Wu Zhizhuang, Ye Xiaodan, Bian Fangyuan, Yu Ganglei, Gao Guibing, Ou Jiande, Wang Yukui, Li Yueqiao, Du Xuhua
China National Bamboo Research Center, Key Laboratory of Bamboo Forest Ecology and Resource Utilization of National Forestry and Grassland Administration, Hangzhou, 310012, Zhengjiang, China.
National Long-term Observation and Research Station for Forest Ecosystem in Hangzhou-Jiaxing-Huzhou Plain; Hangzhou 310012, Zhengjiang, China.
Heliyon. 2022 Sep 28;8(10):e10801. doi: 10.1016/j.heliyon.2022.e10801. eCollection 2022 Oct.
Diels & Gilg, an herbal medicinal plant, is planted widely in bamboo forests in southern China to promote economic benefits. Volatile compounds (VOCs) of 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. 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 . 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.
地耳草是一种药用植物，在中国南方的竹林中广泛种植以提高经济效益。来自不同地理区域的地耳草挥发性化合物（VOCs）在野外森林中难以识别。在本研究中，使用具有10种不同传感器的电子鼻分析了不同地理来源的地耳草叶片样本中的VOCs。采用主成分分析（PCA）、偏最小二乘回归（PLS）、层次聚类分析（HCA）和径向基函数（RBF）神经网络来确定不同本地样本之间的差异。结果表明，PCA对不同样本的准确判别率达到91.31%，HCA将样本分为不同的组。RBF神经网络成功应用于预测未指定产地的样本。地理上相近地区的样本倾向于聚类在一起，而来自遥远地理区域的样本则表现出明显差异。这些结果表明，电子鼻是检测VOCs和鉴别地耳草地理来源的有效工具。本研究为进一步研究植物VOCs的快速检测以及森林和植物草药对改善空气质量的影响提供了见解。
