Classification optimization of traditional Chinese medicine materials based on additive physical properties of powder / 中国中药杂志

According to the method of predicting the physical properties of oily powder based on the additive physical properties of Chinese medicinal powder, Dioscoreae Rhizoma and calcined Ostreae Concha with high sieve rate and good fluidity were mixed and crushed with Persicae Semen, Platycladi Semen, Raphani Semen, Ziziphi Spinosae Semen, and other typical oily materials with high fatty oil content in proportion to obtain 23 mixed powders. Fifteen physical properties such as bulk density, water absorption, and maximum torque force were measured, and the physical properties of typical oily powders were predicted. When the mixing and grinding ratio was in the range of 5∶1-1∶1, the r value in the correlation equation between the weighted average score of the mixed powder and the powder proportion ranged from 0.801 to 0.986, and the linearity was good, indicating that the method of predicting the physical properties of oily powder based on the additive physical properties of traditional Chinese medicine(TCM)powder was feasible. The results of cluster analysis showed that the classification boundaries of the five kinds of TCM materials were clear, and the similarity of the physical fingerprints of powdery and oily materials decreased from 80.6% to 37.2%, which solved the problem of fuzzy classification boundaries of powdery and oily materials due to the lack of representativeness of oily material model drugs. The classification of TCM materials was optimized, laying a foundation for optimizing the prediction model of the prescription of personalized water-paste pills.

Material classification of decoction pieces based on physical properties of powder / 中国中药杂志

Chinese medicinals feature different medicinal parts and enriched components, which makes their powders show obvious microscopic identification characteristics and specific physical properties. On this basis, the commonly used Chinese medicinals can be divided into several categories, such as powdery, fibrous, sugar, oil, and brittle materials, which is of great importance to the research and development of personalized Chinese medicinal preparation technology. However, the existing classification methods are highly subjective and thus difficult to meet the requirements for the development of personalized Chinese medicinal preparations with high quality. In this study, 55 representative Chinese medicinals, such as Dioscoreae Rhizoma and Leonuri Herba, were selected, and the physical properties of their powders were systematically characterized by comprehensive powder tester, torque rheometer, texture analyzer, etc., based on which a data set encompassing physical properties of these powders was built. The typical physical fingerprints of powders from the above 5 categories were established by multivariate statistical analysis. Then, the Chinese medicinals were classified according to the Euclidean distance between each of them and the typical value in the PCA score plot. For those with multiple material properties, whose classification boundary was fuzzy, the proportions of different types of materials were calculated with the combination of Euclidean distance, powder properties, microscopic identification characteristics, and chemical composition, so as to achieve the multivariate quantitative classification of Chinese medicinals. This lays the foundation for the further creation of intelligent personalized Chinese medicinal preparation technology.