Digital identification of Cervi Cornu Pantotrichum based on HPLC-QTOF-MS~E and Adaboost.
10.19540/j.cnki.cjcmm.20241214.103
- Author:
Xiao-Han GUO
1
;
Xian-Rui WANG
1
;
Yu ZHANG
2
;
Ming-Hua LI
1
;
Wen-Guang JING
1
;
Xian-Long CHENG
1
;
Feng WEI
1
Author Information
1. State Key Laboratory of Drug Regulatory Science, National Institutes for Food and Drug Control Beijing 102629, China Institute for Control of Chinese Traditional Medicine and Ethnic Medicine, National Institutes for Food and Drug Control Beijing 102629, China.
2. State Key Laboratory of Drug Regulatory Science, National Institutes for Food and Drug Control Beijing 102629, China Faculty of Functional Food and Wine, Shenyang Pharmaceutical University Shenyang 110016, China.
- Publication Type:Journal Article
- Keywords:
Cervi Cornu Pantotrichum;
digital identification;
machine learning;
origin identification;
polypeptide;
verification of authenticity
- MeSH:
Animals;
Algorithms;
Antlers/chemistry*;
Boosting Machine Learning Algorithms;
Chromatography, High Pressure Liquid/methods*;
Deer;
Drugs, Chinese Herbal/chemistry*;
Mass Spectrometry/methods*;
Quality Control;
Reindeer;
Tandem Mass Spectrometry/methods*;
Tissue Extracts/analysis*
- From:
China Journal of Chinese Materia Medica
2025;50(5):1172-1178
- CountryChina
- Language:Chinese
-
Abstract:
Cervi Cornu Pantotrichum is a precious animal-derived Chinese medicinal material, while there are often adulterants derived from animals not specified in the Chinese Pharmacopeia in the market, which disturbs the safety of medication. This study was conducted with the aim of strengthening the quality control of Cervi Cornu Pantotrichum and standardizing the medication. To achieve digital identification of Cervi Cornu Pantotrichum from different sources, a digital identification model was constructed based on ultra-high performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry(UHPLC-QTOF-MS~E) combined with an adaptive boosting algorithm(Adaboost). The young furred antlers of sika deer, red deer, elk, and reindeer were processed and then subjected to polypeptide analysis by UHPLC-QTOF-MS~E. Then, the mass spectral data reflecting the polypeptide information were obtained by digital quantification. Next, the key data were obtained by feature screening based on Gini index, and the digital identification model was constructed by Adaboost. The model was evaluated based on the recall rate, F_1 composite score, and accuracy. Finally, the results of identification based on the constructed digital identification model were validated. The results showed that when the Gini index was used to screen the data of top 100 characteristic polypeptides, the digital identification model based on Adaboost had the best performance, with the recall rate, F_1 composite score, and accuracy not less than 0.953. The validation analysis showed that the accuracy of the identification of the 10 batches of samples was as high as 100.0%. Therefore, based on UHPLC-QTOF-MS~E and Adaboost algorithm, the digital identification of Cervi Cornu Pantotrichum can be realized efficiently and accurately, which can provide reference for the quality control and original animal identification of Cervi Cornu Pantotrichum.
