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

Zhizhuang Wu; Xiaodan Ye; Fangyuan Bian; Ganglei Yu; Guibing Gao; Jiande Ou; Yukui Wang; Yueqiao Li; Xuhua Du

doi:10.1016/j.heliyon.2022.e10801

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

Heliyon. 2022 Sep 28;8(10):e10801. doi: 10.1016/j.heliyon.2022.e10801. eCollection 2022 Oct.

Authors

Zhizhuang Wu^{1

2}, Xiaodan Ye^{1

2}, Fangyuan Bian^{1

2}, Ganglei Yu³, Guibing Gao^{1

2}, Jiande Ou⁴, Yukui Wang¹, Yueqiao Li⁵, Xuhua Du^{1

2}

Affiliations

¹ China National Bamboo Research Center, Key Laboratory of Bamboo Forest Ecology and Resource Utilization of National Forestry and Grassland Administration, Hangzhou, 310012, Zhengjiang, China.
² National Long-term Observation and Research Station for Forest Ecosystem in Hangzhou-Jiaxing-Huzhou Plain; Hangzhou 310012, Zhengjiang, China.
³ Dongyang Forestry Bureau, Dongyang, 322103, Zhengjiang, China.
⁴ Mingxi Forestry Bureau, Mingxi, 365200, Fujian, China.
⁵ Experimental Center for Subtropical Forestry, Chinese Academic of Forestry, Fenyi, 336600, Jiangxi, China.

Abstract

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 localities. 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.

Keywords: Electronic nose; Geographical origin; T.hemsleyanum diels & gilg; Volatiles.