Xiaoli Pan

1 month 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.

Dec 4, 2024 | cell.com | Yajing Qiu |Yapeng Su |Ermei Xie |Hongcheng Cheng |Jing Du |Yue Xu | +6 more

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Aug 8, 2024 | onlinelibrary.wiley.com | Yang Chen |Jia Zhao |Xiaoli Pan |Lin Li

Conflict of Interests The authors declare no conflict of interest. Supporting Information As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re-organized for online delivery, but are not copy-edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.