Wen Zheng

2 months ago | nature.com | Wenqi Zhao |Wen Zheng |Zimin Wang |Xiaoli Pan |Gaoting Yang |Zongyi Zhao | +2 more

Alismatis Rhizoma (AR) is widely utilized as a natural medicine across many Asian countries. However, in China, due to its complex origins, AR quality varies, which can affect clinical efficacy. Therefore, there is a need for a method that is both fast and objective to determine the source of AR. In this study, a total of 400 samples of two species and four geographic origins from AR were imaged and processed. From these images, 17 features were extracted, including three shape (S), two color (C), and 12 texture features (T), resulting in a total of 6800 data points. Four commonly used classification models Random Forest (RF), Extreme Learning Machine (ELM), Back Propagation (BP) neural network, and Support Vector Machines (SVM) were tested to find the optimal combination of AR fusion features and classification models. The S + T-RF combinations achieved the best results, with 99.17% accuracy in two species identification and 96.67% accuracy in four geographic origin identification on test sets. These results suggest that image processing combined with the RF model can quickly and effectively identify the complex origins of AR and can provide a reference for the origins identification of other natural medicines.

May 8, 2024 | chemistry-europe.onlinelibrary.wiley.com | Ning Song |Wen Zheng |Jie Zheng

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Jul 19, 2023 | mdpi.com | Wen Liu |Min Zhu |Wen Zheng |Xin Zeng

Font Type: Arial Georgia Verdana Line Spacing:    Column Width:    Open AccessArticle by Wen Liu 1,†, Min Zhu 1,†, Meng Gong 2,3, Wen Zheng 3, Xin Zeng 1, Qing Zheng 1, Xiaoyu Li 2, Fudong Fu 2, Yingyi Chen 4, Jingqiu Cheng 1,2, Zhiyong Rao 4, Yanrong Lu 1,* and Younan Chen 1,2,* 1 Department of Clinical Nutrition and Key Laboratory of Transplant Engineering and Immunology, NHFPC, Regenerative Medicine Research Center, West China Hospital, Sichuan University, Chengdu 610041, China 2...