Characterization of Gastrodiae Rhizoma from different geographical origins by HS-GC-IMS and authenticity...

Given the growing demand for Gastrodiae Rhizoma (GR), it is important to establish a strategy for origin authentication and adulteration screening to ensure the high-quality products in the market. Currently, efficient and dependable methods for identifying counterfeit GR are lacking. In this study, a total of 57 volatile components (VOCs) in GR samples from different geographical origins were identified using gas chromatography ion mobility spectrometry (GC-IMS), and 10 featured VOCs were screened, including 2-methyl-1-butanol, limonene, and 1-propanol. Concurrently, GR samples from Yunnan showed relatively high levels of these featured VOCs. Subsequently, a strategy was proposed to combine 2D GC-IMS chromatograms with deep learning algorithms such as convolutional neural networks (CNN) and residual networks (ResNET). Based on this strategy, the accuracy of identifying the geographical origins of GR samples reached over 99.54 %, which was higher than that of conventional machine learning models (around 90 % in the testing sets). This study offers a reference for research on VOCs of GR from different geographical origins, and provides a rapid, simple, and accurate method for discriminating the geographical origins of GR samples.