Classification optimization of traditional Chinese medicine materials based on additive physical properties of powder.
10.19540/j.cnki.cjcmm.20220722.301
- Author:
Yun-Qi LI
1
;
Wen-Xiu TIAN
1
;
Guang YANG
1
;
Wen-Jie LI
1
;
Li-Jie ZHAO
2
;
Yan-Long HONG
1
Author Information
1. Shanghai Innovation Center of TCM Health Service, Shanghai University of Traditional Chinese Medicine Shanghai 201203, China.
2. Innovative Research Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine Shanghai 201203, China.
- Publication Type:Journal Article
- Keywords:
additive property;
classification of traditional Chinese medicine materials;
personalized traditional Chinese medicine preparation;
physical fingerprint;
physical properties of powder
- MeSH:
Medicine, Chinese Traditional;
Drugs, Chinese Herbal;
Powders;
Prescriptions
- From:
China Journal of Chinese Materia Medica
2023;48(7):1866-1875
- CountryChina
- Language:Chinese
-
Abstract:
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.
