文献类型：期刊文献

英文题名：Compositional profiling and classification of radix Angelicae sinensis by effective GC-IMS and chemometric tool

作者：Li, Boyan[1,2];He, Shiyu[1,2];Hu, Yun[3];Wang, Zihan[1,2];Zhang, Jin[1,2];Wang, Yali[4]

第一作者：Li, Boyan

通信作者：Zhang, J[1];Zhang, J[2];Hu, Y[3]

机构：[1]Guizhou Med Univ, Sch Publ Hlth, Key Lab Endem & Ethn Dis, Minist Educ, Guiyang 561113, Peoples R China;[2]Guizhou Med Univ, Key Lab Med Mol Biol Guizhou Prov, Guiyang 561113, Peoples R China;[3]China Tobacco Guizhou Ind Co Ltd, Technol Ctr, Guiyang 550009, Peoples R China;[4]Gansu Univ Chinese Med, Sch Pharm, Lanzhou 730000, Peoples R China

第一机构：Guizhou Med Univ, Sch Publ Hlth, Key Lab Endem & Ethn Dis, Minist Educ, Guiyang 561113, Peoples R China

通信机构：[1]corresponding author), Guizhou Med Univ, Sch Publ Hlth, Key Lab Endem & Ethn Dis, Minist Educ, Guiyang 561113, Peoples R China;[2]corresponding author), Guizhou Med Univ, Key Lab Med Mol Biol Guizhou Prov, Guiyang 561113, Peoples R China;[3]corresponding author), China Tobacco Guizhou Ind Co Ltd, Technol Ctr, Guiyang 550009, Peoples R China.

年份：2025

卷号：217

外文期刊名：LWT-FOOD SCIENCE AND TECHNOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001425402400001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant nos. 82360700 and 82060712) , the Guizhou Provincial Science and Technology Projects (Grant nos. [2018] 1130 and ZK [2021] 045) , and the Key R & D Program of Science and Technology Department of Gansu Province, China (Grant no. 20YF8NA067) .

语种：英文

外文关键词：Radix Angelicae sinensis; GC-IMS; Volatile organic compound; Data augmentation; Principal discriminant variate

摘要：A new analytical architecture was proposed in this study to profile the volatile fraction of edible plant materials of radix Angelicae sinensis (RAS) using non-targeted gas chromatography-ion mobility spectrometry (GC-IMS) technique. High dimensional GC-IMS data were effectively tackled in two encompassing stages and three exploratory spaces by a pool of chemometric methods. 33 flavor compounds were remarkably identified in 287 RAS batches sourced from different geographic origins. The data augmentation through competitive adaptive reweighted sampling method was beneficial to discerning significant features in feature space. The samples were classified in sample space up to a high accuracy of 99.37% by principal discriminant variate regime. The use of augmented features to map sample patterns exhibited a significant edge over peak features automatically detected in LAV software. The work provided a judicious strategy to address the most commonly encountered problems in the GC-IMS analytical context of volatile organic compounds of natural materials.