main chemotypes provide a scientific basis for distinguishing between Zisu and Baisu in previous literatures. Based on the lung toxicity of PK and the traditional use of Perilla, the testing standard of essential oil and Perilla herb shall be built, and PA type is recommended to be used in traditional Chinese medicine.