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基于仿生技术和反向传播神经网络的黄芪产地判别模型构建研究

Identification of origin place for Astragali Radix based on biomimetics
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摘要 目的 基于仿生技术和反向传播神经网络(BPNN)构建黄芪的产地鉴别模型。方法 采用色度计、电子鼻和电子舌共测得21项指标,通过RFI进行特征筛选后得到14项指标,并将黄芪产地鉴定问题建模为多分类问题。通过对随机森林(RF)、支持向量机(SVM)和BPNN这三种机器学习模型的比较,我们建立了一个基于BPNN的黄芪产地分类决策系统。结果 BPNN仅用了11个特征变量就能够较好地预测黄芪产地。多分类模型构建后,引入SHAP值对构建的产地鉴别模型进行解释。结论 SHAP特征重要性的排序揭示了变量在实际构建出的模型的重要程度。可解释预测模型在增加产地预测模型的透明度的同时,又能保持原模型的判别正确率。该研究为产地鉴别模型的构建提供了一定的启示,也为客观产地鉴别提供了参考。 Objective To construct the origin identification model of the roots of Astragalus membranaceus var.mongholicus based on biomimetics and back propagation neural network (BPNN).Methods Totally 21 indicators were measured by colorimeter,electronic nose (E-nose),and electronic tongue (E-tongue).Totally 14 indicators were obtained by random forest importance(RFI) after feature screening,and AR origin identification was modeled as a multi-classification problem.By comparing the three machine learning models (RF,SVM,and BPNN),a decision system was built for classification based on BPNN.Results BPNN well predicted the geographical origins of AR with only 11 feature variables.After constructing the multi-classification model,SHapley Additive exPlanation (SHAP) values were introduced to interpret the constructed origin identification model.Conclusion The importance ranking of the SHAP features shows how important the variables are in the actual model.Interpretable prediction models increase the transparency of the origin prediction model while maintaining the discrimination correctness of the original model.This study provides some reference for the construction of origin identification models.
作者 陈万金 李虹 张沛沛 邵炜娴 王越 范昕煜 赵婷 刘凤波 魏胜利 于芳 张媛 CHEN Wan-jin;LI Hong;ZHANG Pei-pei;SHAO Wei-xian;WANG Yue;FAN Xin-yu;ZHAO Ting;LIU Feng-bo;WEI Sheng-li;YU Fang;ZHANG Yuan(School of Chinese Materia Medica,Beijing University of Chinese Medicine,Beijing 102488;School of Pharmacy,China Pharmaceutical University,Nanjing 211198;Engineering Research Center of Good Agricultural Practice for Chinese Crude Drugs,Ministry of Education,Beijing 102488)
出处 《中南药学》 CAS 2024年第12期3221-3228,共8页 Central South Pharmacy
基金 国家重点研发计划项目-中医药现代化专项(No.2022YFC3501505)。
关键词 黄芪 产地鉴别 仿生技术 反向传播神经网络 SHAP 可解释机器学习 Astragali Radix geographical traceability biomimetics back propagation neural network SHapley Additive exPlanation interpretable machine learning
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