基于贝叶斯优化卷积神经网络的金线莲伪品鉴别

Identification of fake Anoectochilus roxburghii based on Bayesian optimized convolutional neural network

在金线莲粉末中掺入同科台湾银线莲或斑叶兰、血叶兰粉末的现象严重影响了金线莲药材的药效和市场秩序,寻找一种快速有效的方法来鉴别掺假金线莲是亟待解决的问题。针对传统鉴别方法特征提取自适应性的不足以及卷积神经网模型结构复杂、超参数难以调节的难点,本文提出基于一维卷积神经网络的掺假金线莲鉴别模型,并利用贝叶斯优化算法优化卷积神经网络超参数,实现了超参数自动优化调节。实验结果表明,经过超参数寻优后的卷积神经网络相比传统机器学习模型更有竞争力,所提出的基于贝叶斯优化的一维卷积神经网络模型可以快速有效地鉴别金线莲及其伪品。

The phenomenon of mixing the same family of Anoectochilus roxburghii(A.roxburghii)powder with the genus Anoectochilus formosanus or Goodyera schlechtendaliana,Ludisia discolor has seriously affected the drug efficacy and market order of A.roxburghii.Therefore,finding a fast and effective method to identify adulterated A.roxburghii is an urgent problem to be solved.In view of the shortcomings of the adaptive feature extraction of traditional identification methods and the difficulties of complex structure and difficult adjustment of super parameters of convolution neural network model,an adulterated A.roxburghii identification model based on 1D convolutional neural network(1D-CNN)was proposed in this paper,in which the Bayesian optimization algorithm was proposed to optimize its hyperparameters to realize automatic optimization and adjustment of the hyperparameters.Experimental results showed that the 1D-CNN model with hyperparameter optimization was more competitive than other traditional machine learning models.The proposed 1D-CNN model based on Bayesian optimization can quickly and effectively identify A.roxburghii and its counterfeits.