文献类型：期刊文献

英文题名：Determination of the geographical origin of Tetrastigma hemsleyanum Diels & Gilg using an electronic nose technique with multiple algorithms

作者：Wu, Zhizhuang[1,2] Ye, Xiaodan[1,2] Bian, Fangyuan[1,2] Yu, Ganglei[3] Gao, Guibing[1,2] Ou, Jiande[4] Wang, Yukui[1] Li, Yueqiao[5] Du, Xuhua[1,2]

第一作者：Wu, Zhizhuang

通信作者：Bian, FY[1];Du, XH[1];Bian, FY[2];Du, XH[2]

机构：[1]China Natl Bamboo Res Ctr, Key Lab Bamboo Forest Ecol & Resource Utilizat Nat, Hangzhou 310012, Zhengjiang, Peoples R China;[2]Natl Long term Observat & Res Stn Forest Ecosyst H, Hangzhou 310012, Zhengjiang, Peoples R China;[3]Dongyang Forestry Bur, Dongyang 322103, Zhengjiang, Peoples R China;[4]Mingxi Forestry Bur, Mingxi 365200, Fujian, Peoples R China;[5]Chinese Acad Forestry, Expt Ctr Subtrop Forestry, Fenyi 336600, Jiangxi, Peoples R China

年份：2022

卷号：8

期号：10

外文期刊名：HELIYON

收录：;Scopus(收录号：2-s2.0-85139025094);WOS:【SCI-EXPANDED(收录号:WOS:000870514600001)】；

基金：Xuhua DU was supported by People's Government of ZhejiangProvince-Chinese Academy of Forestry cooperative project [2019SY01],Fundamental Research Funds for the Central Non-Profit Research Institution of CAF [CAFYBB2019MB005], National Natural Science Foundation of China [31600448].Yukui Wang was supported by key research and development pro-gram of Zhejiang Province [2020C02008].

语种：英文

外文关键词：Electronic nose; Volatiles; Geographical origin; T; hemsleyanum diels & gilg

摘要：Tetrastigma hemsleyanum Diels & Gilg, an herbal medicinal plant, is planted widely in bamboo forests in southern China to promote economic benefits. Volatile compounds (VOCs) of T. hemsleyanum from different geographical regions are difficult to identify in field forests. In this study, VOCs from leaf samples of different geographical origins were analyzed using an electronic nose with 10 different sensors. Principal component analysis (PCA), partial least-squares regression (PLS), hierarchical cluster analysis (HCA), and radial basis function (RBF) neural networks were used to determine differences among different local samples. The results demonstrated that PCA achieved an accurate discrimination percentage of 91.31% for different samples and HCA separated the samples into different groups. The RBF neural network was successfully applied to predict samples with no specified lo-calities. T. hemsleyanum samples from geographically close regions tended to group together, whereas those from distant geographical regions showed obvious differences. These results indicate that an electronic nose is an effective tool for detecting VOCs and discriminating the geographical origins of T. hemsleyanum. This study provides insights for further studies on the fast detection of VOCs from plants and effect of forests and plant herbal medicines on improving air quality.