Rapid determination of geographical authenticity of Gastrodia elata f. glauca using Fourier transform infrared spectroscopy and deep learning
文献类型： 外文期刊
作者： Deng, Guangmei 1 ; Li, Jieqing 1 ; Liu, Honggao 3 ; Wang, Yuanzhong 2 ;
作者机构： 1.Yunnan Agr Univ, Coll Agron & Biotechnol, Kunming 650201, Peoples R China
2.Yunnan Acad Agr Sci, Med Plants Res Inst, Kunming 650200, Peoples R China
3.Zhaotong Univ, Yunnan Key Lab Gastrodia & Fungi Symbiot Biol, Zhaotong 657000, Yunnan, Peoples R China
关键词： Gastrodia elata f. glauca; Fourier transform infrared spectroscopy; Deep learning; Data driven version of soft independent; modeling of class analogy
期刊名称：FOOD CONTROL （ 影响因子：5.6； 五年影响因子：5.4 ）
ISSN： 0956-7135
年卷期： 2025 年 167 卷
页码：
收录情况： SCI
摘要： For quality evaluation of Gastrodia elata f. glauca (GEFG), it's crucial to develop an analytical method to determine the geographical origin. Herein, 371 GEFGs are collected from five provinces, focusing on analysis of dry matter content (DMC), origin identification, geographical indication (GI) production area discrimination by using a combination of Fourier transform infrared (FTIR) spectroscopy and deep learning, data driven version of soft independent modeling of class analogy (DD-SIMCA). A significant difference in DMC of GEFG between Yunnan and other origins, which may be related to precipitation, altitude, temperature, and soil. The residual neural network (ResNet) model based on synchronous two-dimensional correlation spectroscopy (2DCOS) images has stable performances, its accuracy is 100%. The DD-SIMCA model can differentiate GI production areas of GEFG, while for non-GI areas, the model specificity is 71.38%. This study provides a promising approach for GEFG geographical traceability and GI production area differentiation.
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