Authenticity discrimination of Pulsatillae Radix based on dry-process REIMS fingerprinting combined with machine learning.
10.19540/j.cnki.cjcmm.20221017.103
- Author:
Yan SHI
1
;
Ling-Wen YAO
1
;
Feng WEI
1
;
Shuang-Cheng MA
1
Author Information
1. National Institutes for Food and Drug Control Beijing 102629, China.
- Publication Type:Journal Article
- Keywords:
Pulsatillae Radix;
REIMS;
authenticity discrimination;
data dimensionality reduction;
machine learning
- MeSH:
Medicine, Chinese Traditional;
Algorithms;
Anemone;
Machine Learning
- From:
China Journal of Chinese Materia Medica
2023;48(4):921-929
- CountryChina
- Language:Chinese
-
Abstract:
In this study, rapid evaporative ionization mass spectrometry(REIMS) fingerprints of 388 samples of roots of Pulsatilla chinensis(PC) and its common counterfeits, roots of P. cernua and roots of Anemone tomentosa were analyzed based on REIMS combined with machine learning. The samples were determined by REIMS through dry burning, and the REIMS data underwent cluster analysis, similarity analysis(SA), and principal component analysis(PCA). After dimensionality reduction by PCA, the data were analyzed by similarity analysis and self-organizating map(SOM), followed by modeling. The results indicated that the REIMS fingerprints of the samples showed the characteristics of variety differences and the SOM model could accurately distinguish PC, P. cernua, and A. tomentosa. REIMS combined with machine learning algorithm has a broad application prospect in the field of traditional Chinese medicine.
