版权说明 操作指南
首页 > 成果 > 成果详情

Geographical Origin Traceability of Atractylodes macrocephala Koidz. Using Mass Spectrometry Data Fusion and Ensemble Learning

认领
导出
Link by DOI
反馈
分享
QQ微信 微博
成果类型:
期刊论文
作者:
Tang, Ying;Zhao, Han-Qing;Zhang, Xin-Yi;Wang, Xiao-Zhi;Du, Ci;...
通讯作者:
Chen, Y;Chen, Yao;Wang, T
作者机构:
[Chen, Sha; Du, Ci; Chen, Yao; Tang, Ying] Hunan Univ Technol, Coll Life Sci & Chem, Hunan Key Lab Biomed Mat & Devices, Zhuzhou 412007, Peoples R China.
[Wang, Tong; Zhang, Xin-Yi; Chen, Yao; Wang, Xiao-Zhi; Tang, Ying] Hunan Univ, Coll Chem & Chem Engn, State Key Lab Chemo Biosensing & Chemometr, Changsha 410082, Peoples R China.
[Zhao, Han-Qing] Cent South Univ Forestry & Technol, Sch Sci, Inst Appl Chem, Changsha, Peoples R China.
[Chen, Sha] Zhuzhou City Joint Lab Environm Microbiol & Plant, Zhuzhou, Peoples R China.
通讯机构:
[Wang, T ; Chen, Y ; Chen, Y] H
Hunan Univ Technol, Coll Life Sci & Chem, Hunan Key Lab Biomed Mat & Devices, Zhuzhou 412007, Peoples R China.
Hunan Univ, Coll Chem & Chem Engn, State Key Lab Chemo Biosensing & Chemometr, Changsha 410082, Peoples R China.
语种:
英文
关键词:
Atractylodes macrocephala Koidz.;ensemble learning;gas chromatography - mass spectrometry (GC-MS);high-performance liquid chromatography - mass spectrometry (LC-MS);inductively coupled plasma - mass spectrometry (ICP-MS)
期刊:
Analytical Letters
ISSN:
0003-2719
年:
2024
页码:
06
基金类别:
Scientific research project of Hunan Provincial Department of Education [22B0579, 22C0321]; National Natural Science Foundation of China [22204049]; Natural Science Foundation of Hunan Province [2022JJ40042]
机构署名:
本校为其他机构
院系归属:
理学院
摘要:
The use of data fusion based with multiple analytical techniques was investigated to provide the accurate geographical origin identification of Atractylodes macrocephala Koidz. (AMK). Inductively coupled plasma - mass spectrometry (ICP-MS), gas chromatography - mass spectrometry (GC-MS) and liquid chromatography - mass spectrometry (LC-MS) were used to characterize Hubei, Zhejiang, and Hunan production regions. After the implementation of data fusion, the ensemble learning method multi-forest joint network (MFJN) and classic machine learning methods were used to identify the AMK production reg...

反馈

验证码:
看不清楚,换一个
确定
取消

成果认领

标题:
用户 作者 通讯作者
请选择
请选择
确定
取消

提示

该栏目需要登录且有访问权限才可以访问

如果您有访问权限,请直接 登录访问

如果您没有访问权限,请联系管理员申请开通

管理员联系邮箱:yun@hnwdkj.com