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]; He, Shiyu[1]; Hu, Yun[2]; Wang, Zihan[1]; Zhang, Jin[1]; Wang, Yali[3]

第一作者：Li, Boyan

机构：[1] School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang, 561113, China; [2] Technology Center of China Tobacco Guizhou Industrial Co., Ltd., Guiyang, 550009, China; [3] School of Pharmacy, Gansu University of Chinese Medicine, Lanzhou, 730000, China

第一机构：School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang, 561113, China

通信机构：[2]Technology Center of China Tobacco Guizhou Industrial Co., Ltd., Guiyang, 550009, China;[1]School of Public Health/Key Laboratory of Endemic and Ethnic Diseases, Ministry of Education & Key Laboratory of Medical Molecular Biology of Guizhou Province, Guizhou Medical University, Guiyang, 561113, China

年份：2025

卷号：217

外文期刊名：LWT

收录：EI(收录号：20250517780338);Scopus(收录号：2-s2.0-85216229593)

语种：英文

外文关键词：Flavor compounds - Ion chromatography - Ion mobility spectrometers - Volatile organic compounds

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. ? 2025 The Authors