中国中药杂志2025，Vol.50Issue(5)：1172-1178,7.DOI:10.19540/j.cnki.cjcmm.20241214.103
基于UHPLC-QTOF-MSE和Adaboost的鹿茸数字化鉴定
Digital identification of Cervi Cornu Pantotrichum based on HPLC-QTOF-MSE and Adaboost
摘要
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-MSE)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-MSE.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,F1 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,F1 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-MSE 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.关键词
鹿茸/基原鉴定/真伪鉴定/机器学习/数字化鉴定/多肽Key words
Cervi Cornu Pantotrichum/origin identification/verification of authenticity/machine learning/digital identification/polypeptide引用本文复制引用
郭晓晗,王献瑞,张宇,李明华,荆文光,程显隆,魏锋..基于UHPLC-QTOF-MSE和Adaboost的鹿茸数字化鉴定[J].中国中药杂志,2025,50(5):1172-1178,7.基金项目
国家重点研发计划"中医药现代化"重大专项(2023YFC3504105) （2023YFC3504105）
