摘要
目的:对来自全国13个省市共171个杜仲茎和叶样本进行光谱分析,结合深度学习建立不同产地判别模型,为其资源合理开发利用提供依据。方法:以13个产地的杜仲茎和叶为实验材料,分别检测其近红外光谱,结合二维光谱算法和残差卷积神经网络建立模式识别模型。结果:基于同步二维相关光谱的模型在卷积层数为26层及以上时均取得了100%的分类正确率,而基于异步二维相关光谱的模型预测集正确率低于30%。结论:表明该模型能够应用于不同产地杜仲药材的鉴别。
Objective:To conduct spectral analysis on the 171 stems and leaves of Duzhong from 13 provinces in our country,and establish discrimination models for different habitats combined with deep learning,so as to provide a basis for the rational development and utilization of its resources.Method:The stems and leaves of Duzhong from 13 places of production were used as experimental materials,and their near-infrared spectra were detected respectively.A pattern recognition model was established by combining two-dimensional spectrum algorithm and residual convolutional neural network.Result:The model based on synchronous two-dimensional correlation spectrum achieved 100%classification accuracy when the number of convolution layers was above 26,while the model based on asynchronous two-dimensional correlation spectrum achieved less than 30%classification accuracy.Conclusion:It shows that the model can be applied to the identification of Duzhong from different habitats.
作者
丁于刚
李鹂
左智天
李佑稷
DING Yu-gang;LI Li;ZUO Zhi-tian;Li You-ji(College of Traditional Chinese Medicine,Yunnan University of Traditional Chinese Medicine,Kunming 650500,Yunnan;Institute of Medicinal Plants,Yunnan Academy of Agricultural Sciences,Kunming 650200,Yunnan;College of Biological Resources and Environmental Sciences,Jishou University,Jishou 416000,Hunan)
出处
《中药与临床》
2023年第1期16-19,28,共5页
Pharmacy and Clinics of Chinese Materia Medica
基金
《基于多源信息融合技术的杜仲质量评价体系研究》,国家自然科学基金项目(No.31960323)。
关键词
杜仲
二维相关光谱
深度学习
判别模型
Duzhong
two-dimensional correlation spectroscopy
deep learning
discriminant model
作者简介
丁于刚(1995-),男,在读硕士研究生,研究方向为中药学,Email:dyg2020@126.com;通讯作者:李鹂(1973-),女,博士,研究方向为植物生态与分子生物学,Email:lilyjsu@126.com。
